New Technologies for Power System Operation and Analysis considers the very latest developments in renewable energy inte

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*Table of contents : New Technologies for Power System Operation and AnalysisCopyrightContentsList of contributorsone Introduction 1.1 Overview of power systems 1.2 The development history of power systems in the United States 1.3 Distributed energy resource units 1.4 Steady-state conditions 1.5 AI and machine learning 1.6 Network dynamic operation 1.7 Multisector coupling 1.8 Structure of this book Referencestwo Decoupled linear AC power flow models with accurate estimation of voltage magnitude in transmission and distribution sy... Nomenclature 2.1 Introduction 2.2 Linear power flow models for the meshed transmission systems 2.2.1 Decoupling of voltage magnitude and phase angle 2.2.2 Matrix formulation of the decoupled linearized power flow model 2.2.3 Transformers and phase shifters 2.2.3.1 Contributions to the admittance matrix 2.2.3.2 Influence on the branch MW flow 2.2.4 Derivation and justification of the fast decoupled linearized power flow 2.2.4.1 An illustrative example 2.2.4.2 Theoretical derivation 2.2.4.3 A numerical example 2.3 Linear three-phase power flow models of the unbalanced distribution systems 2.4 Case study 2.4.1 Meshed transmission systems 2.4.2 Balanced distribution systems 2.4.3 Unbalanced distribution systems 2.5 Conclusion Referencesthree Renewable energy integration and system operation challenge: control and optimization of millions of devices 3.1 Introduction 3.2 Distribution system model with high penetration of renewables 3.2.1 Distribution network model 3.2.2 An explicit branch model of distribution network 3.2.3 Dynamic distributed generation model 3.3 Autonomous distributed voltage control 3.3.1 Distributed subgradient algorithm 3.3.2 Distributed subgradient voltage control 3.3.3 Reactive power control and power factor control 3.4 Hierarchical multiagent control of large-scale distribution system 3.4.1 Virtual leader design 3.4.2 Case study 3.5 Islanded microgrid with high penetration of distributed generations 3.6 Grid-edge situational awareness: enhanced observability by voltage inference 3.6.1 Voltage inference method 3.6.2 Network sensitivity 3.6.3 Implementation 3.7 Control-enabled dynamic hosting allowance: P and Q control capacity and impact analysis 3.7.1 Traditional hosting analysis 3.7.2 Dynamic hosting allowance analysis 3.8 Cosimulation of integrated transmission and distribution systems 3.8.1 The framework of cosimulation 3.8.1.1 Integrated T&D system simulation 3.8.1.2 Parallel cosimulation of integrated T&D systems 3.8.2 Simulation results 3.8.2.1 Power flow of integrated T&D systems 3.8.2.2 Integrated T&D systems with PVs and control 3.9 Conclusion Referencesfour Advances of wholesale and retail electricity market development in the context of distributed energy resources 4.1 Introduction 4.2 Modern wholesale electricity market 4.2.1 Driving forces of the electricity market development 4.2.2 Operation process of wholesale electricity markets 4.2.3 New products and designs in wholesale electricity markets 4.2.3.1 Considering flexibility in resource adequacy 4.2.3.2 Flexible ramping products 4.2.3.3 Energy storage market modeling 4.2.3.4 Demand response resources market modeling 4.2.4 Running the electricity market with 100% renewables 4.2.4.1 Motivations 4.2.4.2 Bulk electric power grid dispatch in the all inverter-based resources systems 4.2.4.3 Increased uncertainty and variability 4.2.4.4 Increased complexity for the controls of utility-scale inverter-based resources 4.2.4.5 All zero marginal cost resources 4.2.4.6 Unit commitment and economic dispatch for up to 100% renewables 4.2.4.7 Determination of reserve requirements 4.2.4.8 New frequency response mechanism design 4.3 Modern retail electricity market 4.3.1 State-of-the-art for retail electricity market development 4.3.2 Design of retail electricity market operation framework 4.3.3 Implementation of blockchain technology in electric power systems 4.4 Conclusion Referencesfive Wide-area monitoring and anomaly analysis based on synchrophasor measurement 5.1 Synchrophasor measurement technology introduction 5.1.1 Situational awareness 5.1.2 Advanced control 5.2 Wide-area measurement system example—FNET/GridEye 5.3 FNET/GridEye wide-area measurement system applications overview 5.3.1 Visualization 5.3.2 Disturbance detection and location 5.3.3 Interarea oscillation detection and event-data-based oscillation modal analysis 5.3.4 Online ambient-data-based oscillation modal analysis 5.3.5 Islanding detection 5.3.6 Event replay and postevent analysis 5.3.7 Statistical analysis of historical data 5.3.8 Model validation and parameter verification 5.3.9 Machine learning–based inertia estimation References Further readingsix Advanced grid operational tools based on state estimation 6.1 Introduction 6.2 Model validation 6.2.1 Largest Normalized Lagrange Multiplier test 6.2.1.1 Extraction of Lagrange multipliers from state estimation problem 6.2.1.2 Normalized Lagrange multipliers and hypothesis testing 6.2.1.3 Detection, identification, and correction of model parameter errors 6.2.2 Computationally efficient implementation of the Largest Normalized Lagrange Multiplier test 6.2.3 Detectability and identifiability of parameter and measurement errors 6.3 System monitoring 6.3.1 Motivations for dynamic state estimation 6.3.2 Problem formulation of dynamic state estimation 6.3.3 Unified framework for Bayesian dynamic state estimation through nonlinear regression 6.3.3.1 Bayesian state estimators for nonlinear dynamic system models 6.3.3.2 Proposed unified framework for nonlinear Bayesian dynamic state estimation 6.3.3.3 Proposed framework for robustifying the Bayesian dynamic state estimation 6.3.3.4 Decentralized versus centralized dynamic state estimation for power system 6.4 Protective relaying 6.4.1 Theoretical basis of dynamic state estimation–based protection 6.4.1.1 Dynamic model of the protection zone 6.4.1.2 Quantification of consistency through dynamic state estimation algorithm 6.4.1.3 Trip decision 6.4.2 Numerical experiments 6.4.2.1 Example test system: series compensated transmission line 6.4.2.2 Dynamic model of the series compensated transmission line 6.4.2.2.1 Dynamic model of section k in the multisection line 6.4.2.2.2 Dynamic model of the three-phase series capacitors 6.4.2.2.3 Formulation of the overall dynamic model 6.4.2.3 Legacy protection functions for comparison and corresponding settings 6.4.2.4 Event study 6.5 Conclusion remarks References Further readingseven Advanced machine learning applications to modern power systems 7.1 Introduction 7.2 Modern forecasting technology 7.2.1 Prior research work 7.2.1.1 Deterministic forecasting methods 7.2.1.2 Probabilistic forecasting methods 7.2.1.3 Ensemble learning 7.2.2 Ensemble learning forecasting methodologies 7.2.2.1 Single-machine learning algorithm models 7.2.2.2 Competitive ensemble learning 7.2.2.3 Cooperative ensemble learning 7.2.3 Forecasting results 7.2.3.1 Case study I: wind speed forecasting based on competitive ensemble learning 7.2.3.2 Case study II: wind power forecasting based on cooperative ensemble learning 7.3 Machine learning–based control and optimization 7.3.1 Prior research work 7.3.1.1 Machine learning–based control 7.3.1.2 Machine learning–based optimization 7.3.2 A Machine learning–based network reconfiguration methodology 7.3.2.1 Literature review on network reconfiguration 7.3.3 Network reconfiguration results 7.4 Advanced artificial intelligence and machine learning applications to building occupancy detection 7.4.1 Prior research work 7.4.2 The convolutional neural network–long short-term memory deep learning architecture 7.4.2.1 Occupancy detection problem formulation 7.4.2.2 Convolutional neural network 7.4.2.3 Long short-term memory network 7.4.2.4 The developed convolutional neural network–long short-term memory architecture 7.4.3 Experiments 7.4.4 Results 7.5 Conclusion Referenceseight Power system operation with power electronic inverter–dominated microgrids Nomenclature 8.1 Power system evolution toward modernization 8.2 Networked microgrids with parallel inverters 8.2.1 Advanced microgrid structures 8.2.2 Concept of dynamic microgrids 8.3 Parallel inverter operation in microgrids 8.3.1 Parallel inverter operation in the context of dynamic microgrids—steady-state operation 8.3.2 Parallel inverters operation in the context of dynamic microgrids—transient-state operation 8.3.2.1 Inverter dynamic stability during network reconfiguration 8.3.2.2 Network reconfiguration with improved inverter operation performance 8.3.2.3 Seamless network reconfiguration using advanced inverter control 8.4 Conclusion Referencesnine Automated optimal control in energy systems: the reinforcement learning approach 9.1 Introduction 9.1.1 Background 9.1.2 Markov decision process and Bellman equations 9.1.2.1 Policy 9.1.2.2 Value function 9.1.2.3 Bellman equations 9.1.2.4 Bellman expectation equations 9.1.2.5 Bellman optimal equations 9.1.3 Solving Markov decision process problems 9.1.4 Value-based reinforcement learning 9.1.4.1 Monte Carlo reinforcement learning 9.1.4.2 Temporal difference reinforcement learning 9.1.4.3 Value function approximation 9.1.5 Policy-based reinforcement learning 9.1.6 Actor–critic reinforcement learning 9.1.7 Summary 9.2 Deep reinforcement learning 9.2.1 What is deep reinforcement learning 9.2.2 Introduction to three deep reinforcement learning algorithms 9.2.2.1 Deep Q-network: a value-based approach 9.2.2.2 Asynchronous advantage actor–critic: an actor–critic approach 9.2.2.3 Evolution strategies–based reinforcement learning 9.2.3 Scalable reinforcement learning frameworks 9.2.4 Curriculum learning 9.2.5 Meta learning 9.2.6 Multiagent system 9.2.7 Summary 9.3 Reinforcement learning in energy systems 9.3.1 Advantages of applying reinforcement learning in engineering problems 9.3.2 Training a reinforcement learning controller 9.3.2.1 Typical workflow 9.3.2.2 Building a reinforcement learning environment 9.3.2.3 Selecting the right algorithm 9.3.3 Reinforcement learning applications in energy systems 9.3.3.1 Smart buildings 9.3.3.2 Demand response 9.3.3.3 Grid operation 9.3.3.4 Renewable generation and battery control 9.3.4 Some interesting research topics 9.3.5 Limitations and challenges 9.3.6 Summary ReferencesTen Power, buildings, and other critical networks: Integrated multisystem operation 10.1 Introduction 10.1.1 Aging infrastructure and climate-related impacts 10.1.2 Increasing electrification in the built environment 10.1.3 Increasing connectivity in power, water, and gas networks 10.2 Grid-interactive buildings 10.2.1 Distributed energy resources 10.2.2 Demand response 10.2.3 Emerging considerations for demand response 10.2.4 Climate and environment 10.2.5 Cybersecurity and privacy 10.3 Interdependent critical networks 10.3.1 Water and energy 10.3.2 Power and gas 10.3.3 Combined heat and power 10.4 Electrification of the transportation sector 10.4.1 Consumer vehicles 10.4.2 Public transportation 10.4.3 Rideshare services and emerging methods of transportation 10.4.4 Vehicle-to-grid 10.5 Considerations for future power systems 10.5.1 Physical considerations 10.5.2 Market and organizational considerations 10.5.3 Cyber considerations Acknowledgments ReferencesIndex*

NEW TECHNOLOGIES FOR POWER SYSTEM OPERATION AND ANALYSIS

NEW TECHNOLOGIES FOR POWER SYSTEM OPERATION AND ANALYSIS Edited by

HUAIGUANG JIANG National Renewable Energy Laboratory, Golden, CO, United States

YINGCHEN ZHANG Chief Engineer and Group Manager of National Renewable Energy Laboratory, Golden, CO, United States

EDUARD MULJADI Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849, United States

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-820168-8 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Joe Hayton Acquisition Editor: Lisa Reading Editorial Project Manager: Ali Afzal Khan Production Project Manager: R. Vijay Bharath Cover Designer: Greg Harris Typeset by MPS Limited, Chennai, India

Contents List of contributors

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1. Introduction

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Yan Li, Changhong Zhao, Huaiguang Jiang, Yingchen Zhang and Eduard Muljadi 1.1 Overview of power systems 1.2 The development history of power systems in the United States 1.3 Distributed energy resource units 1.4 Steady-state conditions 1.5 AI and machine learning 1.6 Network dynamic operation 1.7 Multisector coupling 1.8 Structure of this book References

2. Decoupled linear AC power flow models with accurate estimation of voltage magnitude in transmission and distribution systems

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Ning Zhang, Jingwei Yang, Yi Wang, Hai Li and Chongqing Kang Nomenclature 2.1 Introduction 2.2 Linear power flow models for the meshed transmission systems 2.3 Linear three-phase power flow models of the unbalanced distribution systems 2.4 Case study 2.5 Conclusion References

3. Renewable energy integration and system operation challenge: control and optimization of millions of devices

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Ying Xu, Wei Sun and Zhihua Qu 3.1 3.2 3.3 3.4

Introduction Distribution system model with high penetration of renewables Autonomous distributed voltage control Hierarchical multiagent control of large-scale distribution system

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3.5 Islanded microgrid with high penetration of distributed generations 3.6 Grid-edge situational awareness: enhanced observability by voltage inference 3.7 Control-enabled dynamic hosting allowance: P and Q control capacity and impact analysis 3.8 Cosimulation of integrated transmission and distribution systems 3.9 Conclusion References

4. Advances of wholesale and retail electricity market development in the context of distributed energy resources

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Qin Wang 4.1 Introduction 4.2 Modern wholesale electricity market 4.3 Modern retail electricity market 4.4 Conclusion References

5. Wide-area monitoring and anomaly analysis based on synchrophasor measurement

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Shutang You, Yu Su, Yong Liu and Yilu Liu 5.1 Synchrophasor measurement technology introduction 5.2 Wide-area measurement system example—FNET/GridEye 5.3 FNET/GridEye wide-area measurement system applications overview References Further reading

6. Advanced grid operational tools based on state estimation

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Yu Liu, Yuzhang Lin and Junbo Zhao 6.1 Introduction 6.2 Model validation 6.3 System monitoring 6.4 Protective relaying 6.5 Conclusion remarks References Further reading

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7. Advanced machine learning applications to modern power systems

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Cong Feng, Mucun Sun, Morteza Dabbaghjamanesh, Yuanzhi Liu and Jie Zhang 7.1 7.2 7.3 7.4

Introduction Modern forecasting technology Machine learning based control and optimization Advanced artificial intelligence and machine learning applications to building occupancy detection 7.5 Conclusion References

8. Power system operation with power electronic inverter dominated microgrids

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Yuhua Du, Xiaonan Lu and Xiongfei Wang Nomenclature 8.1 Power system evolution toward modernization 8.2 Networked microgrids with parallel inverters 8.3 Parallel inverter operation in microgrids 8.4 Conclusion References

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9. Automated optimal control in energy systems: the reinforcement learning approach

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Xiangyu Zhang and Huaiguang Jiang 9.1 Introduction 9.2 Deep reinforcement learning 9.3 Reinforcement learning in energy systems References

10. Power, buildings, and other critical networks: Integrated multisystem operation

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Kyri Baker 10.1 Introduction 10.2 Grid-interactive buildings 10.3 Interdependent critical networks 10.4 Electrification of the transportation sector 10.5 Considerations for future power systems Acknowledgments References Index

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List of contributors Kyri Baker University of Colorado Boulder, Boulder, CO, United States Morteza Dabbaghjamanesh Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, United States Yuhua Du Temple University College of Engineering, Philadelphia, PA, United States Cong Feng Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, United States Huaiguang Jiang National Renewable Energy Laboratory, Golden, CO, United States Chongqing Kang Department of Electrical Engineering, Tsinghua University, Beijing, P.R. China Hai Li Department of Electrical Engineering, Tsinghua University, Beijing, P.R. China Yan Li Pennsylvania State University, University Park, State College, PA, United States Yuzhang Lin Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA, United States Yilu Liu University of Tennessee, Knoxville, TN, United States; Oak Ridge National Laboratory, Oak Ridge, TN, United States Yong Liu Pacific Gas and Electric Company, San Francisco, CA, United States Yu Liu School of Information Science and Technology, ShanghaiTech University, Shanghai, P.R. China Yuanzhi Liu Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, United States Xiaonan Lu Temple University College of Engineering, Philadelphia, PA, United States Eduard Muljadi Auburn University, Auburn, AL, United, States

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Zhihua Qu Department of Electrical and Computer Engineering, University of Central Florida, FL, United States Yu Su University of Tennessee, Knoxville, TN, United States Mucun Sun Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, United States Wei Sun Department of Electrical and Computer Engineering, University of Central Florida, FL, United States Qin Wang Department of Power Delivery & Utilization, Electric Power Research Institute, Palo Alto, CA, United States Xiongfei Wang Department of Energy Technology, Aalborg University, Aalborg, Denmark Yi Wang Department of Electrical Engineering, Tsinghua University, Beijing, P.R. China Ying Xu Department of Electrical and Computer Engineering, University of Central Florida, FL, United States Jingwei Yang Department of Electrical Engineering, Tsinghua University, Beijing, P.R. China Shutang You University of Tennessee, Knoxville, TN, United States Jie Zhang Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, United States Ning Zhang Department of Electrical Engineering, Tsinghua University, Beijing, P.R. China Xiangyu Zhang National Renewable Energy Laboratory, Golden, CO, United States Yingchen Zhang National Renewable Energy Laboratory, Golden, CO, United States Changhong Zhao Chinese University of Hong Kong, Hong Kong, P.R. China Junbo Zhao Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, United States

CHAPTER ONE

Introduction Yan Li1, Changhong Zhao2, Huaiguang Jiang3, Yingchen Zhang3 and Eduard Muljadi4 1

Pennsylvania State University, University Park, State College, PA, United States Chinese University of Hong Kong, Hong Kong, P.R. China National Renewable Energy Laboratory, Golden, CO, United States 4 Auburn University, Auburn, AL, United, States 2 3

Contents 1.1 Overview of power systems 1.2 The development history of power systems in the United States 1.3 Distributed energy resource units 1.4 Steady-state conditions 1.5 AI and machine learning 1.6 Network dynamic operation 1.7 Multisector coupling 1.8 Structure of this book References

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1.1 Overview of power systems The modern power grid is one of the most complex human-made engineering systems. In power grids, there are three subsystems, that is, generation system, transmission system, and distribution system, as shown in Fig. 1.1. In the generation system, electricity is produced in power plants through burning fossil fuels (e.g., coal, gas, and oil), nuclear material, or using hydroelectric power plants. The function of generation systems is to convert chemical energy to electric energy. Then the electrical energy is converted to a higher voltage level through transformers and transmitted through the power lines in the transmission system. Once the energy has traveled through the transmission system, the voltage is brought back down. Then, the energy reaches the distribution system, where we deliver energy to residential customers, commercial customers, industry, and other power loads. New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00001-8

© 2021 Elsevier Inc. All rights reserved.

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Figure 1.1 Overview of power systems.

Power generation in the traditional power grid is highly centralized, with power and energy flowing unidirectionally from generators in the generation system through a transmission/distribution network to end users. However, technological issues of traditional electric utilities as well as environmental problems caused by the combustion of fossil fuels have prompted the research and development of new pattern of power systems. With the emergence of distributed energy resource (DER) units for example, wind, photovoltaic (PV), battery, biomass, micro-turbine, and fuel cell microgrids have attracted increasing attention as an effective means of integrating such DER units into power systems. The major constituents in microgrid are [1] DER units for power generation and energy storage, control systems for power/energy control and dispatch, and controllable loads for demand response. Microgrid research and development is currently propelled by technology advances in renewable energy, power electronics (PEs), control theory, data management, interconnected systems, wireless networks, as well as advances in interoperability and high-performance computing. The abovementioned technologies have been increasing the complexity of operation and control of microgrids. On one hand, microgrids will create a complicated

Introduction

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interconnected power grid with various partners as shown in Fig. 1.2; on the other hand, the presence of microgrids will transform the traditional centralized system in to a distributed active one. A typical microgrid prototype is given in Fig. 1.3, where PV, battery, PE device, and controller are installed in a house. This microgrid has two operational modes: a grid-connected mode and islanded mode. This means “my house” can either purchase electricity from “power utility” or directly get power energy from PV or battery. When the sunlight is strong, the electricity power generated by PV can be stored in the battery. When the sunlight is weak or during night, the power energy stored in the battery can be released to support customers’ electricity consumption. Thus, it significantly improves the reliability of electricity. Moreover, the controller is playing a critical role in coordinating and operating this microgrid prototype. Although this microgrid system is very promising, it also induces several challenging problems. For instance, how can it dispatch power between roof top solar panel and battery? How can we sell electricity to power utility?

Figure 1.2 The future power system.

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Figure 1.3 An example of community microgrid.

1.2 The development history of power systems in the United States The development of power systems started in the 18th century.

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The first real practical uses of electricity began with the telegraph in the 1860s. The use of electricity continued with arc lighting in the 1870s. In the early 1880s, Edison introduced Pearl Street DC system in Manhattan, which supplied 59 customers. In 1884, Sprague produced practical DC motor. In 1885, the transformer was invented to change voltage levels. In the mid-1880s, AC power system was introduced by Westinghouse/Tesla. In late 1880s, the AC induction motor was invented by Tesla. In 1893, the first three-phase transmission line was produced, which was at 2.3 kV. In 1896, AC lines started to deliver electricity. For instance, electricity was delivered from hydro generation at Niagara Falls to Buffalo, which was 20 miles away. In the 19th century, the power system is further developed.

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In the early 1900s, private utilities supplied all customers in area (city), which was recognized as a natural monopoly. At that time, states stepped in to begin regulating the system. By the 1920s, large interstate holding companies control most electricity systems. In 1935 US Congress the passed Public Utility Holding Company Act to establish national regulation, which broke up large interstate utilities. This gave rise to electric utilities that only operated in one state. In 1935/36, the Rural Electrification Act brought electricity to rural areas. In the 1930s, electric utilities were established as vertical monopolies. In the 1930s, the frequency of power systems was standardized as 60 Hz in the United States. The 1970s brought inflation, increased fossil-fuel prices, and called for conservation and growing environmental concerns. In 1978, the Public Utilities Regulator Policies Act (PURPA) was passed by US Congress, which mandated that utilities must purchase power from independent generators located in their service territory. Therefore, PURPA started to introduce competition. Then in 1992, National Energy Policy Act was passed to boost the major opening of industry to competition. This act mandated that utilities provide “nondiscriminatory” access to the high voltage transmission, which means the goal was to set up true competition in generation. Over the last few years the electric utility industry has been dramatically restructured.

1.3 Distributed energy resource units DER units have attracted increasing attention due to the worldwide problem of energy and the rapid development of PEs. DER units can be either rotating or stationary (PE-based DER units) and either dispatchable or nondispatchable. Moreover, some DER units may operate in plugand-play fashion. In microgrids, the dispatchable DER units can be divided into three categories: (1) the DER unit itself can be dispatchable, for example, fuel cell, micro-turbine, and battery; (2) these units can be operated with revised MPPT making them dispatchable, although the outputs of DER units fluctuate, which are usually controlled by maximum power point tracking (MPPT), for example, wind and PV; and (3)

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for the abovementioned DER units with fluctuating outputs, they can be combined with energy storage units. The combination of these various types of DER units makes microgrid control a challenging task. Furthermore, the increasing penetration of DER units changes the operation and management of power systems. The main challenges include but are not limited to the bidirectional power flow, the variations in voltage and frequency profiles, the variations in short circuit current, etc. All of these play an important role in the system operations; and thus, it is important to investigate and understand the operations of DER units. Taking PV as an example, Fig. 1.4 shows the typical structure of connecting PV to the grid. PV is a direct means to convert sunlight to electrical energy. The efficiency of energy conversion is usually in the range of 10%20%. One important feature of PV is that it outputs DC power instead of AC power. The DC output of PV is converted by a PE interface (e.g., the inverter in Fig. 1.4) to AC output and then integrated into the physical system through an interface circuit. Phase lock loop (PLL), outer controller, inner controller, and pulse width modulation (PWM) are the typical control systems of PV. Specifically, the PLL is adopted to identify the phase of input signals, usually the three-phase voltages. This phase is then used to generate a control signal for the double-loop controller, specifically the outer controller and inner controller. Finally, the signals generated by the double-loop controller are transformed from the dq frame to the abc frame, and the PWM technique is then used to generate signals to

Figure 1.4 Connecting photovoltaic with grids.

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control the switches, such as insulated-gate bipolar transistors (IGBTs), in the inverter. The output of PV highly depends on the environment condition, such as irradiance and temperature. Fig. 1.5 gives PV curves that show typical output of PV under different conditions. Fig. 1.5 shows that there is a maximum output for each specific temperature or irradiance value. Therefore, the MPPT strategy is usually adopted to allow PV to generate the maximum power output, in order to increase the efficiency of energy conversion. Fig. 1.5 also shows that when the temperature increases, the maximum values of PV curves decrease; when the irradiance increases, the maximum values of PV curves increase as well. Integrating DER into the power grids has been a challenge for utilities. Recently, microgrids have been studied from various aspects, including control strategies for DER units in a microgrid; stability analysis for a single microgrid, and control and operation of microgrids. For the control strategies, optimal control methods based on different algorithms (e.g., particle swarm optimization [25] and sequential quadratic programing [6]) from different operation perspectives (e.g., stability and economics) were proposed for the optimal dispatch and control of DER units in a microgrid. For the stability research of a single microgrid, optimal design of novel controllers (e.g., feedback controllers [7], mode-adaptive controller [8,9], hierarchical control system [1012], adaptive decentralized controller [13], advanced droop control loop [14,15], nonlinear stabilizer [16], and distribution static compensator [17]) were proposed for the effective integration of DER units and stable operation of microgrids [18]. Besides, a synchronverter was developed to mimic synchronous generators, based on which DER units within microgrids can be

Figure 1.5 PV curves. PV, Photovoltaic.

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automatically synchronized with main grid without the traditional PLL and then controlled to participate frequency regulation and voltage regulation [19,20]. For the multiple microgrid system, research has been conducted for the control and operation. New methodologies exploiting optimization tools (e.g., based on a metaheuristic approach [21]) were developed for the voltage stability in the multiple microgrid system. In addition, frequency control issue was investigated in Ref. [21] based on hierarchical control structure of multiple microgrids during mode transitions and load following in islanded operation. Furthermore, frequency control reserves in multiple microgrids were researched in Refs. [22,23] under different control strategies and different dynamic loads. Also state estimation and assessment of multiple microgrids were investigated in Refs. [21,24] based on the weighted least squares algorithm, real-time measurements, and fuzzy state estimation approach for different operation modes. In addition, other research has been focused on impact of high DER penetration and system parameters and structures [2527], optimal power flow (OPF) [28], demand response [29,30], stability analysis [3138], power quality issue [39], modeling and evaluation of system reliability [39], optimized operation [40], market mechanism [41], service restoration for black start [42], etc.

1.4 Steady-state conditions Due to system interconnections and integration of DERs, the modern electrical power systems have become more and more complex. The steady-state operation is playing a fundamental role in running power [36]. Steady-state operation refers to the ability of the power system to maintain synchronism after small and slow disturbances, such as gradual power changes and fluctuations of PV. From the frequency perspective, in the United States, all synchronous generators are required to rotate with the same speed and produce 60 Hz voltage. The permitted frequency deviation is less than 6 0.5 Hz. From the voltage perspective, any increase or decrease in power load will change the angle between the induced voltage and terminal voltage. So, to maintain the stable operation, the voltage on each bus must be within the range of 6 5% 6 8% of the rated value.

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Power flow calculation is usually used to investigate the steady-state operation of power grids. In power flow calculation, there are three typical buses as shown in Fig. 1.6: a slack bus, a generator bus, and a load bus. The slack bus provides the reference for the whole system, which means its voltage magnitude and angle are predefined. For the generator buses, their active power output and voltage magnitude are usually known, which means the voltage angle and reactive power need to be determined via power flow calculation. For the load buses, their active and reactive power outputs are usually known, which means the voltage magnitude and angle need to be determined via power flow calculation. The goal of power calculation is to get the voltage and power output in the power system by solving the following algebraic equations: I 5 YU;

(1.1)

where Y is the system admittance matrix, U is the voltage vector, and I is the injection current vector which represents the power output of generators and power consumption of loads. There are several algorithms to solve the previous power flow issue; for instance, NewtonRaphson solution method and its derivatives, GaussSeidel method, Fast-decoupled-load-flow method, and DC power flow. The appropriate algorithm should be selected on the basis of study system. AC OPF is a fundamental problem in power system operations. At the distribution level, OPF underlies (and possibly unifies) many applications, such as Volt/VAr/Watt control, dispatch of renewable energy sources, and demand response. With the rapid growth of DERs—including renewables, energy storage devices, and flexible loads—it is crucial for distribution systems to solve OPF in a fast and scalable way over a large number of active nodes. Toward this end, nonconvexity of AC OPF is a

Figure 1.6 Classification of buses in power grids.

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major hurdle, and recent efforts have looked at centralized and distributed OPF solution methods based on convex approximations or relaxations; see, for example, Refs. [4346]. A popular convex approximation is obtained through the linearization of the power flow equations [47,48]. Semidefinite relaxation is another commonly taken approach to convexify OPF problems. To the best of our knowledge, it was first proposed in Ref. [49] to solve OPF as a semidefinite program (SDP) in single-phase networks with general topologies, and it was first studied in Ref. [50] whether and when this SDP relaxation is exact, meaning will the optimal solution of the SDP be feasible and globally optimal for the original nonconvex OPF as well. Sparsity of power networks was exploited to simplify the SDP relaxation [51,52], and relaxation to a more efficiently solvable second-order cone program (SOCP) is available in radial (tree) networks [53,54]. Techniques such as quadratic convex relaxation [55], moment/the sum-of-squares hierarchy [56], and strong SOCP relaxation [57] have also been explored to strengthen the SDP relaxation. However, power distribution networks in practice are multiphase and radial and composed of both wye- and deltaconnected power sources and loads. In multiphase radial networks, Ref. [58] was the first that we know of that applied SDP relaxation; later, Ref. [59] illustrated SDP relaxation on a numerically more stable branch flow model. Both Refs. [58,59] considered wye connections only. In Ref. [60], a first semidefinite relaxation was proposed to incorporate delta-connected sources and loads.

1.5 AI and machine learning With the increasing number of the smart sensors such as synchronized sensors and unsynchronized sensors in power systems, massive heterogeneous data are collected: structured data such as voltage, frequency, wind speed, solar irradiance, and real-time electrical price in addition to unstructured data such as customer service (voice and text), sky image, and satellite image. There are three major features that can be found in the collected big data from power systems [61,62]: 1. Heterogeneous: The collected data is from different areas and contains different characteristics such as different sample speeds, different volumes, different scales, and different physical meanings.

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2. Large volume and high velocity: The data volume can reach PB level with various kinds of high-speed sensors, and the natural language (customer service) and images also bring in a large volume of data. 3. Low value density: With this huge volume of data, the useful information is very sparse in the collected data and requires fast and accurate methods to process. As discussed previously, it is imperative to build an artificial intelligence (AI) and machine learning based on the data processing and information extraction methods developed for power system operation and control. Generally, there are four major areas: 1. Perception: In this area, the goal is to monitor the power system, estimate the real system state, and analyze different scenarios (including the various faults) that appeared in the power system. 2. Forecasting: In this area, the goal is to track and forecast several variables in power systems such as load and system states. Furthermore, some basic inferences, such as basic decisions can also be based on the forecasting or regression results. 3. Operation and control: In this area, the AI and machine learning approaches are used to assist power system optimization, control, schedule, operation, and complex decision-making based on the results of perception and forecasting. In Refs. [6366], based on the machine learning methods, the smart sensor data are used to detect, locate, and identify different scenarios or faults in the power systems. In Refs. [6770], various machine learning methods are implemented to forecast the power system’s future information, such as load and system states. In Refs. [7173], with the perception and forecast results, various machine learningbased methods are investigated for power system scheduling, control, operation, and complex decision-making. Furthermore, with advanced machine learning approaches, such as reinforcement learning and other approaches, the performance of power system operation, schedule, and control can be further improved and can be integrated with other systems [7478].

1.6 Network dynamic operation Although steady state is fundamentally important for power system operation, only studying the steady-state operation is not enough to run a practical power system. In order to operate an interconnected

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power grid, all the generators (and other synchronous machines) must remain in synchronism, which requires the rotors of synchronous machines turning at the same speed. Loss of synchronism will result in several severe conditions where power cannot be successfully delivered to customers. If one or more generators lose synchronism under a disturbance, this scenario is transiently unstable. In order to study the transient response of a power system, we need to develop system models during the transient time frame of several seconds following a system disturbance, where both electrical and mechanical models will be developed. Fig. 1.7 summarizes the timescale of different power system studies. For different studies, we may need to consider different models. For instance, when we analyze transient stability, three systems usually need to be investigated, that is, prefault, faulted, and postfault. In the prefault condition, the system is assumed to be at an equilibrium point before the fault occurs. During fault, the system equations are changed, moving the system away from the above equilibrium point. Postfault shows system performance after the fault is cleared, where the system might or might not move to a new stable operating point. Power system stability is another important concept in analyzing the dynamics of power grids. Fig. 1.8 summarizes the typical power system stability studies.

Figure 1.7 Timescale of power system studies [79].

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Figure 1.8 Categories of power system stability.

There are several methods for each stability analysis. Taking transient stability as an example, time-domain numerical integration and direct method are two typical approaches. Time-domain numerical integration is used to determine following a contingency whether the power system can return to a steady-state operating point. The goal of numerical integration is to solve a set of differential and algebraic equations (DAE) which are used to model the system. The DAE is given as follows: 8 < dx 5 f ðx; yÞ dt (1.2) : 0 5 gðx; yÞ; where x is the set of dynamic state variables and y is the set of algebraic variables. Numerical integration is by far the most common technique to analyze system dynamics, particularly for large power systems. During the fault and after the fault, the power system DAE models are updated and solved using numerical methods. In the numerical integration, when the values of some variables change very slowly, such as the chemical action in the fuel cell, they can

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be treated as constant variables during the integration, whereas when the values of other variables change very quickly, such as PE devices, they can be treated as algebraic variables. The second approach of system transient stability analysis is direct method, which is mostly used to provide an intuitive insight into the transient stability problem. The regions of attraction can be provided by direct method computation, which is unattainable with time-domain numerical integration. Moreover, direct method can be used to quickly check whether a specific control action is able to stabilize a power system. For a two-bus power system, this method is known as the equal area criteria. The continued growth of variable renewable energy brings major challenges to the grid: the increased volatility of power injections from renewables and the reduced number of synchronous generators providing mechanical inertial support can severely degrade the dynamic performance and jeopardize the stability of the grid, especially in terms of frequency. Frequency control is one of the most critical grid services to maintain the safety of the electrical infrastructure and the quality of power delivery. Traditionally, large synchronous generators have been responsible for keeping the frequency tightly around its nominal value (e.g., 60 Hz in the United States) through coordinated actions across multiple timescales: inertial response of rotating masses to limit the rate of change of frequency in the first few seconds after a disturbance; droop control or primary frequency control of speed governors to stabilize frequency to a steady state within the low tens of seconds; and automatic generation control or secondary frequency control, which is a centralized paradigm run by the transmission system operator, to restore the nominal frequency within a few minutes. This type of hierarchical control architecture featuring a clear timescale separation and relying on slow and infrequent actions of a small number of generators might be sufficient for today’s grid; but it lacks the responsiveness and flexibility to meet the future requirements for grid resilience, reliability, and efficiency at higher penetration levels of variable renewable generation. A tremendous opportunity to address this rising concern lies in (1) the proliferation of DERs, for example, controllable loads, electric vehicles, energy storage devices, and PV systems, which are equipped with advanced PEs such as DC/AC inverters that can push or pull power at a much faster timescale and with a much finer resolution than synchronous generators and (2) the recent and continued advances in sensing, control,

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and communication technologies and the large-scale deployment of these technologies in the grid. Based on these trends, we envision a future grid with millions of interconnected active endpoints—DERs—as well as our increased capability to coordinate and optimize the operations of these endpoints. With this in mind the following natural question arises: how shall we exploit the control capabilities of DERs to improve grid dynamics, stability, and resilience in response to severer generation variations and contingencies under higher penetration levels of renewables? This question can be answered in different contexts (transient stability, voltage regulation, frequency control, economic dispatch, etc.). One series of work [8082] tried to answer this question from a perspective of jointly developing dynamic feedback control and steady-state optimization of power networks, with application in distributed, load-side, optimal frequency control. Specifically, scalable distributed feedback control algorithms are developed and deployed to steer the power network frequency, power flow, generation, and load to a steady-state operating point that not only fulfills all the stability and security requirements but also achieves economic efficiency measured by generation cost, user disutility, and so on. From another perspective, feedback-based algorithms have been developed to save computation effort in pursing the optimal steady state and facilitate online tracking of OPF solutions that may vary on a timescale of seconds [8385].

1.7 Multisector coupling Subsystems constituting our interconnected critical infrastructure, such as electricity, natural gas, municipal water, district heating, and transportation systems, are traditionally operated in isolation. Further, due to nonlinearity and nonconvexity of their underlying physics, off-the-shelf “black-box” tools are used to solve challenging optimization and control problems associated with each subsystem separately. Such separation of architecture and limitation in optimization tools brings, a huge gap between the benefit that can be achieved theoretically and that has been gained today. Overcoming these limitations can bring significant benefits from operational-efficiency, socioeconomic, and environmental perspectives, as recognized by the literature. Therefore, in recent years, researchers have been pursuing the goal of developing distributed and

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computationally efficient optimization algorithms for interconnected power, gas, water, heating, and transportation networks. For instance, Ref. [86] formulated an optimal powerwater flow problem to jointly control DERs, tanks, and pumps in an interconnected power distribution and municipal water network and used a successive convex inner approximation method to identify a locally optimal solution. In addition, it developed a distributed optimization solver based on alternating the direction method of multipliers, which enables power and water operators to pursue individual objectives, while respecting intrinsic couplings between them. Another facet of research on the coupling of multiple sectors is to integrate transportation network with power network, on which Ref. [87] delivers a first version of an analytic model and distributed algorithm for optimal routing and charging scheduling of electric vehicles to simultaneously shave the peak load in the power distribution system and the peak traffic in the urban transportation system. Future research challenges along this direction will include (1) deriving optimization models for large-scale comprehensive urban infrastructure that strikes a good balance between accuracy and computational affordability; (2) developing convex relaxation techniques that can produce globally optimal solutions or characterize optimality gaps for optimization problems containing nonconvex constraints associated with the intrinsic

Figure 1.9 Structure of this book.

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physics of different sectors; (3) developing fast, scalable, and privacypreserving distributed optimization and control algorithms for the synergic and economically efficient operations of different system operators and utilities in different sectors; and (4) exploiting learning approaches applied on the big volume of urban data to aid complex and coupled system modeling and controller design.

1.8 Structure of this book The structure of this book is given in Fig. 1.9, where all the chapters briefly introduced previously will be discussed.

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[69] H. Jiang, F. Ding, Y. Zhang, Short-term load forecasting based automatic distribution network reconfiguration, in: IEEE Power and energy Society General Meeting, 2017. [70] F. Ding, H. Jiang, J. Tan, Automatic distribution network reconfiguration: an eventdriven approach, in: IEEE Power and energy Society General Meeting, 2016. [71] Y. Gu, H. Jiang, J.J. Zhang, Y. Zhang, E. Muljadi, Multi-timescale three-phase unbalanced distribution system operation with variable renewable generations, in: IEEE Transactions on Smart Grid, Jul. 2018. [72] Y. Gu, H. Jiang, J.J. Zhang, Y. Zhang, E. Muljadi, F.J. Solis, Chance-constrained day-ahead hourly scheduling in distribution system operation, in: 2017 IEEE. 51st Asilomar Conference on Signals, Systems, and Computers, 2017, pp. 13631367. [73] Y. Gu, H. Jiang, J.J. Zhang, Y. Zhang, E. Muljadi, F.J. Solis, Load forecasting based distribution system network reconfiguration—a distributed data-driven approach, in: 2017 IEEE. 51st Asilomar Conference on Signals, Systems, and Computers, 2017, pp. 13581362. [74] Q. Lu, Safe and efficient intersection control of connected and autonomous intersection traffic, arXiv (2018) preprint arXiv:1801.09361. [75] Q. Lu, K.-D. Kim, A genetic algorithm approach for expedited crossing of emergency vehicles in connected and autonomous intersection traffic, J. Adv. Transp. 2017 (2017). [76] Q. Lu, K.-D. Kim, Autonomous and connected intersection crossing traffic management using discrete-time occupancies trajectory, Appl. Intell. 49 (5) (2019) 16211635. [77] Q. Lu, K.-D. Kim, Intelligent intersection management of autonomous traffic using discrete-time occupancies trajectory, J. Traffic Logist. Eng. 4 (1) (2016) 16. [78] H. Xu, X. Li, X. Zhang, J. Zhang, Arbitrage of energy storage in electricity markets with deep reinforcement learning, arXiv (2019) 1904.12232v1. [79] V. Vijay, J.D. McCalley, P.A. Anderson, A.A. Fouad, Power system control and stability. John Wiley & Sons, 2019. [80] C. Zhao, U. Topcu, N. Li, S.H. Low, Design and stability of load-side primary frequency control in power systems, IEEE Trans. Autom. Control 59 (5) (2014) 11771189. [81] E. Mallada, C. Zhao, S.H. Low, Optimal load-side control for frequency regulation in smart grids, IEEE Trans. Autom. Control 62 (12) (2017) 62946309. [82] C. Zhao, E. Mallada, S.H. Low, J. Bialek, Distributed plug-and-play optimal generator and load control for power system frequency regulation, Int. J. Electr. Power Energy Syst. 101 (2018) 112. [83] L. Gan, S.H. Low, An online gradient algorithm for optimal power flow on radial networks, IEEE J. Sel. Areas Commun. 34 (3) (2016) 625638. [84] Y. Tang, K. Dvijotham, S.H. Low, Real-time optimal power flow, IEEE Trans. Smart Grid 8 (6) (2017) 29632973. [85] E. Dall’Anese, A. Simonetto, Optimal power flow pursuit, IEEE Trans. Smart Grid 9 (2) (2018) 942952. [86] A.S. Zamzam, E. Dall’Anese, C. Zhao, J.A. Taylor, N.D. Sidiropoulos, Optimal water-power flow problem: formulation and distributed optimal solution, IEEE Trans. Control Netw. Syst. 6 (1) (2019) 3747. [87] H. Jiang, Y. Zhang, Y. Chen, C. Zhao, J. Tan, Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing—a hierarchical data-driven approach, Appl. Energy 229 (2018) 756766.

CHAPTER TWO

Decoupled linear AC power flow models with accurate estimation of voltage magnitude in transmission and distribution systems Ning Zhang, Jingwei Yang, Yi Wang, Hai Li and Chongqing Kang Department of Electrical Engineering, Tsinghua University, Beijing, P.R. China

Contents Nomenclature 2.1 Introduction 2.2 Linear power flow models for the meshed transmission systems 2.2.1 Decoupling of voltage magnitude and phase angle 2.2.2 Matrix formulation of the decoupled linearized power flow model 2.2.3 Transformers and phase shifters 2.2.4 Derivation and justification of the fast decoupled linearized power flow 2.3 Linear three-phase power flow models of the unbalanced distribution systems 2.4 Case study 2.4.1 Meshed transmission systems 2.4.2 Balanced distribution systems 2.4.3 Unbalanced distribution systems 2.5 Conclusion References

23 24 26 26 30 31 33 37 40 40 41 44 47 47

Nomenclature The main notation used in this chapter is provided later; other symbols are defined as required.

n number of power system buses m number of power system lines R set of V θ buses (slack buses) S set of PV buses

New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00002-X

© 2021 Elsevier Inc. All rights reserved.

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ℒ set of PQ buses Y 5 G 1 jB admittance matrix of the power system Y0 5 G0 1 jB0 admittance matrix without shunt elements Yij 5 Gij 1 jBij element in the ith row and jth column of Y Y 0ij 5 G0ij 1 jB0ij element in the ith row and jth column of Y V, θ magnitudes and phase angles of bus voltages Vi, θi voltage magnitude and phase angle of bus i P, Q bus injected active and reactive powers Pi, Qi injected active and reactive power at bus i Pij lossless MW flow from bus i to bus j zij 5 rij 1 jxij impedance of line (i, j) yij 5 gij 1 jbij admittance of line (i, j) yii 5 gii 1 jbii shunt admittance at bus i

2.1 Introduction Power flow analysis is the backbone of power system studies and is widely applied in daily operations as well as short-term and long-term planning. Direct calculations of power flow are of great significance for contingency analysis [1], reliability assessment [2], and probabilistic load flow analysis [3], if they can be performed accurately and efficiently. Although conventional AC power flow calculations yield accurate results, their nonlinearity, difficulty in convergence, and low computational efficiency limit their application in the power system industry, especially for large-scale systems [4]. Therefore a fast and linear power flow model would be of great interest, provided it could offer reasonable accuracy and robustness for all types of grids. Such a linear model could also be beneficial for solving optimization problems, such as locational marginal pricing [5] and security-constrained unit commitment (SCUC) [6], by allowing these problems to be transformed into linear programming problems. The DC power flow (DCPF) model is one of the most widely used linear power flow models for power systems. Because it is a linear, noniterative model with reasonable accuracy in terms of MW flow, it has considerable analytical and computational appeal compared with the AC power flow model [7]. The classical DCPF model is derived based on the assumptions of a lossless MW flow and a flat bus voltage profile. More general versions of the DCPF model have been proposed, including hotand cold-start models, for broader applications.

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

25

Hot-start models correct the bus power injections according to base points obtained from initial AC power flow solutions in various ways, such as net loss dispersal and base-point matching [7]. However, in situations such as DC-model-based SCUC [6], financial transmission rights allocations [8] and power system planning, no reliable AC base points are available. Therefore it is necessary to use cold-start models. As alternatives to the classical DCPF model, some cold-start models modify the bus injections by estimating loss as a percentage of net load, whereas others apply an empirical fixed voltage magnitude. Several tests have shown, however, that there is no large difference in MW-flow error between the classical DCPF model and other cold-start models [7]. Although cold-start DCPF models are widely used in many fields, it is worth noting that they can neither consider reactive power nor shed light on bus voltage magnitude. In fact, the lack of a voltage magnitude consideration leads to many problems in system planning and operation when cold-start DCPF models are applied. For instance, it is a common practice to simply consider the MW flows in reliability evaluations. The bus voltage quality is inevitably ignored and, moreover, ZIP loads are modeled at nominal and typical voltages [2]. In terms of electricity markets, only active generation is priced in markets such as PJM. The reactive power offered by generators is paid for merely as an ancillary service [9]. Probabilistic load flow calculations, which are an increasingly important tool in power system analysis, typically face the dilemma that DCPF models are incapable of accounting for voltage magnitude, whereas AC power flow models that are linearized around the operating point lead to errors in scenarios with large signal perturbations [10]. However, it is not easy to consider bus voltage magnitudes in a decoupled linear flow model, because they are sensitive to both active and reactive power injections. In Ref. [11] a linear model was proposed for radial distribution systems. However, this model is dependent on the radial topology of the distribution system and cannot be extended to meshed transmission systems. The authors of Ref. [12] have achieved a great advancement in formulating a linear load equation that considers reactive power. This power flow model reflects both active and reactive power balance with respect to the square of the voltage magnitude, V 2, and the modified phase angle, V 2θ. Nevertheless, the voltage magnitude and phase angle are not completely decoupled, giving rise to a quadratic programming problem if this model is applied for optimization.

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This chapter makes a further step toward a V 2 θ decoupled linear power flow model. We propose a decoupled linearized power flow (DLPF) model with respect to the voltage magnitude V and the phase angle θ. The model is distinguished by its accuracy in voltage magnitude and robustness for application to different types of systems. The error in MW flow is comparable to that of cold-start DCPF models, which is much smaller in practice than is predicted, as mentioned in Ref. [7]. We also design a fast DLPF (FDLPF) model by developing a fast approximation of the matrices used in the DLPF model, which is as fast as classical DCPF calculations while still as accurate as DLPF calculations. The approximation is justified by theoretical derivation and numerical tests. Furthermore, we generalize this model to the three-phase power flow in unbalanced distribution systems. The remainder of the chapter is organized as follows: Section 2.2 formulates the DLPF model in transmission grid based on the initial AC power flow equation and derives the expressions for voltage magnitude, phase angle, and branch MW flow. Then the FDLPF model is formulated by making a subtle change to the DLPF model, which is justified by theoretical derivation and numerical examples. The specific steps for utilizing the FDLPF model are presented. In Section 2.3 a linear three-phase power flow of unbalanced distribution systems is proposed, which is a generalization of the DLPF model. The linearization of ZIP characteristics of the loads is also discussed in this section. In Section 2.4, several cases, including radial distribution systems, meshed large-scale transmission systems, and ill-conditioned systems, are analyzed to demonstrate the accuracy and robustness of the DLPF/FDLPF approach and the computational efficiency of the FDLPF model. Finally, conclusions are drawn and further discussions are presented.

2.2 Linear power flow models for the meshed transmission systems 2.2.1 Decoupling of voltage magnitude and phase angle For a power system with n buses the polar powervoltage AC power flow model is well known: Pi 5

n X j51

Gij Vi Vj cos θij 1

n X j51

Bij Vi Vj sin θij

(2.1)

27

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

Qi 5

n X

Bij Vi Vj cos θij 1

j51

n X

Gij Vi Vj sin θij

i 5 1; 2; . . .; n

(2.2)

j51

For most scenarios in real power systems the bus voltage magnitudes are approximately 1 p.u. The absolute value of phase angle differences across lines rarely exceed 30 degrees [12], and most of them are within 10 degrees [13]. Moreover, the admittance matrix of the power system has a special structure, in which the diagonal elements are the sum of the nondiagonal elements in the corresponding rows, with the contribution of shunt elements as well: 8 if j 6¼ i > < 2 yij n X Yij 5 y 1 (2.3) yik if j 5 i > : ii k51; k 6¼ i

Note that in this chapter, the “shunt elements” yii denote not only the contribution of shunt capacitors but also the line-charging susceptance and the equivalent admittance of transformers and phase shifters (Section 2.2.3 of this section provides the details of the consideration of transformers and phase shifters). Considering these facts, the following linear approximations are applied to (2.1) to decouple the voltage magnitudes and the phase angles: Pi 5

n X

Gij Vi Vj cos θij 1

j51

n X Bij Vi Vj sin θij j51

n X 5 gii Vi2 1 gij Vi Vi 2 Vj cos θij 2 bij Vi Vj sin θij j51; j6¼1 n X

gii Vi 1

j51; j6¼1

5 Vi

n X

gij ðVi 2 Vj Þ 2

n X

gij 1

n X

!

ð2 gij ÞVj

j51; j6¼1 n n X X 5 Gij Vj 2 B0ij θj j51 j51 j51

bij ðθi 2 θj Þ

j51; j6¼1

2 θi

n X

bij 1

j51; j6¼1

n X

! ð2 bij Þθj

j51; j6¼1

(2.4)

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Similarly, Eq. (2.2) for reactive power can be approximated as follows: Qi 5 2

n X j51

Bij Vj 2

n X

Gij θj

(2.5)

j51

Note that G0ij Gij is assumed in (2.5) because the shunt conductance is negligible compared with the shunt susceptance. The approximations made in (2.4) and (2.5) are not the same as those made in other linear power flow models. Consider the following three alternative approximations in the expression of branch MW flow: corresponding to the DCPF, the approximation made by [12], and the proposed approximation, respectively, • Approximation 1: gij Vi(Vi 2 Vj cos θij) 0 • Approximation 2: gij Vi ðVi 2 Vj cos θij Þ gij ðVi2 2 Vj2 Þ • Approximation 3: gij Vi(Vi 2 Vj cos θij) gij (Vi 2 Vj) The common assumption is that cos θij 1. However, the methods dealing with voltage magnitude are not identical. In the DC model the voltage differences across branches are neglected. However, these are considered in Approximation 3, which is the key approximation step in the derivation of the proposed DLPF model. Although Ref. [12] takes voltage differences into account by using Approximation 2, the result is not satisfactory. Fig. 2.1 illustrates the effects of the three approximations on branch MW flow in two typical scenarios (a high and a low r/x ratio). It is evident that the approximation proposed in this chapter (Approximation 3) performs the best in both cases. In fact, a theoretical mechanism exists underlying the proposed approximation: gij Vi Vi 2 Vj cos θij gij Vi Vi 2 Vj 5 gij 1 1 ΔVj ΔVi 2 ΔVj (2.6) gij ΔVi 2 ΔVj 5 gij 1 1 ΔVi 2 1 2 ΔVj 5 gij Vi 2 Vj Note that Vi is decomposed into (1 1 ΔVi), where ΔVi is typically one order of magnitude smaller than Vi. In the second step of (2.6), ΔVi2 and ΔViΔVj can be neglected without excessive error because they are two orders of magnitude smaller than Vi and Vj.

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

29

(A) Approximation 1 Approximation 2 Approximation 3 No approximation

Branch MW flow (p.u.)

2

1

0

–1

–2 –10

(B)

10

Approximation 1 Approximation 2 Approximation 3 No approximation

2 Branch MW flow (p.u.)

–5 0 5 Phase angle difference (degree)

1

0

–1 –10

–5

0

5

10

Phase angle difference (degree)

Figure 2.1 An illustration of the accuracy of different approximations with Vi 5 1.05 p.u. and Vj 5 0.98 p.u. (A) Case I: r/x 5 1/4 with r 5 0.02 p.u. and x 5 0.08 p.u. and (B) case II: r/x 5 4/1 with r 5 0.08 p.u. and x 5 0.02 p.u.

A linear expression for the average branch MW flow Pij can also be easily obtained from the first step of (2.4): Pij 5 gij Vi 2 Vj 2 bij θi 2 θj (2.7) Because the line loss is ignored for linearity, we have Pij 5 2 Pji, where Pij denotes the power flow from bus i to bus j. It should be noted that Eq. (2.7) has to be modified for lines with transformers and phase shifters. The details are also provided in Section 2.2.3.

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2.2.2 Matrix formulation of the decoupled linearized power flow model Equations (2.4) and (2.5) represent the basic formulation of the DLPF model. The matrix form is as follows: 0 P B G θ 52 (2.8) Q G B V It is evident that both θ and V consist of three subvectors, corresponding to the V θ, PV, and PQ buses. Without loss of generality, we arrange these buses in the following sequence: V θ, PV, and PQ. θ 5 ½θTR ; θTS ; θTℒ T V 5 ½V TR ; V TS ; V Tℒ T

(2.9)

The admittance matrix Y can also be partitioned in the same manner: 2 3 Y RR Y RS Y Rℒ Y 5 4 Y SR Y SS Y Sℒ 5 (2.10) Y ℒR Y ℒS Y ℒℒ With the aid of the known subvectors θR , V R , and V S , we transform (2.8) into the following equation: H N θ~ P~ (2.11) ~ 5 M L Q V~ where

2 32 0 32 3 PS B SR 2 GSR 2 GSS θR P~ 4 54 B0 ℒR 2 GℒR 2 GℒS 54 V R 5 ~ 5 Pℒ Q Qℒ B0 ℒR 2 GℒR 2 GℒS VS 2 0 3 B SS 2 B0 Sℒ 2 GSℒ H N 5 2 4 B0 ℒS 2 B0 ℒℒ 2 Gℒℒ 5 M L GℒS 2 Gℒℒ 2 Bℒℒ T θ~ 5 θTS ; θTℒ V~ 5 V ℒ

(2.12)

(2.13)

(2.14)

Performing elementary row operations on both sides of (2.11) leads to the following equations:

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

and

~ H 2 NL21 M P~ 2 NL 21 Q 5 ~ M Q

H P~ 21 ~ 5 0 ~ Q 2 MH P

0 L

N L 2 MH 21 N

θ~ V~

θ~ V~

31

(2.15) (2.16)

Combining the first part of (2.15) and the second part of (2.16) into one equation leads to the decoupling of voltage magnitudes and phase angles: ~ ~ 0 θ~ H P~ 2 NL 21 Q (2.17) ~ 2 MH 21 P~ 5 0 L~ Q V~ where ~ 5 H 2 NL21 M H

(2.18)

L~ 5 L 2 MH 21 N

(2.19)

According to (2.17), the expressions for calculating the unknown V~ ~ are as follows: and θ~ with respect to the known bus injections P~ and Q ~ ~ 21 P~ 2 H ~ 21 NL21 Q θ~ 5 H

(2.20)

21 ~ 2 L~ 21 MH 21 P~ V~ 5 L~ Q

(2.21)

Note that the decoupling of the voltage magnitudes and phase angles is not achieved by simply ignoring the relatively small conductance matrix G. On the contrary, the coupling between the active and reactive powers is considered in Eqs. (2.20) and (2.21).

2.2.3 Transformers and phase shifters This section describes the changes to the admittance matrix and MW flow approximations needed for branches with transformers or phase shifters. 2.2.3.1 Contributions to the admittance matrix According to the formation of nodal admittance matrix Y [14], the contribution of the branch with a phase-shifting transformer (Fig. 2.2, t for tap ratio and θs for phase-shifting angle) to the nodal admittance matrix is

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t∠θs:1

yij

i

J

Figure 2.2 A general diagram for a branch with phase-shifting transformers.

ΔY 5 Cyij C

T

(2.22)

where C is an n 3 1 vector of all zeros except Ci 5 1=te2jθs and Cj 5 21. C is the conjugate of C. ΔY can be split into two parts: ΔY 5 ΔY 1 1 ΔY 2

(2.23)

where ΔY1 has four nonzero elements: yij te2jθs yij ΔY 1; jj 5 2 ΔY 1;ji 5 jθs te ΔY 1;ii 5 2 ΔY 1;ij 5

while ΔY2 is a diagonal matrix with only two nonzero elements: yij yij ΔY 2;ii 5 2 2 2jθs t te yij ΔY 2; jj 5 yij 5 jθs te

(2.24)

(2.25)

In fact, ΔY1 represents the contribution of equivalent line admittance to Y. ΔY2 can be regarded as the equivalent shunt elements of phaseshifting transformers and is considered in yii as stated in Section 2.2.1. 2.2.3.2 Influence on the branch MW flow For the branch shown in Fig. 2.2, the branch complex power has the following relationship with bus voltage and phase angle: 2 yij yij 3 2 2jθs 2jθ 2 te 7 V e jθi 6 t S ij Vi e i 7 i jθ 6 5 (2.26) 5 Vj e j Sji Vj e2jθj 4 2 yij yij jθs te The branch MW flow Pij is the real part of Sij:

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

Pij

!! y y ij ij 5 Re Vi e2jθi 2 Vi e jθi 2 2jθs Vj e jθj t te ! yij 2 yij 5 Re 2 Vi 2 Vi Vj e2jðθi 2θj 2θs Þ t t ! gij Vi 2 Vi Vj cos ðθi 2 θj 2 θs Þ 5 Vi t t bij 2 Vi Vj sinðθi 2 θj 2 θs Þ t ! gij Vi bij 2 Vj 2 ðθi 2 θj 2 θs Þ t t t

33

(2.27)

The approximation made in the last step of (2.27) is similar to that in (2.4), and this provides a linear expression for branch MW flow with transformers or phase shifters. Because the line loss is ignored for linearity, we have Pij 5 2 Pji. It should be noted that (2.27) and (2.7) have the same pattern. Eq. (2.7) can be seen as a special case of (2.27) where t 5 1 and θs 5 0.

2.2.4 Derivation and justification of the fast decoupled linearized power flow A fast computation speed is one of the key advantages of DCPF models. The classical DCPF model achieves this speed because the admittance matrix is formulated using 1/xij-directly from the system information. For the DLPF model proposed in Section 2.2, its solution requires the factori~ and L. ~ Although H and L are sparse zation of four matrices: H, L, H, ~ ~ matrices, H and L maybe nonsparse matrices, for which factorization is ~ and L~ computationally intractable. This section, however, shows that H 0 0 ~ ~ can be approximated by the sparse matrices H and L , which have the same structure as H and L: 8 1 > 2 if j 6¼ i > > < xij H~ 0 ij (2.28) n X 1 > > if j 5 i > : x k 5 1; k 6¼ i ik

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8 1 > 2 > > < xij L~0 ij X n 1 > > > : x k51

if j 6¼ i (2.29) if j 5 i

ik

Expressions (2.20) and (2.21) for bus voltage magnitude and phase angle are thus transformed into ~ ~ 021 P~ 2 H ~ 021 NL21 Q θ~ 5 H

(2.30)

~ 2 L~ 021 MH 21 P~ V~ 5 L~ 021 Q

(2.31)

The approximation always holds for a radial system or a meshed system with a constant r/x ratio, as justified by the theoretical derivation presented in this section. For more complex cases the approximation error is mild, as can be demonstrated by a numerical example. 2.2.4.1 An illustrative example Let us begin with a 2-bus system (Fig. 2.3) for illustration. In this case, Eq. (2.13) becomes H N 2 b12 g12 (2.32) 5 2 g12 2 b12 2 b22 M L ~ and L~ can be calculated as follows: and H ~ H

Figure 2.3 A 2-bus system.

5 H 2 NL21 M 5 2 b12 2 g12 ðb12 1b22 Þ21 g12 C 2 b12 2 g12 b21 12 g12 1 5 x12

(2.33)

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

L~

5 L 2 MH 21 N 5 2 b12 2 b22 2 g12 b21 12 g12 1 1 5 1 x12 x22

35

(2.34)

It can be seen from (2.33) and (2.34) that the updated elements are ~ 0 and L~ 0 . This surprising exactly identical to the diagonal elements of H feature can be extended to more general cases, as demonstrated next. 2.2.4.2 Theoretical derivation ~ and L~ are too complicated to explicitly For a general power system, H derive. However, for a radial system or a constant-r/x-ratio system with~ whereas H ~ 0 is a out PV buses, it can be proven that L~ 0 is identical to L, ~ good approximation of H. Let ySℒ 5 gSℒ 1 jbSℒ denote the primitive admittance matrix of a system in which the V θ buses are grounded and all shunt susceptances are neglected. C represents the node-to-branch incidence matrix. The shunt susceptances are represented by the (n 2 1) 3 (n 2 1) diagonal matrix diag(b0). For a meshed system with a constant r/x ratio, let us assume that for any branch l, rl/xl 5 α. This leads to rl 2 xl gl 5 2 2 5 2 αbl xl rl 1 x2l ! (2.35) 2 r 2 xl 1 ð1 1 α2 Þbl 5 1 1 l2 2 5 2 xl xl rl 1 x2l and thus ~ H

5 H 2 NL21 M

5 2 CbSℒ CT 2 CgSℒ CT U 21 CgSℒ CT CbSℒ CT 1diagðb0 Þ 2 CbSℒ CT 2 α2 CbSℒ CT U 21 CbSℒ CT CbSℒ CT 5 2 ð1 1 α2 ÞCbSℒ CT ~0 5H

(2.36)

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L~ 5 L 2 MH 21 N 5 2 CbSℒ CT 2 diagðb0 Þ 21 2 CgSℒ CT CbSℒ CT CgSℒ CT 5 2 1 1 α2 CbSℒ CT 2 diagðb0 Þ 5 L~ 0

(2.37)

For a radial system (usually a distribution system), C is a nonsingular ~ 0 H, ~ in the same matrix. It can also be easily found that L~ 0 5 L~ and H manner as for meshed systems. 2.2.4.3 A numerical example ~ and L~ also hold for more general meshed The approximations of H systems. Let us demonstrate this claim using a numerical example of a Grainger and Stevenson 4-bus system [14], which is shown in Fig. 2.4. Using Eq. (2.13), the matrices H, N, M, and L are as follows: 3 2 40:97 2 25:85 2 15:12 2 5:17 2 3:02 7 6 2 25:85 44:93 0 8:99 0 7 6 H N 56 2 15:12 0 40:97 0 8:19 7 7 6 M L 5 4 5:17 2 8:99 0 44:84 0 3:02 0 2 8:19 0 40:86 ~ and L~ are derived from Eqs. (2.18) and (2.19): The matrices H 2 3 41:79 2 26:88 2 15:72 5; L~ 5 46:63 0 ~ 5 4 2 26:88 46:73 H 0 0 42:50 2 15:72 0 42:61

Figure 2.4 Grainger and Stevenson 4-bus system.

37

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

~ 0 and L~ 0 are defined by (2.28) and (2.29): The approximate matrices H 2 3 42:60 2 26:88 2 15:72 46:63 0 0 0 4 5 ~ ~ 0 ; L 5 H 5 2 26:88 46:72 0 42:50 2 15:72 0 42:61 ~ 0 is It can be easily seen that L~ 0 and L~ are identical, whereas H ~ extremely close to H.

2.3 Linear three-phase power flow models of the unbalanced distribution systems The basic three-phase power flow equations can be formulated by taking the mutual inductance and interphase capacitance among different phases into consideration. It is shown as follows: Piα 5 Viα Qαi 5 Viα

N X X

αβ αβ Vjβ Gijαβ cos θαβ ij 1 Bij sin θij

(2.38)

αβ αβ Vjβ Gijαβ sin θαβ 2 B cos θ ij ij ij

(2.39)

j51 β 5 a;b;c N X X j51 β 5 a;b;c

where N denotes the number of nodes of active distribution network; i and j are node indexes; α and β are phase indexes; Piα and Qαi denote the active and reactive injections of phase α and node i; Viα denotes the voltage magnitude and phase angle of phase α; and node i; Gijαβ , Bαβ ij , and αβ θij denote the conductance, susceptance, and angle difference between phases α and β, and nodes i and j, respectively. Three linear approximations are applied to linearize the relationship among active and reactive power, voltage magnitude, and phase angle. Approximation 1. Since the angle difference θαα of each branch in ij active distribution network is very small, the trigonometric sines and αβ αα αα cosines of it can be approximated as cos θαα ij 1 and sin θij θij . θij , the angle difference of different phases, is near 2/3π or 22/3π, the trigonometric functions of which can also be linearized similarly near 2/3π or 22/3π.

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Approximation 2. The production of two voltages is linearized as follows by neglecting the second-order small quantities. Vk Vk 2 Vj 5 Vk 2 Vj 1 ð12Vk Þ2 2 ð1 2 Vk Þ 1 2 Vj Vk 2 Vj (2.40) Approximation 3. The ZIP model of the loads describing the load variation with respect to the voltage is (taking active power as example): P (V ) 5 (FZV 2 1 FIV 1 FP)P0 where, FZ, FI, and FP are the constant coefficients corresponding to impedance, current, and power contributions, respectively; P0 denotes the active power of the load under normal voltage. The second-order small quantities ΔV 2 is neglected to obtain the approximated IP model of the loads. PðV Þ 5 FZ V 2 1 FI V 1 FP P0 5 FZ ð11ΔV Þ2 1 FI ð1 1 ΔV Þ 1 FP P0 ðFP 2 FZ ÞP0 1 ½ð2FZ 1 FI ÞP0 V (2.41) The first two approximations linearize the nonlinear terms and trigonometric functions on the right side of Eq. (2.1) by first-order approximation. The third approximation transforms the ZIP model into PI model of the loads in the left side of Eq. (2.1) by removing the production of two voltages. Thus the relationship between voltages and injections described in Eq. (2.1) is fully linearized. Eq. (2.1) can be viewed as the summation of two parts: A 5 Viα B 5 Viα

n X c X

Vjβ Gijαβ cos θαβ ij

(2.42)

αβ Vjβ Bαβ ij sin θij

(2.43)

j51 β5α n X c X j51 β5α

To illustrate out derivation process the first part of Eq. (2.1), which is denoted as A, is provided to demonstrate how the linear relationship is derived. The rest of Eq. (2.1) can be linearized accordingly. According to Approximation 1, cosθαβ ij can be linearized as follows:

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

A 5 Viα

39

n X c X Vjβ Gijαβ cos θαβ ij

n X

j51 β5α

Vjα Gijαα

j51

1

n X X

Vjβ Gijαβ

j51 β6¼α

! pﬃﬃﬃ 1 3 αβ 2 1 θ 2 2 ij

(2.44)

By disassembling the second sum term and according to Approximation 2, Eq. (2.7) can be further approximated: n n X c 3X 1X Vjα Gijαα 2 Vjβ Gijαβ 2 j51 2 j51 β5α pﬃﬃﬃ n pﬃﬃﬃ n X 3 3 X ααlag ααlag ααlead ααlead 1 Gij θij 2 G θij 2 j51 2 j51 ij

A

(2.45)

where αlead and αlag denote the phases that lead or lag to phase α, respectively. Then the original three-phase power equations can be neatly presented in a matrix form: 2 A 3 2 J AA J AB J AC J AA J AB J AC 32 A 3 V P PV PV PV Pθ Pθ Pθ BA BB BC BA BB BC 76 B 7 6 PB 7 6 V 7 7 6 JPV JPV JPV JPθ JPθ JPθ 7 6 76 6 C 7 6 CA CB CC CA CB CC 76 C 7 J J J J J J 6P 7 6 6 PV PV PV Pθ Pθ Pθ 7 V 7 756 7 6 6 (2.46) 6 AA AB AC AA AB AC 76 A 7 6 QA 7 6 J θ 7 6 QV JQV JQV JQθ JQθ JQθ 7 7 6 6 7 6 B 7 6 BA BB BC BA BB BC 76 B 7 4 Q 5 4 JQV JQV JQV JQθ JQθ JQθ 54 θ 5 QC

CA JQV

CB JQV

CC JQV

CA JQθ

CB JQθ

CC JQθ

θC

where Pα,Qα, Vα, and θα denote the N-dimensional vectors for active αβ power, reactive power, voltage magnitude, and angle of phase α. JðPQÞðV θÞ is the N 3 N dimensional matrix describing the coupling relationship between active (or reactive) power and voltage magnitude (or angle) of phases α and β. This matrix uncovers how the injections of the nodes affect the nodal voltages. αβ The matrix JðPQÞðV admittance θÞ can be calculated using pthree-phase ﬃﬃﬃ AB AA matrix. For example, JPV 5 2 1=2 G AB 1 3=2 BAB , JPV 5 GAA αβ αβ where G and B denote the conductance and susceptance matrices of phases α and β. If the ZIP model of the loads is considered, the vectors αα αα Pα, Qα and matrices JPV , JQV should be revised accordingly by Eq. (2.4). α Finally, the vectors V and θα can be solved by Gauss elimination using Eq. (2.9).

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2.4 Case study 2.4.1 Meshed transmission systems Table 2.1 compares the performances of the different models for various kinds of transmission systems. The model proposed in Ref. [11] is excluded from this comparison because it was designed only for radial networks. Fig. 2.5 visualizes the solutions for the IEEE 118-bus system in terms of voltage magnitude and branch MW flow as an example for illustration. A comparison of the computation times for the large-scale system is also presented in Table 2.2. From Table 2.1 and Fig. 2.5, it is evident that the results of the DLPF and FDLPF models are the most accurate in terms of voltage magnitude among various cases of large-scale transmission systems. The accuracy of the DLPF model in terms of MW flow is also higher than that of the classical DCPF model. Although the FDLPF model performs a little worse than the DCPF model with respect to MW flow in some cases, the errors are acceptable because the main advantage of FDLPF is its accurate evaluation of bus voltage magnitudes. Moreover, Table 2.2 suggests that the FDLPF model significantly improved computational efficiency by sacrificing only limited precision in terms of MW flow while maintaining the voltage magnitude accuracy of the DLPF model. The FDLPF model is therefore suitable for applications that require a large number of repeated load flow calculations. A comparison of the computation times for the large-scale system is presented in Table 2.2. Comparing it with Table 2.1 suggests that the Table 2.1 Errors of different linear power flow models for transmission systems. a Test case εDCPF εDLPF εFDLPF εDCPF εDLPF εFDLPF εmd2½12 εmd2½12 V V V P P P V P

IEEE 30-bus IEEE 57-bus IEEE 118-bus IEEE 300-bus Polish 2383-bus Polish 3012-bus Pegase 2869-bus Pegase 9241-bus

0.018 0.024 0.023 0.025 0.008 0.089 0.034 0.032

0.011 0.012 0.003 0.016 0.005 0.005 0.008 0.014

0.001 0.007 0.001 0.014 0.003 0.002 0.008 0.007

0.001 0.009 0.001 0.014 0.003 0.003 0.008 0.007

0.004 0.015 0.035 0.105 0.026 0.022 0.119 0.181

0.023 0.062 0.090 0.148 0.047 0.055 0.157 0.208

DCPF, DC power flow. a The bus voltage profile is assumed to be flat at 1.0 p.u. in the DCPF model.

0.002 0.010 0.034 0.102 0.022 0.021 0.115 0.178

0.007 0.017 0.042 0.105 0.038 0.034 0.119 0.192

41

Voltage magnitude

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

1.06

ACPF DCPF md-[12] DLPF FDLPF

1.04 1.02 1.00 0.98 0.96 0.94 0

10

30

20

40

50

400 MW flow

60

70

80

90

100

110

120

Bus number

200 0 –200 –400 –600 0

20

40

60

80

100

120

140

160

180

Branch number

Figure 2.5 Voltage magnitudes and branch MW flows for the IEEE 118-bus system. Table 2.2 Computational efficiencies of different linear power flow models for largescale systems. Test case t ACP Fa t DCP F t md-[12] t DLP F t F DLP F

Polish 2383 Polish 3012 Pegase 2869 Pegase 9241 a

0.24 0.27 0.30 4.03

0.01 0.02 0.02 0.08

1.75 5.60 1.90 43.0

1.60 9.42 1.71 110

0.01 0.02 0.02 0.09

The unit of computational time is second.

FDLPF model achieves significantly improved computational efficiency by sacrificing only limited precision in terms of MW flow while maintaining the voltage magnitude accuracy of the DLPF model. The FDLPF model is therefore suitable for applications that require a large number of repeated load flow calculations.

2.4.2 Balanced distribution systems In this section, several cases are analyzed to demonstrate the accuracy and robustness of the DLPF/FDLPF approach and the fast computation speed of the FDLPF model [15]. Various types of power systems are studied. The analyses were performed in MATLAB with the aid of MATPOWER 5.1 [16]. The simulation platform was a ThinkPad workstation with an Intel i7 [email protected] GHz and 8 GB of RAM. Three important results were obtained for comparison: bus voltage magnitudes, branch MW flows, and computation times. With the results of the AC

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Error of voltage magnitude

0.20

DCPF md-[12] md-[11] DLPF FDLPF

0.15

0.10

0.05

0.00 1

2

3

4

5

Load scale (uniform overload)

Figure 2.6 33-Node ill-conditioned system (uniform overload).

0.12 DCPF md-[12] md-[11] DLPF FDLPF

Error of voltage magnitude

0.10

0.08

0.06

0.04

0.02

0.00 0

2

4

6

8

10

12

Load scale (lumped overload)

Figure 2.7 33-Node ill-conditioned system (lumped overload).

power flow model as the benchmark, the error for each model (md) was calculated as follows: εmd V

n 1X V md 2 V AC ðp:u:Þ 5 i i n i51

(2.47)

n 1X P md 2 P AC ðp:u:Þ i i m i51

(2.48)

εmd P 5

To avoid random timing errors, each MATLAB program was run 10 times, and the average computation time tmd was considered. Table 2.3 compares the performances of different models on the IEEE 33- and 123-node test feeders. The solutions were obtained using the DCPF model, the method proposed in Ref. [12] (md-[12]), the

Table 2.3 Errors of different linear power flow models for distribution systems. a Test case εDLPF εFDLPF εDCPF εmd2½12 εmd2½11 V V V V V

εDCPF V

εPmd2½12

b εmd2½11 P

εDLPF P

εFDLPF P

IEEE 33-node IEEE 123-node

0.026 0.008

0.847 0.291

0.979 0.173

0.026 0.008

0.026 0.008

0.052 0.051

0.027 0.027

0.004 0.004

0.004 0.003

0.004 0.003

DCPF, DC power flow. a The bus voltage profile is assumed to be flat at 1.0 p.u. in the DCPF model. b The linear expression for branch MW flow is not provided in Ref. [11]. Because the phase angle is accurate, according to the results reported in Ref. [11], we assume the same expression as that for the DCPF model, namely, ’ branches (i, j), P(i,j) 5 (θi 2 θj)/xij.

Voltage magnitude

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1.00

ACPF DCPF md-[12] md-[11] DLPF FDLPF

0.98 0.96 0.94 0.92 0.90 0

5

10

15

20

25

30

35

Bus number MW flow

4 2 0 –2

0

5

10

15

20

25

30

35

40

Branch number

Figure 2.8 Voltage magnitudes and branch MW flows for the IEEE 33-node test feeder.

method proposed in Ref. [11] (md-[11]) and the DLPF/FDLPF approach proposed in this chapter. Fig. 2.8 visualizes the solutions for the 33-node system in terms of voltage magnitudes and branch MW flows. It should be noted that an average error of 0.05 p.u. in voltage magnitude is beyond tolerance, given that the operational voltage limit is typically between 0.95 and 1.05 p.u. From Table 2.3 and Fig. 2.8, it is obvious that the DLPF and the FDLPF models perform the best not only in voltage magnitudes but also in active power flows as well. Note that md-[11] exhibits the same accuracy in bus voltage magnitudes as the DLPF/FDLPF model but a poor performance in branch MW flows. This is not due to the flaws in the model itself but our choice to use DCPF branch flow expression, as no linear expression for branch MW flow is provided in Ref. [11]. In fact, the proposed model is a generalization of md-[11] from radial distribution systems to meshed networks with PV buses. A detailed discussion is provided in the Appendix.

2.4.3 Unbalanced distribution systems The proposed method is applied to the IEEE 37-node test feeder with unbalanced loads [17]. For simplicity the voltage dependencies of all loads are assumed to be identical (FZ 5 0.7, FI 5 0.2, and FP 5 0.1) for both active and reactive power. Nodes 21 and 27 are specified as PV nodes

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

45

1.03 Phase A-Real Phase B-Real Phase C-Real Phase A-LPF/W Phase B-LPF/W Phase C-LPF/W Phase A-LPF/O Phase B-LPF/O Phase C-LPF/O

1.025

Voltage magnitude

1.02 1.015 1.01 1.005 1 0.995 0.99 0.985

5

10

15 20 Node number

25

30

35

Figure 2.9 Voltage magnitudes of all nodes calculated by AC power flow and the proposed method with (W) and without (O) PV nodes.

with the same voltage magnitudes as the original system in the case with PV nodes. The voltages obtained by the backwardforward sweep algorithm are used as real values. The error of the proposed method is defined as the absolute difference between the calculated value and real value. Fig. 2.9 shows the real and calculated voltage magnitudes with (W) and without (O) PV nodes. The dotted line (calculated) is close to or even overlapped with the solid line (real value). Without PV nodes, the maximum and average errors are 1.49 3 1023 and 3.70 3 1024. Such error meets the needs of many practical applications. Fig. 2.10 presents the errors of the three-phase voltage magnitudes calculated by our proposed method with PV nodes. The maximum and average errors are 2.91 3 1024 and 8.79 3 1025, respectively, which are comparable with the method proposed in Ref. [18]. The introduction of PV nodes can also largely reduce the error. In rural distribution networks, voltage percent variations maybe higher than 5%. To illustrate that our proposed method can also be used indeed to detect lager voltage variations, the original load is expanded by 150%, 200%, and 300%. The average error of the calculated voltage magnitudes are 1.00 3 1023, 1.40 3 1023, and 2.80 3 1023, respectively. The calculated voltage magnitudes of all nodes when the loads are tripled are shown in Fig. 2.11. It can be seen that even though the node voltage can be low as 0.91, the calculated voltages can obtain

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–4 3 ⫻10

Phase A Phase B

2.5

Phase C Average

Error

2

1.5

1

0.5

0

5

10

15

20 Node number

25

30

35

Figure 2.10 Error of the three-phase voltage magnitudes between AC power flow and the proposed method with PV nodes. 1.04 Phase A-Real Phase B-Real Phase C-Real Phase A-LPF Phase B-LPF Phase C-LPF

1.02

Voltage magnitude

1 0.98

0.96

0.94

0.92

0.9 0

5

10

15

20

25

30

35

Node number

Figure 2.11 Voltage magnitudes of all nodes calculated by AC power flow and the proposed method with loads tripled.

relative higher accuracy. In addition, the reactive power injections of PV nodes (nodes 21 and 27) have been calculated. The relative errors are 0.013% and 0.014%, respectively.

Decoupled linear AC power flow models with accurate estimation of voltage magnitude

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2.5 Conclusion In this chapter, we propose a state-independent, V 2 θ DLPF model that is distinguished by high accuracy in voltage magnitude. An in-depth analysis of the matrices used for calculation is presented, giving rise to a fast version of the DLPF model, namely, the FDLPF model. The approximation that is applied to obtain the FDLPF model from the DLPF model is justified by theoretical derivation and numerical examples. An analysis of various cases, including radial distribution systems with high r/x ratios, large-scale transmission systems, and ill-conditioned systems, proves the accuracy and robustness of the two proposed power flow models. In large-scale cases the FDLPF model is proven to be much faster than the DLPF model, thereby demonstrating the effectiveness of the approximation technique. We envision that the proposed models can serve as useful tools in power system planning and operation. Specifically, the DLPF model has the potential to be employed in optimization problem in place of the AC power flow equations, which may allow nonlinear, nonconvex problems to be transformed into linear programming problems. In addition, the FDLPF model is characterized by high computational efficiency and has the potential to be applied in contingency analysis and probabilistic load flow problems. By utilizing the FDLPF model, voltage magnitude and reactive power can be considered as in the AC power flow model with a computational efficiency that is as high as that of the classical DCPF model.

References [1] M.K. Enns, J.J. Quada, B. Sackett, Fast linear contingency analysis, IEEE Trans. Power Apparatus Syst. PAS-101 (4) (1982) 783791. Available from: https://doi.org/ 10.1109/TPAS.1982.317142. [2] R. Billinton, R.N. Allan, Reliability Evaluation of Engineering Systems, 1992. [3] Z. Hu, X. Wang, A probabilistic load flow method considering branch outages, IEEE Trans. Power Syst. 21 (2) (2006) 507514. Available from: https://doi.org/10.1109/ TPWRS.2006.873118. [4] F. Dorfler, F. Bullo, Novel insights into lossless ac and dc power flow, Power and Energy Society General Meeting, IEEE, 2013. 15. Available from: https://doi.org/ 10.1109/PESMG.2013.6672260. [5] F. Li, R. Bo, DCOPF-based LMP simulation: algorithm, comparison with ACOPF, and sensitivity, IEEE Trans. Power Syst. 22 (4) (2007) 14751485. Available from: https://doi.org/10.1109/TPWRS.2007.907924.

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[6] F. Lee, J. Huang, R. Adapa, Multi-area unit commitment via sequential method and a DC power flow network model, IEEE Trans. Power Syst. 9 (1) (1994) 279287. Available from: https://doi.org/10.1109/59.317600. [7] B. Stott, J. Jardim, O. Alsac, DC power flow revisited, IEEE Trans. Power Syst. 24 (3) (2009) 12901300. Available from: https://doi.org/10.1109/TPWRS.2009. 2021235. [8] V. Sarkar, S. Khaparde, A comprehensive assessment of the evolution of financial transmission rights, IEEE Trans. Power Syst. 23 (4) (2008) 17831795. Available from: https://doi.org/10.1109/TPWRS.2008.2002182. [9] PJM Training Materials, 2015. ,http://pjm.com/.. [10] A. Leite da Silva, V. Arienti, Probabilistic load flow by a multilinear simulation algorithm, generation, transmission and distribution, IEE Proc. C 137 (4) (1990) 276282. [11] S. Bolognani, S. Zampieri, On the existence and linear approximation of the power flow solution in power distribution networks, IEEE Trans. Power Syst. 31 (1) (2016) 163172. Available from: https://doi.org/10.1109/TPWRS.2015.2395452. [12] S. Fatemi, S. Abedi, G. Gharehpetian, S. Hosseinian, M. Abedi, Introducing a novel DC power flow method with reactive power considerations, IEEE Trans. Power Syst. 30 (6) (2015) 30123023. Available from: https://doi.org/10.1109/TPWRS. 2014.2368572. [13] D. Van Hertem, J. Verboomen, K. Purchala, R. Belmans, W. Kling, Usefulness of DC power flow for active power flow analysis with flow controlling devices, in: The Eighth IEE International Conference on AC and DC Power Transmission, 2006, 2006, pp. 5862. Available from: https://doi.org/10.1049/cp:20060013. [14] J.J. Grainger, W.D. Stevenson, Power Systems Analysis, McGraw-Hill, 1994. [15] Source Code for the Linear Power Flow Models, GitHub, 2017. ,https://github. com/Jingwei-THU/linear-power-flow.. [16] R. Zimmerman, C. Murillo-Sanchez, R. Thomas, MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education, IEEE Trans. Power Syst. 26 (1) (2011) 1219. Available from: https://doi.org/ 10.1109/TPWRS.2010.2051168. [17] W.H. Kersting, Radial distribution test feeders, IEEE Trans. Power Syst. 6 (3) (1991) 975985. [18] A. Garces, A linear three-phase load flow for power distribution systems, IEEE Trans. Power Syst. 31 (1) (2015) 827828.

CHAPTER THREE

Renewable energy integration and system operation challenge: control and optimization of millions of devices Ying Xu, Wei Sun and Zhihua Qu Department of Electrical and Computer Engineering, University of Central Florida, FL, United States

Contents 3.1 Introduction 3.2 Distribution system model with high penetration of renewables 3.2.1 Distribution network model 3.2.2 An explicit branch model of distribution network 3.2.3 Dynamic distributed generation model 3.3 Autonomous distributed voltage control 3.3.1 Distributed subgradient algorithm 3.3.2 Distributed subgradient voltage control 3.3.3 Reactive power control and power factor control 3.4 Hierarchical multiagent control of large-scale distribution system 3.4.1 Virtual leader design 3.4.2 Case study 3.5 Islanded microgrid with high penetration of distributed generations 3.6 Grid-edge situational awareness: enhanced observability by voltage inference 3.6.1 Voltage inference method 3.6.2 Network sensitivity 3.6.3 Implementation 3.7 Control-enabled dynamic hosting allowance: P and Q control capacity and impact analysis 3.7.1 Traditional hosting analysis 3.7.2 Dynamic hosting allowance analysis 3.8 Cosimulation of integrated transmission and distribution systems 3.8.1 The framework of cosimulation 3.8.2 Simulation results 3.9 Conclusion References

New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00003-1

© 2021 Elsevier Inc. All rights reserved.

50 52 52 55 56 57 58 59 64 68 69 72 74 79 79 81 82 86 86 87 91 91 92 95 97

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3.1 Introduction As the fast development of the sustainable, clean and renewable energy, the power industry is believed by most to have the potential to meet the rising challenges of climate change, environment deterioration and resource depletion on the way forward of human beings. The modern grid is expected to integrate diverse generation, energy-efficient and clean resources. While the existing power grid infrastructure was designed and built on large-scale power plants further away from loads, operated under centralized control (Supervisory control and data acquisition system/Energy management system/Distribution Management system-SCADA/EMS/DMS) the integration of distributed energy resources (DERs) is greatly limited. In the scenario of a large-scale distribution system with extremely high penetration of renewables, it is challenging to control thousands to millions of “smaller” distributed generating devices. To address this challenge the distributed control and operation with multiagent implementation are promising approaches, and tremendous efforts have been devoted into the development during the past decade, as reviewed in [1]. The distributed algorithms have several advantages over centralized methods. First, in the distributed architecture, each agent requires only local communication and shares limited information with its neighbors; second, the modular design and robust system enable the secure operation against failure of some agents; and third, the privacy of each agent is well protected. However, from the control perspective, a distributed design is not always better than the centralized approach, for example, the convergence time is longer especially when the size of system is large. Also due to business practice, the power industry has to move gradually from existing infrastructure mainly relied on centralized control to a modernized grid, which is flexible to integrated large amount of renewables in a costeffective, secure, resilient, and reliable manner. Therefore, the hierarchical design [2] that combines the advantage of both centralized and distributed methods is more practical. In this chapter, we follow the layered and divisional design principle for large-scale power system operation and control. While the voltage/reactive power (Volt/VAr) control is mainly treated as a local control, the real power control is a system-level control (frequency)

Renewable energy integration and system operation challenge

51

and a supplementary control for local voltage, which will only respond in the case of insufficient reactive power control. The large-scale distribution system is modeled by algebraic network model and dynamic distributed generation (DG) models. For electrical circuit, both nodal injection and branch power flow models are used to model distribution network. A simplified power control model [3] of DG is used, which is simple but good enough to illustrate the design of system operation and control. It is preferable to design the reactive power control of DGs to respond to voltage events at a local level and in an autonomous way. Nevertheless, as a result of local controls, the reactive power at the feeder head (the slack bus) would change accordingly; hence, the power factor at the substation will fluctuate, and sometimes it might not be acceptable to the transmission side. A detailed discussion is presented in this chapter. Moreover, how the presented control applies to distribution system operated in an islanded mode is also provided. Considering the limitations of investment and maintenance, in the distribution systems, the ICT system of real-time measurement and control in distribution system is not applicable. Therefore, the observability of distribution network is usually a problem, which is a major obstacle of distribution system state estimation, control, and optimization. However, based on the properties observed from many distribution network case studies, it is highly possible to infer the system situation using limited information from the measured buses [4]. To this end, we present a sensitivity-based grid-edge situational awareness method. Following that, a network sensitivity-based dynamic hosting allowance (DHA) method is presented for system operation. At last, to validate and demonstrate the feasibility and scalability of proposed algorithms, a cosimulation architecture of integrated T&D system is developed. The main contents of this chapter are listed as follows: • model of distribution system with high penetration renewables • distributed cooperative voltage control • hierarchical multiagent real and reactive power control • control of islanded distribution system with high penetration of renewables • grid-edge situational awareness: enhanced observability by voltage inference • control-enabled DHA: P and Q control capacity and impact analysis • cosimulation of transmission and distribution

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3.2 Distribution system model with high penetration of renewables This section will focus on the detailed mathematical models of voltage and frequency (VF) control in distribution systems with high penetration of renewables. Both nodal and branch power flows will be applied to model distribution network. A DG model [3] is used, which is simple but good enough to illustrate the design from the system control perspective.

3.2.1 Distribution network model A distribution grid can be seen as a tree graph T: 5 ðN; BÞ with node set N and branch set B. The notation of each node in the tree graph corresponds with that of each bus in distribution network. With respect to bus i in a distribution network, as shown in Fig. 3.1, it has a unique parent bus Γi and a set of children buses, denoted by Ci. We denote the set of neighbors of node i by Ni, which contains its parent Γi, and its children set Ci, that is, Ni: 5 {Γi} , Ci. With root (substation) indexed by 0, it is assumed that each line points to node i from Γi and is indexed by a single subscript i for simplicity of denotation, B: 5 N f0g. To simplify the nomenclature of distribution system, the branch impedance and admittance of line i are denoted by Z~ i 5 Ri 1 jXi and yi 5 gi 2 jbi, respectively, the complex current through line i by I~ i 5 Ii +θIi , and complex power at the receiving end (as shown in Fig. 3.1) of line i by S~ si 1 Psi 1 jQsi . Also we denote the complex voltage at bus i by V~ i 5 Vi +δVi , the angle difference by θik 5 θi 2 θk (k A Ni), and complex power injection by S~ i 5 Pi 1 jQi .

Psi+JQsi

Vi

Ii Bus Γi

Bus i Line i Pi+JQi

Figure 3.1 Notations of distribution network.

Children set Ci

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The bus injection power flow and branch power flow equations (Dist-Flow [5]) are two commonly used models in distribution power system analysis. They are essentially equivalent on distribution systems but with different expressions: node injection power flow is a clear expression of power and voltage at all the nodes, while the branch power flow has extra information of current and power flow on each branch. The bus injection power flow equations are written as (3.1a,b) for each node (e.g., the ith node): X Vi Vk ð gk cosθik 1 bk sinθik Þ (3.1a) Pi 5 Vi2 gii 1 kANi

Qi 5 2 Vi2 bii 1

X

Vi Vk ð gk sinθik 2 bk cosθik Þ

(3.1b)

kANi

P P where gii 5 2 kACi ,fig gk , bii 5 2 kACi , fig bk , and the conductance of bus i to ground has been considered as a portion of power injection in S~ i . On the other hand, the branch power flow equations are based on each branch of the system (e.g., the ith line): X Psk 1 PLk 5 Psi 2 Pi ; (3.2a) kACi

X

Qsk 1 QLk 5 Qsi 2 Qi ;

(3.2b)

kACi

vΓi 2 vi 5 2 Ri Psi 1 Xi Qsi 1 jzi j2 ‘i ; vi ‘i 5 Ps2i 1 Q2si ;

(3.2c) (3.2d)

where ‘i 5 Ii2 , vΓi 5 VΓ2i , vi 5 Vi2 , PLk , and QLk represent the power flow at the receiving end of the kth line: PLi 5 Ri ‘i ;

QLi 5 Xi ‘i ;

(3.3)

The physical meaning of branch model is quite straightforward: (3.2a) and (3.2b) represent the power balance of real and reactive power on the ith line, respectively; (2) is the RMS value form of power law equation. To explain (3.2c), we use Fig. 3.2. In Fig. 3.2, assuming that the angle of V~ i is the reference angle and equals to zero, the angle of V~ Γi is θΓi . Define the voltage difference of the ith bus and its parent by δV~ i 5 V~ Γi 2 V~ i , and the real and image parts of δV~ i are δVix and ΔViy , respectively. It follows Ohm’s law on the ith line that

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(A)

VΓi∠θΓi Ri+jXi

(B)

Vi∠0

Ii

θi–1

Psi+jQsi

VΓi∠θΓi

δVi Vi∠0

δVi δVix

Figure 3.2 The phasor notations on the ith line: (A) circuit and (B) phasor diagram.

δV~ i 5 I~ i ðRi 1 jXi Þ

(3.4)

PS 2 jQSi : I~ i 5 i Vi

(3.5)

and

Substituting (3.5) into (3.4) yields δVix 5

PSi Ri 1 QSi Xi ; Vi

δViy 5

PSi Xi 2 QSi Ri : Vi

Using triangle formula in Fig. 3.2B, that is, 2 Vi 1δVix 1 δVi2x 5 VΓ2i ;

(3.6)

hence (3.2c) can be obtained, which is actually the connection between magnitude deviation of V~ i from V~ Γi and the power on the ith line. By substituting (3.3) into (3.2a), we have (3.7) that describes the Kirchhoff balance on ith line: X PLi 5 Psi 1 Pi 2 Psk : (3.7) kACi

Remark 3.2.1. One advantage about branch power flow (3.2a,b,c,d) is that it offers more information on the lines, such as current, real, and reactive power through the lines. Another advantage is that no angle variables are involved and hence no terms with triangular functions, which does not need angle measurements and is easier to relax the model as a convex formulation that can be solved by standard mathematical tools.

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3.2.2 An explicit branch model of distribution network The branch model of the distribution network has become popular and been used in many distributed algorithm implementations in power system. For each agent, it has a nonlinear cascade form with four equations of each agent (e.g., agent i as shown in Fig. 3.1, including branch i and bus i), and four variables each: the square of voltage magnitude (vi), the square of current magnitude (‘i), and the power injection at bus i (Pi and Qi). From the perspective of power system control, the injection (Pi and Qi) is the control and system voltage (and/or loss) is the outcome. Hence, a useful transformation of (3.2a,b,c,d), which focuses on the effects of bus injection on system voltage, is written as following: i X h vk 5 2 2Rkj Pj 1 2Xkj Qj 1 Z 0kj PLj 1 v0 ; (3.8) jAP1k , Tk

where the set P1k is defined as the path from bus 1 to k; Ti is the node set of the subtree under node i; Rkj , Xkj , and Z 0kj and γ j are network parameters (constants), that is, X X Rkj 5 Ri ; Xkj 5 Xi ; γ j 5 Xj =Rj ; iAP1j - P1k

iAP1j - P1k

Z 0kj 5 R0kj 1 X0kj γj ; and R0kj

5

Rkj 2 Rj ; if j , 5 k ; otherwise Rkj ;

X0kj

5

Xkj 2 Xj ; if j , 5 k : otherwise Xkj ;

The explicit form of power flow (3.8) is derived as follows [i.e., from (3.9) to (3.11)]: It follows (3.2a,b,c,d) that vi21 2 vi 5 2PSi Ri 1 2QSi Xi 1 PLi Ri 1 QLi Xi ;

(3.9)

According to Kirchhoff law, on the subtree Ti , we can write the power balance equations as X X PSi 5 Pi 1 Pj 1 PLj ; QSi 5 Qi 1 Qj 1 QLj : (3.10) jATi

jATi

Plugging (3.10) into (3.9), we can then add (3.9) of all iAP1k together which yields

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Ying Xu et al.

v0 2 vk 5

X

"

X

2

ðRi Pj 1 Xi Qj Þ

jATi , fig

iAP1k

#

X

ðPLj Ri 1 QLj Xi 1 ðPLi Ri 1 QLi Xi Þ 0 1 X X X @Pj Ri 1 Qj Xi A 52

12

jATi

j

12

X jAT1

1

X

0

iAP1j - P1k

@PLj

X iAP1minfj21;kg

iAP1j - P1k

Ri 1 QLj

X

(3.11) 1

Xi A

iAP1minfj21;kg

ðRj PLj 1 Xj QLj Þ;

iAP1k

where QLi can be represented by PLi , that is, QLi 5 γi PLi . Hence, (3.11) can be wrapped up as the concise form (3.8). Note 3.2.2. It is important to emphasize that the recursive model (3.8) is the exact model without any approximations. From (3.8), many practical forms of distribution system equation can be easily derived by some assumptions, for example, by neglecting the system loss, (3.8) becomes X 2Rkj Pj 1 2Xkj Qj 1 v0 : (3.12) vk 5 2 jAP1k , Tk

Given the fact that Vk is always close to 1 pu, (3.8) can be written as the following linearized form X Δvk 5 2 Rkj ΔPj 1 Xkj ΔQj ; (3.13) jAP1k , Tk

which has been used in many distributed optimization algorithms in power system and smart grid analysis.

3.2.3 Dynamic distributed generation model The previous analysis does not differentiate the power generation and consumption at each bus. To illustrate the system-level design, we define the generation (DGs) and consumption (loads) on each bus i by Pgi 1 jQgi and Pdi 1 jQdi , respectively, and define net injection by combining DGs generation and loads by

Renewable energy integration and system operation challenge

Pi 1 jQi 5 2 Pgi 2 Pdi 2 j Qgi 2 Qdi :

57

(3.14)

The detailed model of DGs will be provided as follows. For the simplicity of analysis, it is assumed that Pgi and Qgi are determined by decoupled dq control method via phase locked loops (PLL), and assume that inner dynamics of inverter-based DG usually diminish much faster compared to the power outputs, their control model [3] can be written as Pgi 5 Vi Ipi (3.15) Qgi 5 Vi Iqi and

I_ pi 5 upi ; I_ qi 5 uqi

(3.16)

where Ipi and Iqi are the output current in dq-axis, upi and uqi are the real/ reactive power control inputs to be designed, respectively. The active and reactive power of each DG should be well controlled in their operational limits: 0 # Pgi # P gi ;

jQgi j # Qgi ;

where P gi is the maxim available real power output of each DG, and qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2 Qgi 5 Si 2 Pg2i ; (3.17) Qgi is the maxim available reactive power output, and S i is the apparent power capacity of the inverter, which is usually constant. Remark 3.2.3. upi and uqi are the real and reactive power of each DG which can be controlled to achieve specific objectives, hence providing immense potential flexibility to improve overall performance of power systems.

3.3 Autonomous distributed voltage control It is challenging for the existing centralized tools such as EMS/ SCADA systems, to control thousands to millions distributed small-sized

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devices as described by (3.15) in future power grids. Therefore the distributed cooperative control is chosen to handle this operation. This control strategy should be built upon the distributed communication structure, with each node communicating with its neighbors. Using the cooperative control theory [6] and distributed subgradient-based multiagent optimization method [7], the distributed VF control in distribution network is formulated as a multiagent problem.

3.3.1 Distributed subgradient algorithm We use a general form of vector zi (ziARn) to represent the local information of agent i, and Nic is defined as the neighboring set of the ith agent. Note that the ICT structure does not necessarily have the same topology connection as electrical buses in the power distribution network. Use a binary matrix to express the communication topology as 2 3 s11 ðtÞ ? s1n ðtÞ; S54 ^ & ^ 5; (3.18) sn1 ðtÞ ? snn ðtÞ where sij 5 1 if jANic and sij 5 0 if otherwise, for all i; jAN. We start from a general multiagent optimization problem in which agents cooperatively optimize a common additive objective function. Each agent minimizes its own cost function and communicates with other agents, X min fi ðzÞ (3.19) iAN

where fi is the distributed cost function of agent i with a convex form. Denote the optimal valuePof (3.19) by f , and the optimal solution set by Z , which is Z 5 zAj iAN fi ðzÞ 5 f . In this setting the information state zi is an estimate of the optimal solution of (3.19). Each agent updates its estimate first, then exchanges information with others, then updates itself again, and iterates until converging. We propose the close-loop cooperative control law based on the conclusion of [7]: X dij zj ðkÞ 2 β i gi ðkÞ: (3.20) zi ðk 1 1Þ 5 jANic

Renewable energy integration and system operation challenge

59

where k denotes the iteration; the vector gi is a subgradient of control objective function fi of agent i with respect to zi; β i is the step size used by the ith agent; dij denotes the weights of communication topology: wij sij ; (3.21) dij 5 P jAN c wij sij i

wij . 0 are the weights and all wij 5 1 for symmetric systems which is true for all power systems.

3.3.2 Distributed subgradient voltage control In this section the distributed control algorithm is implemented through DG inverters to cooperatively control real and reactive power generation of each DG, so that the system voltage performance can be well maintained in order to satisfy the regulation requirement, as follows: j1 2 Vi j # 0:05:

(3.22)

Therefore the distributed objective function is designed to minimize the voltage deviation at the ith bus, which is as follows: 2 λp 2 λq λv ref fi 5 i Vi 2Vi 1 i Pgi 2Pgrefi 1 i Q2gi ; (3.23) 2 2 2 where the last two terms are designed to minimize the real power curtailref ref ment and the reactive power usage; Vi and Pi are the reference voltage and power, respectively, at the ith bus and can be obtained from OPF or ref other regulatory level design, for example, Vi 5 1 and Pgrefi 5 P gi ; λvi $ 0, p q λi $ 0, and λi $ 0 are the weighting coefficients. We first justify that (3.23) is convex by the following steps: 2 ref λvi =2 Vi 2Vi is proved in Refs. [8,9] to be strictly convex with respect to Qgi in the context of power system operation; by the same procedure, it is straightforward to prove that it is also strictly with q convex p respect to Pgi ; the last two terms λi =2ðPgi 2Pgrefi Þ2 and λi =2 Q2gi are also convex. One way to implement the distributed subgradient control (3.20) for voltage control is the fair utilization ratio method [10], which uses the utilization ratios of real and reactive power at the ith bus as the control variables, that is,

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αpi 9

Pgi ; P gi

(3.24a)

αqi 9

Qg i : Qg i

(3.24b)

Hence, the subgradient of each agent, which is defined by gi in (3.20), can be calculated by taking the derivative of fi with respect to αpi and αqi as follows:

@Pgi @fi p p v @fi @Vi ref 5 λi 1 λi ðPgi 2 Pgi Þ ; (3.25a) gi 5 @αpi @Vi @Pgi @αpi

@Qgi @fi q q v @fi @Vi 5 λi 1 λi Qgi : (3.25b) gi 5 @αqi @Vi @Qgi @αqi It follows (3.23) and (3.24a,b) that @fi ref 5 Vi 2 Vi : @Vi

(3.26)

@Pgi 5 P gi ; @αpi

(3.27a)

@Qi 5 Qgi : @αqi

(3.27b)

and

By taking the derivative of power flow (3.1a) and (3.1b), one can get the following equations for real/reactive power versus bus voltage: X @Qi 5 2 2Vi bii 1 Vk ðgk sinθik 2 bk cosθik Þ @Vi kANi (3.28) Qi 2 Vi2 bii 5 ; Vi and X @Pi 5 2Vi gii 1 Vk ðgk cosθik 1 bk sinθik Þ @Vi kAN i

Pi 2 Vi2 gii : 5 Vi

(3.29)

Renewable energy integration and system operation challenge

61

Without loss of generosity, let us assume @Pi [email protected] 5 @Pgi [email protected] and @Qi [email protected] 5 @Qgi [email protected] . Then following (3.28) and (3.29), the derivatives of bus voltage with respect to real/reactive power generation of DG can be written as @Vi Vi 5 ; @Pgi Pi 1 Vi2 gii

(3.30a)

@Vi Vi 5 : @Qgi Qi 2 Vi2 bii

(3.30b)

Plugging (3.26), (3.27a,b), and (3.30a,b) into (3.25a,b) yields the subgradient formula: Vi p ref p v ref gi 5 P gi λi Vi 2 Vi 1 λi Pgi 2 Pgi (3.31a) Pi 1 Vi2 gii

Vi q ref q v (3.31b) 1 λi Qgi gi 5 Qgi λi Vi 2 Vi Qi 1 Vi2 bii It can be inferred from (3.31a,b) that the subgradient calculation of voltage control for each agent only requires local information. In summary the continuous form of distributed real power control is written as X p α_ pi 5 dij αpj 2 αpi 2 β i gi ; (3.32) jANic

and the reactive power control is X q dij αqj 2 αqi 2 β i gi : α_ qi 5

(3.33)

jANic

The overview of the distributed cooperative voltage control is shown by Fig. 3.3. To synthesize the previous control into the feedback loop of each DG as defined by (3.15), we take derivative of (3.15) and (3.24a) and (3.24b), then we get the following equations: P_ gi 5 Vi I_ pi 5 Vi upi

(3.34a)

_ g 5 Vi I_ qi 5 Vi uqi Q i

(3.34b)

P_ gi 5 P gi α_ pi ;

(3.35a)

and

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Ying Xu et al.

Pref Leader i

Distributed cooperative algorithm Network measurement

fi

Local control

PDGi DGi

Storage

DGj

Customer response

Figure 3.3 Cooperative real power control.

_ α ; _ g 5 Qg α_ qi 1 Q Q gi q i i i

(3.35b)

_ can be where Vi and P gi should be measured from the bus, and Q gi obtained from (3.17), that is, _ 5 2 P gi α_ : Q p gi Qgi i

(3.36)

Thus by (3.34b), (3.35b), and (3.36), the reactive power control of the ith DG, uqi is written as uqi

αq P g Qi α_ q 2 i i α_ p Vi i Qgi Vi i 2 3 Qi 4 X q q 5 dij ðαqj 2 αqi Þ 2 β i gi 5 Vi jAN c 2i 3 X αq P g p p 2 i i4 dij ðαpj 2 αpi Þ 2 β i gi 5: Qgi Vi jAN c 5

(3.37)

i

Similarly, using (3.34a) and (3.35a), the real power control upi is written as 2 3 X Pi Pi p p upi 5 α_ pi 5 4 dij αpj 2 αpi 2 β i gi 5: (3.38) Vi Vi jAN c i

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Renewable energy integration and system operation challenge

Note 3.3.2. According to (3.37), uqi is dependent on αpi . The reason is the control strategy of real power control priority is adopted. upi and uqi can have other forms according to different control priorities, but the derivation should be the same. The performance of the proposed voltage control has been evaluated on multiple test systems such as: IEEE 123-bus, 8500-node, and other standard systems [11]. To show the effectiveness of the proposed approach on even larger scale systems, a 100,000 (100k)-node circuit is built by combining different type of feeders: the circuit is assembled by 12 urban/ suburban feeders, as shown in Fig. 3.4. The total load of the circuit is 122 MW. On top of the physical layer, 268 clusters are predefined for communication and control. Assume all load tap changers in the circuit are fixed at the predefined positions during the fast control of inverters. Two extremely harsh scenarios developed by the greedy search method [11] are studied: Case 1 defines 104 large-scale photovoltaics (PVs) among 12 feeders, which is the large-scale PV Farms scenario; Case 2 defines 2528 PVs distributed across the network, which is the distributed PVs scenario. In both cases, each of the feeders is selfDd:power, max = 1.24E005

Y

610.0

605.0

600.0

3985.0

3995.0

3990.0 X

Figure 3.4 100k synthetic circuits.

4000.0

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Ying Xu et al.

balanced in terms of its own PV generation and load consumption, and the load in each feeder is evenly distributed among PVs. Simulation results of both cases show that the proposed cooperative reactive power control can effectively regulate the system voltages for distributed PVs, as shown in Fig. 3.5. The spatial voltage profile of all phases is shown in Fig. 3.5, where Y-axis is the pu value of bus voltages and X-axis denotes the distance from substation. In Scenario 1 a total of 37 MVAr inductive reactive power is generated by PV inverters to suppress the voltage violation; the highest inverter capacity is 108.6%. In Scenario 2 a total of 22 MVAr of inductive reactive power is generated by PV inverters; the highest inverter capacity is 103.3%. Note that it is easier for the distributed scenario to control the voltage. To better illustrate the control effect, the detailed information about one part of the 100k system in Scenario 1 is shown in Fig. 3.5C. As shown by the results, the voltage is controlled within in the limits, and reactive power utilization ratios of all DGs in each feeder reach consensus.

3.3.3 Reactive power control and power factor control The voltage control scheme aims to hold the system voltage by the inverter’s ability of offering reactive power support at DG buses. Following (3.13), to control system voltage autonomously, the reactive power of DGs must respond to the real power injection of renewables. As a result, the reactive power at the feeder (the slack bus) would change accordingly; hence, the power factor at the substation will fluctuate and sometimes it might not be acceptable to the transmission side. To illustrate the effect of autonomous control on power factor, a simplified system with two concentrated loads and one aggregated DG is used, as shown in Fig. 3.6. In the system, E, Vl, and Ve are bus voltages; P0 1 jQ0 is the power at the feeder; the total generation is represented by Pg 1 jQg as an aggregated DG; the synthetic electrical line between aggregated DG and substation is assumed to be R 1 jX; the total load is denoted by P 1 jQ. The parameter α A [0, 1] is used to denote the position of the load bus, and βA[0, 1] is an additional parameter to split the load between two buses. Also define the losses by PL1 1 jQL1 and PL2 1 jQL2 for the two line segments (E,Vl) and (Vl,Ve), respectively.

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Renewable energy integration and system operation challenge

(A) p.u. voltage

p.u. voltage

L-N voltage profile

L-N voltage profile

1.080 1.040 1.060 1.020 1.040 1.000 1.020 0.980 1.000 0.960

0.980

0.940

0.960 10.0

0.0

20.0 Distance (km)

30.0

0.0

10.0

20.0 Distance (km)

30.0

(B) p.u. voltage

p.u. voltage

L-N voltage profile

L-N voltage profile

1.040

1.060 1.040

1.020

1.020 1.000 1.000 0.980 0.980 0.960

0.960 0.0

10.0

20.0 Distance (km)

30.0

10.0

0.0

20.0 Distance (km)

30.0

(C) 1.1

1.5

Case 1 Case 2 Case 3

0 1.05

1

Voltage (pu)

1 Voltage limits

0.95 0.9

0.5 Base line case W/O control Cooperative control Utilization

0.85 0.8

2

4

6 8 10 12 14 DG buses (sequence numbers)

Q/Qmax: Utilization ratio

Voltage limits –0.2 –0.4 –0.6 –0.8 Voltage phasors

–1 0

0

0.2

0.4

0.6

0.8

1

Voltage of bus s_ncctt575

Figure 3.5 Results of voltage control on 100k synthetic circuits: (A) scenario 1, (B) scenario 2, and (C) scenario 1: details.4

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Ying Xu et al.

Vl

E α(R+jX)

Ve (1–α) (R+jX)

Pg+jQg DG

P0+jQ0 β(P+jQ)

(1–β) (P+jQ)

Load

Load

Figure 3.6 Illustration system of distribution network with high penetration renewables.

It follows (3.8) that E2 2 Vl2 5 2αRP 1 2αXQ 1 αRPL1 1 2αRPL2 1 αXQL1 1 2αXQL2 ; E2 2 Ve2 5 2ðαβ 1 1 2 βÞRP 1 2ðαβ 1 1 2 βÞXQ 1 αRPL1 1 ð1 1 αÞRPL2 1 αXQL1 1 ð1 1 αÞXQL2 ; and 2 2 αR ; P2P 1P 1 Q2Q 1Q g L g L 2 2 Vl2 2 2 ð1 2 αÞR ð12βÞP2P 1 ð12βÞQ2Q PL2 5 ; g g Ve2 PL1 5

where QL1 5

PL1 X R

and

QL2 5

PL2 X : R

Given the voltage at feeder E, the four variables, Ve, Vl, PL1 , and PL2 , can be solved from the previous four equations. The voltage Ve and the total loss PL1 1 PL2 with respect to DG injection are shown in Fig. 3.7A and B, respectively. Previous results show that system voltage is monotonic to bus power injection [12], and system loss is convex when bus injections are near normal operational points [5]. These two properties are very useful to the control design of large-scale distribution network with high penetration of renewables. By Kirchhoff law the power at the feeder is then calculated by P0 5 P 2 Pg 1 PL1 1 PL2 ; Q0 5 Q 2 Qg 1 QL1 1 QL2

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Renewable energy integration and system operation challenge

To compensate the power factor at the feeder, the reactive power support required at the substation can be calculated by sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ﬃ 1 Qbank 9Q0 2 P0 21 (3.39) pf The results of Qbank and Q0 are shown in Fig. 3.8. As shown in Fig. 3.8B, it is clear that the reactive power of DG (Qg) increases (absorption) along with the increase of its real power (Pg), which is close to a linear relationship, as described in (3.13), given relatively small line loss. This is also true for Q0 and Qbank. The necessary reactive power support is also (A)

(B)

Total loss

Ve (pu)

0.06

1.04

0.05 0.04

1.02

0.03 0.02

1

0.01

0.98 0.8 0.6 0.4 0.2 Pg 0

0.96 1 0.5 Pg

0 –1

0

–0.5

1

0.5

–0.5

Qg

1

0.5

0 Qg

Figure 3.7 Bus voltage and system loss with respect to power injection from DGs: (A) bus voltage and (B) system loss. DG, Distributed generation. (A) 1.2

(B) Q bank capacity needed (pu)

Q0(pu)

1

1

pf = 0.95

0.8 0.8 0.6

pf = 1.0 pf = 0.95 pf = 0.9 pf = 0.85

0.4 0.2

0.2

0 –0.2 –0.4

0 –0.2

0.6 0.4

Qg Q QL1 QL2 Q0

–0.6 0

0.5

Pg(pu)

1

1.5

0

0.5

1

1.5

Pg(pu)

Figure 3.8 Reactive power support at the feeder with respect to power injection from DGs: (A) Qbank and (B) Q0. DG, Distributed generation.

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Ying Xu et al.

(A)

(B) Q bank capacity needed (pu)

1.4

1.6 1.4

α = 0.4 α = 0.6 α = 0.8 α = 1.0

β = 0.95

1.2

1.2 1

1 0.8

0.8

0.6

0.6

0.4 0.2

0.4

0 –0.2 0

Q bank capacity needed (pu)

α = 0.6 β = 0.95 β = 0.85 β = 0.75 β = 0.65

0.2 0.5

Pg(pu)

1

1.5

0 0

0.5

1

1.5

Pg(pu)

Figure 3.9 Reactive power support at the feeder with respect to power injection from DGs under different load conditions: (A) Qbank under different α and (B) different β. DG, Distributed generation.

studied with different α and β (different load conditions), and results are shown in Fig. 3.9A and B. Note 3.3.2. Previous equivalent model of a feeder is useful to analyze certain properties of distribution network, such as: reactive power schedule on transmission side, and the network planning. Also it can be used as heuristic modeling of feeders with less (or no) measurement, T&D cosimulation and control, etc.

3.4 Hierarchical multiagent control of large-scale distribution system The layered and divisional principle for large-scale power system planning, operation, and control has been practiced for years: Principal. For large-scale power system operation: 1. Volt/VAr control should be designed as local control, which is within certain electrical or geological area to prevent the transfer of reactive power, and to avoid unnecessary losses; 2. Real power control should respond to system-level objectives such as frequency regulation, and operation optimization.

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Renewable energy integration and system operation challenge

System level control Frequency (real power ) control

Voltage control Local control

Figure 3.10 Hierarchical control design for voltage and frequency control.

By following the previous operation principal, a hierarchical control is presented, as shown in Fig. 3.10. In the hierarchy, voltage control is considered as a local problem within an electrical or geological area, while real power control (frequency control) is formulated as global problem. Accordingly, the communication network is organized as either local or wide-area network. We define each local area as a cluster and each bus (or each information agent) as a node. We assign a virtual leader (VL) in each cluster which is the connection between the two layers: it works as virtual node in local cluster and an aggregated agent for the upper level control, as shown in Fig. 3.10. We expand the denotations to fit the hierarchy as follows: GL is the set of all VLs in all clusters; Nk is the set of all nodes in the kth cluster, kAGL ; 0 is a default member of all Nk s, which means the subscript 0 is specified for variables of VL node within each cluster, for example, Pg0 and Qg0 are used to denote the power output of VLs. Then we can redefine the communication matrix (3.18) for each cluster with its augmented form as follows: 2 3 s00 ðtÞ s01 ðtÞ ? s0n ðtÞ; 6 s ðtÞ s ðtÞ ? s ðtÞ; 7 11 1n 6 10 7 (3.40) S56 7; 4^ 5 & ? ^ sn0 ðtÞ

?

?

snn ðtÞ

where sij 5 1 if jANic , otherwise sij 5 0; and dij is redefined in the same way as in (3.21) for all i; jANk .

3.4.1 Virtual leader design The properties of VLs are specified by a superscript L and being defined L L as follows: P k and Qk are defined to collect the capacities of all DGs within the kth cluster, that is,

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L

P gk 5

X

L

P gj ;

Qg k 5

jANk

X

Qg j ;

kAGL ;

(3.41)

jANk

αLpk and αLqk are defined as utilization ratios of the kth cluster. For simplicity of expression, in the context of local control within the kth cluster, αLpk and αLqk are represented as αp0 and αq0 , respectively. The computational sequences in the hierarchy are shown in the information flow in Fig. 3.11: αkq is the result of local control; hence, it is calculated from each cluster by standard cooperative law, while real power αkp is on the opposite way, from system level to each of the clusters. The real power control aims to pursue system-level objectives such as driving the downstream or upstream power flow at the feeder to follow the dispatch signals from OPF (or control center), optimizing operation in terms of system loss or power quality, and so on. One practical design of control objective for real power, which focuses on the fast response to load demand and voltage regulation, can be written as following: fi L 5

Lf 2 λLv 2 λi Pf 2Pf 1 i 12ViL ; 2 2

iAGL ;

Lf

(3.42)

where λi $ 0 and λLv i $ 0 are the coordinate coefficients at the ith agent; Pf and Pf denote the power at the feeder of distribution systems and its dispatch value, respectively; ViL is the representative voltage of the cluster, Lf particularly, the worst measured voltage of the cluster. λi is the impact factor of frequency or power dispatch control for main grid (transmission side), which should be zeros when the distribution system is isolated from main grid. Let us define zik as the estimation of Pgk at the ith agent. As described by Fig. 3.11, reactive power is not considered as decision variable at sys Lf tem level. We follow [5] to make the assumption that λi =2ðPf 2Pf Þ2 is convex in the context of normal power system operation. Hence, (3.42) is convex and the distributed subgradient control (3.20) is also deployed System level decision k αp

k

αq

l

kth VL αp0

l

αp

αq lth VL

αq0

Local response

αp0

αq0

Local response

Figure 3.11 Information flow of the hierarchical control.

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Renewable energy integration and system operation challenge

on system level. Then distributed subgradient law (3.20) can be used, and we have X z_ ik 5 dij zjk 2 zik 2 β i gik ; (3.43) jANic

where gik is the gradient of fi L with respect to zi which can be written as @p @Vi f f Lf 1 λi ðVi 2 1Þ : (3.44) gik 5 λi Pf 2 Pf @pgk @Pgk Note 3.4.1. Different from local subgradient calculation at lower level, the standard subgradient calculation requires information across the system to pursue the optimal operation point on the system level (@Vi [email protected] ). However, the computation should not be too large for reasonably planned systems, because only the clusters that cannot control the voltage by themselves need the subgradients calculation. According to the hierarchy design, the voltage control term in (3.44) is a supplementary control and will only be performed when the local reactive power control is not enough. So @Vi [email protected] 5 0 when i 5 k because the local voltage control has already been considered at the lower level. When i6¼k, the supplementary voltage control is needed and hence the calculation of @Vi [email protected] is performed only under the following conditions: C1: αiq (or αq0 in the ith cluster) has reached its limit; C2: Vi violates the regulation limits; C3: Vk is well controlled within the regulation limits. The condition C3 implies that the reactive power at the kth cluster is sufficient to maintain the local voltage within that cluster, and the reactive power control is a fast response to change of Pk given Vk being well controlled. So we can estimate the reactive power response at VLs and use (3.13) to approximate the derivative of Qk to Pk, that is, 2Rkk =Xkk . We refer to branch power flow model (3.8), condition C3, and through derivation we can get the following results:

X @PLj @Vi 1 Rkk Xik 2 5 Rik 2 Z 0kj ; kAGL ; 2Vi @Pgk Xkk @P g k jAP - ðP , T Þ 1k

1i

i

(3.45)

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where the derivative of PLj with respect to Pgk can be calculated from (3.2a,b,c,d) and (3.10), that is, 8 " # > 2R R < j kk @PLj 2 if kATj PSj 2 QSj 5 ; (3.46) vj Xkk @Pgk > : 0 if otherwise where PSj and QSj can be measured. Note that (3.46) is an approximation made by assuming that the impact of both losses in the subtree of bus j on PLj and @PLj [email protected] is negligible. From the previous analysis, zik are consensus variables and the decision variable Pgi should be its estimation at the ith agent, that is, Pgi 5 zii :

(3.47)

3.4.2 Case study IEEE 8500-node system is used to test the proposed hierarchical control on both the system- and cluster-level real/reactive power control. First, we use a case with low voltage problem when only reactive power control is implemented, as shown in Fig. 3.12. The spatial power unbalance in the circuit causes huge voltage deviations between power sources and loads, so it is necessary to implement a system-level control that can work as supplementary control to fix these problems, especially for large-scale systems. In this case the area FM4 is experiencing low voltage problem due to the voltage deviations between power sources and loads in the large-scale system. The supplementary control is to compensate the shortfall that the local power control is only for the local voltage. The control response is shown in Fig. 3.13. It is shown that the system voltages increase due to the power injection at t 5 2 second, then the local reactive power control starts to push the voltage down. At around t 5 11 second the voltage at the lowest bus starts to violate the lower limit of voltage regulation. This is because the local reactive power control is only for the local voltage, which is shown in the second plot of the figure. About the same time the system-level control starts to kick in and the real power control (curtailment) responds to the system-level objectives. Eventually, the control settles down and all the voltages throughout the network are well controlled.

(A)

(B)

Y

IEEE8500:power, max=5E003 FM4

12,290,000

PVs FM10

pu voltage

L-N voltage profile

1.100

Low voltage

1.050 FM10

12,280,000

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FM9 PVs

FM9

0.950

12,270,000

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12,260,000 0.850 1,660,000 1,670,000 1,680,000 1,690,000 1,700,000

0.0

5.0

X

10.0

15.0

Distance (km)

Figure 3.12 Low voltage problem due to the spatial unbalance of power flow in IEEE 8500-node system: (A) circuit and (B) voltage profile.

(A)

(B) Mag

Iwstbus: V1

7800

dg_g1: V_DG

Mag 8600 8400 8200

7600

8000 7400 7800 7200

7600 7400

7000 0.0

10.0

(C)

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–0.50

0.00 0.0

10.0

20.0 Time (s)

30.0

0.0

10.0

20.0 Time (s)

30.0

Figure 3.13 The control response of DGs to disturbance in the system: (A) the lowest bus voltage, (B) bus voltage of DG1, (C) real power utilization ratio of DG1, and (D) reactive power utilization ratio of DG1. DG, Distributed generation.

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3.5 Islanded microgrid with high penetration of distributed generations In the previous control design, all inverters can be operated in PQmode (the grid-following control), because there are VF references when the distribution network is connected to the main power grid through midvoltage network. However in islanded mode, the reference for frequency and voltage control will not be available. In some design a VF-mode voltage source inverter [13] is used to provide the reference for VF as shown in Fig. 3.14; thus it is possible to operate the MG in islanded mode. In islanded mode of microgrids the output power of VF inverter (e.g., the energy storage systems) will balance out the load changes. The VF-mode inverter control design can be categorized into three types [14]: (1) droop control, (2) virtual synchronous machine (VSM), and (3) nonlinear oscillator synchronization. Take VSM method, for example, the bus angle, δref, and frequency, ωref, can be modeled by the following strategy: δ_ ref 5 ωVF 2 ω0 ; (3.48) M ω_ VF 5 PVF 2 PV F0 1 DðωVF 2 ω0 Þ where ω0 is the nominal value of frequency, PVF and P0 are the instantaneous and nominal power of VF-mode ESS, respectively, M and D are coefficiencies of VSM design. The PQ-mode DGs are assumed to follow the control command. In this scenario, let us assume the droop ratio mi for each of the PQ inverters, then the power desired in the system (which is the output power of the

VSI control

V,I

V,I

PLL ω PQ control

ωVF DC AC

Electrical network

AC DC Loads

Figure 3.14 The reference for the islanded system operation.

Primer mover

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VF inverters) can be distributed among all PQ inverters by the following droop strategy (as shown in Fig. 3.15): X mi ðω0 2 ωi Þ; (3.49) PVF 2 PVF0 5 where ωi is the instantaneous frequency measured by PLL at the connection bus, which is generated by VF inverters (ωVF) and spread through the electrical network. Hence, we have ωi 5 ωVF ;

(3.50)

given the dynamic of PLL is neglected. It is clear that the VF inverters in the islanded mode work the same as the slack bus. So in the islanded mode, we can split all VF inverters as the slack bus and apply the same control strategy (3.43) to the rest DGs. The power dispatch in (3.42) is equivalent to the output of VF inverters, that is,

Pf 2 Pf 5 PVF 2 PVF0 ; then we have

Pf 2 Pf 5

X

(3.51)

mi ðω0 2 ωVF Þ:

(3.52)

Hence, similar to (3.42), the control objective for the real power control in islanded mode can be written as follows:

Frequency

f f0

Power P

P0

Figure 3.15 The droop strategy for PQ inverters.

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fi 5

f 2 2 λp λi λv ðω0 2ωVF Þ2 1 i Vi 2Vi 1 i Pgrefi 2Pgi 2 2 2

f

(3.53)

p

where λi $ 0, λvi $ 0, and λi $ 0 are the coordinate coefficients at the ith agent; Pgi stands for the power injection of the ith agent; Vi is the representative voltage of the ith agent, particularly, the worst measured voltage f at that agent. We use λi to include the information of the droop gains (mis) for the simplicity of expression. The distributed subgradient algorithm (3.20) is used again to control the real power in order to pursue the previous goal. The subgradient of objective function defined by (3.53) can be calculated in the same way as p in Section 3.4: the subgradients of λvi =2ðVi 2Vi Þ2 and λi =2ðPgrefi 2Pgi Þ2 f are the same as in (3.44). The derivative of λi =2ðω0 2ωV F Þ2 with respect to Pgk can be calculated as follows: f

@λi =2ðω0 2ωV F Þ2 f 5 λi mk ðω0 2 ωV F Þ: @Pgk

(3.54)

Using (3.54) and (3.45), the subgradient of (3.53) can be obtained and hence the real power control for islanded mode is complete. It is necessary to emphasize in the islanded mode that the frequency and power balance is of much more importance than other objectives. This will be reflected in the design of coordinate coefficients, and an extreme case is to set λvi p and λi to zero and the objective becomes 1 fi 5 ðω0 2ωi Þ2 : 2

(3.55)

In this case the algorithm regresses to droop control. The fair utilization ratio method (3.32) is also applicable: X p dij αpj 2 αpi 2 β i gi ; (3.56) α_ pi 5 jANic p

where gi is the subgradient of fi which can be calculated from the previously mentioned procedure. For reactive power control in the islanded microgrid, the same control (3.33) is applied according to the previous local voltage control strategy (principal 4). Using OpenDSS, we evaluate the performance of the previous control scheme by standard IEEE system: 8500-node circuit. A virtual synchronous generator model is used to generate the frequency reference in the system, as shown in Fig. 3.16. The stiff source bus is simulated by the

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x'd

V∠θ

G E'∠δ P + jQ

Source

…

Stiff bus x'd E'∠δ

V∠θ

Source bus Circuit

Figure 3.16 The stiff source bus is simulated as the inner bus behind transient admittance of a virtual synchronous generator.

classic synchronous machine model with the constant inner bus voltage behind transient admittance of the generator. Then the frequency of islanded system can be generated by the frequency of the VSM. A worst scenario found by the greedy search method [11] of the system with 4 large-scale PV farms and 100% penetration is shown in Fig. 3.17. First, we built up an islanded system by opening the breaker at the feeder of IEEE 8500-node circuit, and using VSM to supply 300 kW at connection bus, assuming the reactive power supply is sufficient to maintain the inner bus voltage. Second, all regulators of the circuit are fixed at the predefined positions during the control. To test the active power and frequency control, we use dynamic simulation mode in OpenDSS. The simulation is set as simulation time T 5 30 second with time step h 5 0.005 second. A system disturbance of 2 MW load drop happens at t0 5 0.7 second. As shown in Fig. 3.18, the proposed algorithm is effective for system frequency control in the case of islanded operation of distribution system. When the demand of one load decreases at 0.7 second, the system frequency starts to increase correspondingly. Then the active power control of PVs responds to the change of frequency. As a result, both the frequency is well maintained by the proposed control. At the same time, the reactive power control is also effective to maintain the system voltages.

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Y

IEEE8500:power, max=5E003

12,290,000

12,280,000 VSM

12,270,000

Load drop at bus ‘M1027043’ 12,260,000

1,660,000

1,670,000

1,680,000

1,690,000

1,700,000

X

Figure 3.17 The worst scenario of IEEE 8500 system with four large-scale PVs.

dg_g1: frequency

Mag

dg_g1: V_DG

Mag

60.080 7500.0 Voltage

Frequency

60.060 60.040 60.020 60.000

7490.0

7480.0

59.980

0.0

10.0

Mag

20.0 Time (s)

30.0 dg_g1: P_DG

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–657.00

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10.0

20.0 Time (s)

0.0

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Real power

3200 –658.00

3100

–658.50

3000

–659.00 2900 0.0

10.0

20.0 Time (s)

30.0

30.0

Figure 3.18 The frequency control of IEEE 8500-node system on islanded mode.

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3.6 Grid-edge situational awareness: enhanced observability by voltage inference There are synchronous measurement and control units (SMCUs) and asynchronous measurement units (AMUs) in distribution systems. While SMCUs are probably limited, AMUs should be deployed on all the load buses. The insufficiency of SMCUs is the reason of the low observability of distribution network. However, it is not difficult and less expensive to upgrade AMUs for a smaller time interval of information gathering. It is possible to infer the system situation using information from both SMCUs and AMUs. To this end, we present a sensitivitybased grid-edge situational awareness method in this section.

3.6.1 Voltage inference method Assume some of the buses have SMCUs, while the rest buses do not. Instead, the rest buses have AMUs which can update their information for a particular period of time, T, which can be 5 minutes or several hours. We summarize two types of distribution network voltage inference scenarios in Fig. 3.19. In this figure, there are prosumers under some of the buses i, j, k, m, and n. The net power injection (direction defined by arrow) of each prosumer is denoted by P 1 jQ , where the superscript represents the bus number. The blue block represents the realtime measurement and control unit, while the yellow block denotes off-line measurement. The two scenarios of operational situation awareness for prosumer-dominated distribution systems are (I) subtree type (Fig. 3.19A), and (II) the two-end type (Fig. 3.19B). Type (I) represents a typical scenario in distribution network that bus i is an SMCU, but in the subtree of bus i, all buses are AMUs; type (II) is for a portion of circuit that the two ends are SMCUs; and most of other cases can be considered as a kind of the mix of these two types. Hence, in the following analysis, we will focus on the voltage inference of the two types of distribution network. Let us assume bus i as the relative root bus in both circuits, type (I) and (II), and also assume that it is an SMCU. So Eq. (3.8) is applicable to all buses in both circuits considering bus i as the slack bus:

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(A)

Vi

Vj

Vk

⋯

Vn

⋯

⋯

(Psi+jQsi) (Pj+jQj)

(Pi+jQi) (B)

Vi

(Pn+jQn)

(Pk+jQk)

Vj

(Psm+jQsm)

⋯

Vm

⋯

⋯

(Psi+jQsi) (Pm+jQm)

(Pi+jQi) Vk

⋯

(Pk+jQk)

Vn

(Pn+jQn)

Figure 3.19 Example of distribution network with measurements: (A) subtree type and (B) mix type.

Vk2 2

X mAPik , Tk

2Rkm Pm 1 2Xkm Qm 1 Z 0km PLm 1 Vi2 ;

kATi ; (3.57)

where Vi can be measured in real-time. Define the sensitivities of voltage at bus k to real and reactive power injection at bus j by ζ kj and ξkj, respectively: ζ kj 9

@Vk @Vk and ξ kj 9 ; @Pj @Qj

(3.58)

then we can infer all AMU bus voltages by Vk 5 Vk0 1 ΔVk ; where Vk0 is the initial value and ΔVk can be estimated by X ζ km ΔPm 1 ξ km ΔQm : ΔVk 5 2

(3.59)

(3.60)

mAPik , Tk

Therefore, the key of voltage inference is the network sensitivity calculation, which will be discussed in the next subsection.

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3.6.2 Network sensitivity It follows (3.57) that the sensitivities can be written as X Rkj 1 @PLm ζ kj 9 2 2 Z 0km ; 2Vk mAP , T Vk @Pj ik

(3.61)

k

and ζ kj 9 2

X Xkj 1 @PLm 2 Z 0km : 2Vk mAP , T Vk @Qj ik

(3.62)

k

From the previous analysis the system loss should always be relatively small compared to the power injection, for example, the loss of the illustration example (Fig. 3.6) is shown in Fig. 3.20. As in the example, within the studied region of (Pg, Qg), the derivatives are close to zero and monotonic along with the injection: loss is decreasing at the negative part and increasing after certain point (zero). By neglecting the system loss the voltage sensitivities of real and reactive power can be written as follows: ζ kj 2

Rkj Xkj and ξkj : Vk Vk

(3.63)

Using the illustration system, the previously defined sensitivities of bus voltage with respect to real and reactive power injections are shown in Fig. 3.21. As the plots show, Vi increases when the power Pg and Qg increase, so that the derivatives slightly decrease. Also it is clear that ζ kj 2 Rkj and ξ kj 2 Xkj ;

(3.64)

as shown in the illustration example (Rkj 5 0:025 and Xkj 5 0:05), and bus voltages are always close to 1 pu Hence, the voltage inference Eq. (3.60) is modified as the following application: X Vk 5 Vk0 2 ζ km ΔPm 1 ξkm ΔQm ; (3.65) mAPik , Tk

where Vk0 is the base value; ΔPm and ΔQm represent the power fluctuation at AMU buses and will be discussed in the next subsection.

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(A) Derivative of loss to Pg

0.2

0.1

0

–0.1

–0.2 –1 Qg

0

10

0.8

0.6

0.4 Pg

0.2

1

(B) Derivative of loss to Qg

0.2

0.1

0

–0.1

–0.2 Pg

1 0.5 0 –1

–0.5

0 Qg

0.5

1

Figure 3.20 The derivative of loss with respect to power injection: (A) loss to Pg and (B) loss to Qg.

3.6.3 Implementation From (3.65) the last question for voltage inference is to determine the power injection of AMU buses. Use the proposed model (3.13); we use standard estimation methods to tackle this problem. Load estimation is not the focus of this chapter; hence, we will briefly describe the problem and will not expand the detailed process here. According to the setup, at time Tt, all the measurements from both SMCUs and AMUs are known; in the time between Tt and Tt11 the information of SMCUs is available,

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Renewable energy integration and system operation challenge

(A)

Derivative of Ve to Pg 0.1

0.08 0.06 0.04 0.02 0 0

0.2

0.4

0.6

0.8

Pg (B)

11 0

–1 Qg

Derivative of Ve to Qg 0.1 0.08 0.06 0.04 0.02 0 –1

–0.5

0

0.5 Qg

1 10 0.5 P g

Figure 3.21 The derivative of loss with respect to power injection: (A) loss to Pg and (B) loss to Qg.

while that of AMUs is not until Ti11. Under the same assumption of neglecting system loss, the total power injection of all AMU buses (defined by Pt 1 jQt) can be calculated by applying Kirchhoff law in Fig. 3.19: in type (I) Pt 5 PSi 2 Pi and Qt 5 QSi 2 Qi ;

(3.66)

Pt 5 PSi 2 Pi 2 PSm and Qt 5 QSi 2 Qi 2 QSm :

(3.67)

and in type (II)

There are mainly four steps to implement the grid-edge situational awareness method: first, the total injection Pt 1 jQt and voltage deviation ΔVi (and/or ΔVm) can be obtained from measurement; second, using historical data and measurement at time Tt, we can apply one of

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the short-term prediction methods such as [15,16] to form a participation factor of each bus, then Pt 1 jQt can be distributed among these buses; third, using the real-time measurement at SMCUs (voltage, power flow), standard estimation method, for example, least square method, can be employed to improve the results of ΔPm and ΔQm; and last, plugging them into (3.65) to solve Vk, which completes the inference process. We use IEEE-123 system to test the performance of the proposed method. As shown in Fig. 3.22, four PVs are installed in the subtree of bus 72, two SMCUs (black stars) at bus 72 and 77, and 16 AMUs (emptystars) at some other buses. Two stars are at bus 77, one for each case: case 1 with bus 77 as an AMU and case 2 with bus 77 as an SMCU. The sensitivities of bus voltages with respect to different real and reactive power injections at bus 89 are shown in Fig. 3.23. The results are PU values with the base power as 3.6 MVAr and base voltage as 2.4 kV. The lines are the results from simulation of changing the power injection at bus 89, while the stars are the approximated sensitivities at each bus using (3.64) by the network parameters. It is clear that the numerical results are close to the results approximated by system parameters. So in the case of fast calculation, network sensitivity

Figure 3.22 IEEE 123 system with SMCUs (black stars), AMUs (empty stars), and PVs on bus 72, 77, and 89. AMU, Asynchronous measurement unit.

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Renewable energy integration and system operation challenge

Voltage sensitivity w.r.t. PV injection at bus 89 0.04

to Q

0.035

Sensitivity

0.03 Simulation results System parameters

0.025 0.02 0.015

to P

0.01 0.005

Bu s9 5

Bu s9 3

Bu s9 1

Bu s8 9

Bu s8 7

Bu s8 6

Bu s7 6

Bu s7 2

0

Figure 3.23 Voltage sensitivity with respect to different real and reactive power injections.

can be roughly approximated by network parameters, that is, ζ kj 2 Rkj and ξ kj C 2 Xkj . Case 1 is designed as type (I) voltage inference scenario, where no SMCU is placed in the subtree of bus 72 (SMCU). Assume that, at time Tk, all the measurements are available at bus 72, but all measurements from AMUs are not reachable at time Tk 1 Δt, Δt A (Tk, Tk11). At time Tk 1 Δt the voltage and the downstream (upstream) power at SMCU can be measured; given a forecast load, the output of PVs can be estimated. Then bus voltages can be inferred by the proposed method. Simulation results in Fig. 3.24A show that the voltages can be accurately inferred. The red and blue lines are the system voltage lines of two scenarios with different PV allocations. The red one is worse because the load prediction is worse than the blue. Case 2 is designed as type (II) voltage inference scenario, where another SMCU is placed at bus 77. The extra information help improve the result, especially when the load change is larger in the subtree of bus 77. In this case, we set a load disturbance of PV 77. The results are shown in Fig. 3.24B, where the stars are the results with realtime measurement at bus 77 and the diamonds are without SMCU there. It is obvious that the voltage inference results with real-time measurement are better than without real-time measurement.

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(A)

1.04 1.0395

Voltage (pu)

1.039 1.0385 1.038 1.0375

PV output is close to prediction PV is not close to prediction Voltage inference

1.037 1.0365

5 s9 Bu

3 Bu

s9

1 s9

s8 Bu

Bu

9

7 s8 Bu

Bu

s8

6

6 s7 Bu

Bu

s7

2

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Actual voltage Inference with AMU at bus 77 Inference with SMU at bus 77

1.04

5 Bu s9

Bu s9 3

1 Bu s9

9 Bu s8

7 Bu s8

Bu s8 6

6 Bu s7

Bu s7

2

1.039

Figure 3.24 Simulation results of voltage inference on IEEE 123 system: (A) Case 1 and (B) case 2.

3.7 Control-enabled dynamic hosting allowance: P and Q control capacity and impact analysis 3.7.1 Traditional hosting analysis Hosting capacity (HC) is an important planning tool for both distribution system operators and DG investors to assess how much DG generation

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can be integrated into the distribution network [17]. It is a limit of the quantity of renewables allowed to be integrated without imposing any changes to the existing infrastructure and without violating operational limits. Up until HC, DGs can be easily interconnected and could be approved through fast-track process. There are numerous unspecified factors for HC calculation, such as DER locations and capacities, control settings of feeder equipment such as voltage regulators, and the changing operational conditions (load level). The exhaustive detailed HC calculation is usually not preferred in practice, so instead the stochastic methods are widely used. Based on the trends observed from many detailed study cases, EPRI presented a streamline method to speed up HC calculation [18]. However, all existing hosting analyses are generally conservative and require further development in the following aspects: • to incorporate time-serious analysis using prediction and trend data other than worst-case snapshot data • to involve advanced inverter control and complimentary DER technologies • to explore rapid approaches for HC calculation and scanning • to further study the interactivities between DERs and the impact on HC It is very possible and feasible, along with the development of renewable technologies, to install more DER than HC either by upgrading or installing additional equipment, or developing advanced operational and control strategies. Furthermore, HC is a system-oriented terminology and specific for planning. To the extent of system operation, it is required to go beyond the HC which needs more extensive and much faster analysis and control strategy. To this end a network sensitivitybased DHA method is presented in this section, which includes both the operating condition and the control impact of DER inverters.

3.7.2 Dynamic hosting allowance analysis Voltage problems, specially voltage rise, are considered as the most significant problem for high penetration of DG integration [19], so the systemwide max voltage is a major factor in hosting analysis. As shown in Fig. 3.25, the trend of maximum feeder-wide primary voltage in a particular deployment of PV with respect to penetration level appears roughly linear [18], but the physical nature behind this observation has not been

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s on ati c o ll ma pti ns o n tio ca No o l al tim p O

Penetration

Figure 3.25 System voltage shows a linear trend with respect to renewable penetration [18].

discussed yet. In the previous section, we built the network sensitivity, and through analysis, we found that the network sensitivity is a property of distribution network. Here we rewrite (3.60) by the following matrix form:

where

ΔV 5 ΠΔP 1 ΞΔQ;

(3.68)

n o n o Π 5 ζ kj and Ξ 5 ξkj ;

(3.69)

which can be approximated as constant (the system parameter of distribution network, Rkj and Xkj ). The properties of network sensitivity, that is, Π and Ξ, can explain the linear trend shown in the next figure. Using sensitivity matrix, we can improve the HC maps [17] by revealing the additive effect of system-wide DERs to critical buses—DHA. As shown in Fig. 3.26, the traditional HC map did not answer the question of how Pi 1 jQi and Pk 1 jQk contribute to the critical voltage Vn; on the other hand, with certain head room of Vn (and/or other critical bus voltages), how to strategically deploy Pi 1 jQi and Pk 1 jQk, as well as the injection of other DERs. As shown in Fig. 3.27, the network sensitivity together with synchronous and asynchronous measurement can greatly improve the functionality of HC maps. This can be further extended into real-time DG operation and control (if the DG inverters are able to offer extra control ability).

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Figure 3.26 HC map can indicate how HC varies along feeder segments using appropriate color coding in regards to the current percentage of each segment, but additive effect of different DGs to different bus voltages is not analyzed. DG, Distributed generation; HC, hosting capacity.

(A)

Vj

~

Vi

V0

⋯

(Pj+jQj)

~

~

Vm

Vn

⋯

~

(Pi+jQi)

~

~ ⋯

~

(Pk+jQk)

~

~

- Prosumer - AMU - SMU

(B) Vj

(Pj+jQj) Vm

Vi (Pi+jQi)

Sensitivity

Vk

(Pk+jQk)

(Pn+jQn)

Vn (Pn+jQn) - AMU - SMU

Figure 3.27 The sensitivity of system-wide DERs to the critical bus: (A) physical system and (B) network sensitivity. DER, Distributed energy resources.

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In the context of operation control, the critical bus voltage is the key factor of DHA. We can first identify the head room of critical voltage, which can be assessed through the grid-edge situational inference process. Assume that the voltage head room of critical bus n is ΔVn, then we have X ζ nm ΔPm 1 ξnm ΔQm : (3.70) ΔVn 5 2 mAPin , Tn

Then, with specific control strategy in the system, we can quickly calculate the DHA for the system. For example, define a parameter as σ to describe capability of inverter, that S 5 ð1 1 σÞP. We can calculate ΔQm by pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ΔQm 5 2 Qm 5 σ2 1 2σP m 5 2 σ2 1 2σðPm 1 ΔPm Þ; (3.71) and finally solve (3.70) for ΔP, which is the result of DHA. Note that as reactive power control is enabled, the system current increase; hence, the thermal limits should be checked. We use IEEE 123-bus system to illustrate the DHA process. We use case 1 in the previous section as the example to calculate DHA at bus 72 in IEEE 123-bus system. From the simulation results (Fig. 3.23) in the previous section, we know that the network sensitivity has the property of monotonically increasing from root to the leaf side of the circuit. So the highest DHA of this part of the system should be DHA at bus 72 with the concern of voltage rise. From the results of case 1, we also know that the voltage head room of this scenario is ΔV 5 1.05 21.0396 5 0.0104 pu (base power is 3.6 MW and base voltage is 2.4 kV). Also we know that the real and reactive power sensitivities at bus 72 are ζ in 5 0.048701 and ξ in 5 0.0936 pu. If reactive power control is not considered, the DHA at bus 72 can be directly calculated: ΔP 5

ΔV 5 0:2135 pu: ζ in

(3.72)

When reactive power control is enabled, we assume σ 5 0.02, then we can plug (3.71) into (3.70) and solve ΔP as pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ΔV 1 σ2 1 2σξin P pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ΔP 5 5 0:52 pu: (3.73) ζ in 2 σ2 1 2σξin

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3.8 Cosimulation of integrated transmission and distribution systems 3.8.1 The framework of cosimulation 3.8.1.1 Integrated T&D system simulation The cosimulation architecture of integrated T&D system is developed as follows. The snapshot of transmission system power flow is first solved, and the voltage at each bus connected with the distribution systems is saved. Each distribution system is assigned a process with parameters of voltage levels, circuit name, and process number. Once each process is established, they start and execute simultaneously. The various sizes and complexity of distribution systems cause the power flow to solve in different time duration. Thus the algorithm waits for the convergence of all distribution systems before sending the real and reactive power values at the substation back to the transmission bus. A text file is generated containing the real and reactive power at the substation after each power flow solution with respect to the transmission voltage. The transmission system power flow is then solved again with the updated parameters. The algorithm iterates through this process until the ΔV # 0.0001. In summary the cosimulation architecture follows the following steps: S-1. Load and solve transmission system. S-2. Assign a distribution system to 10 of the load buses. S-3. Extract Vpu at each load bus. S-4. Define a process for each distribution system with its corresponding voltage value. S-5. Solve each distribution system in parallel. S-6. Send P and Q from substation node to the transmission system. S-7. Update transmission load bus with P and Q obtained from distribution. S-8. Repeat steps 17 until ΔVpu (of each load bus) # 0.001. The integrated T&D system cosimulation architecture was modeled and simulated using cosimulation of Python and MA-OpenDSS. Loads in the buses of transmission system were analyzed to determine real and reactive power values which resembled the total real and reactive power of various distribution systems. Once the buses were identified, the distribution systems are allocated to each individually. Python was used to

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interface with MA-OpenDSS and facilitate the usage of parallel processing when solving the power flow of each of the distribution systems. 3.8.1.2 Parallel cosimulation of integrated T&D systems The parallel simulation was performed on the distribution systems, while the transmission system waited to receive the real and reactive power values. The same type of simulation was performed but in a sequential manner. The distribution systems ran one at a time, while the transmission system waited for the results. This was done to compare and verify the results. When running the distribution systems in sequence, the simulation time was increased, but real and reactive power values remained the same. Simulation results show that even though the sequential simulation of each distribution system tends to be slightly faster than parallel simulation (less processing power required to run one distribution system at a time), it takes longer to complete the overall simulation. While the parallel simulation is bottlenecked by the simulation of the largest distribution system, the sequential simulation waits for one distribution circuit to converge before running the next, thus taking much longer time to perform the integrated T&D simulation. The simulation framework has been implemented on the latest version of MA-OPenDSS with the parallel capability, which allows different distribution networks to be simulated in parallel by assigning them to different cores in a multicore computer. This makes it scalable to handle multiple large-scale distribution networks in integrated T/D studies.

3.8.2 Simulation results 3.8.2.1 Power flow of integrated T&D systems The topology of T&D cosimulation is shown in Fig. 3.28. The T&D test system consists of an IEEE 14-bus transmission system with three detailed G Generators C Synchronous condensers 100k_8

100k_2 13

Nodes12

100k_1 Nodes 11

G 1

100k_4 Nodes

100k_0 Nodes Transmission bus voltage

6 100k_5

C

Three Winding transformer equivalent 9

100k_7 Nodes

Nodes

9

C 8

100k-node circuit 2 100k-node circuit 3

7 4

100k_6 Nodes

2

100k-node circuit 4 100k-node circuit 5

G 3

Distribution P and Q at substation

100k-node circuit 1 100k_9 Nodes

10

Transmission

8 4

Nodes 5

C

7

14

100k_3 Nodes

AEP 14 bus test system bus code diagram

Figure 3.28 Integrated T&D simulation topology.

100k-node circuit 10

Send P and Q to transmission Wait for all to converge

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distribution networks: IEEE 8500-node system, 11,000-node (IEEE 8500 1 EPRI Circuit 7) system, and 100,000-node (12 of 8500-node circuits) system. The system can easily be expanded by replacing lumped transmission-level loads with distribution networks. Table 3.1 shows the simulation results of four iterations with total 27 seconds of simulation time. One can observe the response of distribution systems to various voltage changes in each iteration. The 14-bus transmission system can be scaled up to accommodate for ten 100k-node distribution circuits located at each of its load buses. Preliminary simulation of ten 100k-node distribution circuits was executed in both parallel and sequential manners without any transmission connection, in order to compare the computation time. For sequential simulation, each of the 10 circuits took approximately 4.7 seconds to solve, and the overall simulation time was about 53 seconds (exporting results, setting up the systems counts are part of the total simulation time). On the other hand, the parallel simulation of 10 circuits resulted in a total of 35 seconds, which counts for one iteration of the integrated T&D without the transmission power flow simulation time. Thus it can be projected that there will be a tremendous benefit in reducing computation time. Furthermore, a faster machine with more processing power and core availability can improve the parallel processing computation time. 3.8.2.2 Integrated T&D systems with PVs and control PVs and control are added to the integrated T&D systems. A synthetic 100,000-node NREL system with 100% penetration of small-scale PVs and distributed control was selected as the distribution system. Table 3.2 displays the real and reactive power for the base case, the case adding PVs without control, and the case adding PVs with control. When PVs are present, the reactive power demand increases significantly (approximately 1.4 times the base case) in order to address overvoltage problems. On the other hand, the distribution system is supplying the transmission with about 1.05 MW. In order to address high reactive power demand, capacitor banks are placed on the secondary side (12.47 kV) of 14 feeders of NREL-100k system. Two T&D cosimulations were carried out with an aggregated capacitance of 110.8 and 153.5 MVAr, as shown in Table 3.3. Two capacitor banks were used in order to address the fluctuations of the substation voltage. Table 3.4 displays T&D cosimulation results of three different simulation scenarios: (3), (4), and (5). The distribution system was connected to

Table 3.1 Simulation results of integrated T&D study. 8.5k Nodes

11k Nodes

100k Nodes

Ite

V bus 13

P

Q

V bus 11

P

Q

V bus 14

P

Q

1 2 3 4

0.9495 0.9666 0.9660 0.9659

10,092.358 10,422.447 10,410.870 10,409.064

2707.363 2760.587 2758.983 2758.731

0.9495 0.9665 0.9659 0.9658

77,552.727 79,997.143 79,907.645 79,894.028

35,871.226 36,923.693 36,887.208 36,881.875

0.9493 0.9664 0.9658 0.9657

144,610.512 144,026.118 144,055.285 144,059.901

21,991.715 18,664.282 18,825.498 18,850.989

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Table 3.2 T&D cosimulation cases. Simulation type

P (kW)

Q (kW)

1. Base case 2. PVs, no control 3. PVs, with control

123,992.6 2 870.58 2 1052.5

48,282.3 38,468.4 66,138.5

Table 3.3 T&D cosimulation cases with capacitor banks. Simulation type Aggregated capacitance (MVAr)

P (kW)

Q (kVAr)

4. DG, with control 110.80 (12.47 kV) 5. DG, with control 153.50 (12.47 kV)

2 1147.8 2 1192.26

2 390.2 2 34,922.2

DG, Distributed generation.

bus 11 of the IEEE 14-bus system, considering the smaller load at this bus (3.5 MW, 1.8 MVAr). It can be observed that it takes nine iterations to converge without the capacitor banks. On the order hand, it takes five and four iterations to converge with aggregated capacitor banks of 110.8 and 153.50 MVAr, respectively. Another observation can be made with respect to the difference in total reactive power supplied by the capacitors. Although the power factor at the substation when solving distribution system power flow is much better with the total Q of 110.80 MVAr provided by capacitor banks, this is not the case when the voltage changes at the transmission side. The reactive power shifts from supplying 390.2 kVAr to transmission to demanding 8370.72 kVAr from transmission. Meanwhile, the feeder supplies reactive power when there is approximately 43 MVAr increase in the total capacitor banks. Further studies can be performed in order to find the optimal capacitor placement and capacity in order to maintain a relatively reasonable power factor.

3.9 Conclusion This chapter presented a hierarchical distributed control framework to model, analyze, optimize, and control large-scale distribution system with extremely high penetration of renewables. Following the layered and divisional principle for large-scale power system planning, the Volt/VAr control is mainly treated as a local control, while the real power

Table 3.4 Comparison of T&D cosimulation cases with capacitor banks. (3) No Qbank (4) Qbank 5 110.80 MVAr

(5) Qbank 5 153.50 MVAr

V (pu)

P (kW)

Q (kVAr)

Voltage

P (kW)

Q (kVAr)

V (pu)

P (kW)

Q (kVAr)

1.0179 0.9341 1.0154 0.9398 1.0068 0.9602 0.9736 0.9697 0.9698

2 1038 2 2824 2 1167 2 2847 2 1566 2 2458 2 2344 2 2396 2 2363

62,519 21,250 60,371 26,773 51,582 45,637 47,560 47,585 47592

1.04536 1.04418 1.04439 1.04446 1.04453

2 1070.58 2 1101.21 2 1084.81 2 1079.17 2 1074.32

8527.31 8354.14 8364.2 8366.38 8370.72

1.0179 1.06945 1.06955 1.06956

2 1022 2 1009 2 1070 2 1069

2 35,725 2 30,867 2 30,698 2 30,699

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control is a system-level control (frequency) and a supplementary control for local voltage, which will respond only when reactive power control is insufficient. For electrical circuit, both nodal injection and branch power flow models are used to model distribution network; and a simplified power control model of DG is used, which is simple but good enough to illustrate the design of system operation and control. A detailed analysis of the system-level effect of local autonomous controls is presented in this chapter. Also how the presented control runs in islanded mode of the distribution system is provided. To tackle the problem of low observability in distribution network, a sensitivity-based grid-edge situational awareness method is presented for distribution system state estimation, control, and optimization. Following that a network sensitivitybased DHA method is presented for system operation, as an extensive and much faster analysis compared to the traditional HC. At last, to validate the feasibility and demonstrate the scalability of developed models and algorithms, a cosimulation architecture of integrated T&D system is developed.

References [1] D.K. Molzahn, F. Dörfler, H. Sandberg, S.H. Low, S. Chakrabarti, R. Baldick, et al., A survey of distributed optimization and control algorithms for electric power systems, IEEE Trans. Smart Grid 8 (6) (2017) 29412962. [2] S. Xia, S. Bu, C. Wan, X. Lu, K.W. Chan, B. Zhou, A fully distributed hierarchical control framework for coordinated operation of DERs in active distribution power networks, IEEE Trans. Power Syst. 34 (2018) 51845197. [3] H. Xin, Z. Lu, Z. Qu, D. Gan, D. Qi, Cooperative control strategy for multiple photovoltaic generators in distribution networks, IET Control Theory Appl. 5 (14) (2011) 16171629. [4] Y. Xu, Z. Qu, R. Harvey, T. Namerikawa, Data-driven wide-area control design of power system using the passivity shortage framework, IEEE Trans. Power Syst., 2019. [5] L. Gan, S.H. Low, An online gradient algorithm for optimal power flow on radial networks, IEEE J. Sel. Areas Commun. 34 (3) (2016) 625638. [6] Z. Qu, Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles, Springer Science & Business Media, 2009. [7] A. Nedic, A. Ozdaglar, Distributed subgradient methods for multi-agent optimization, IEEE Trans. Autom. Control. 54 (1) (2009) 4861. [8] R. Harvey, Y. Xu, Z. Qu and T. Namerikawa, “Dissipativity-based design of local and wide-area DER controls for large-scale power systems with high penetration of renewables,” 2017 IEEE Conference on Control Technology and Applications (CCTA), Mauna Lani, HI, 2017, pp. 21802187. [9] A. Gusrialdi, Y. Xu, Z. Qu, M.A. Simaan, A real-time big data control-theoretical framework for cyber-physical-human systems, in: M. Blondin, P.M. Pardalos, J.S. Sáez (Eds.), Computational Intelligence and Optimization Methods for Control Engineering, Chapter 7, Springer Nature Switzerland AG, Switzerland, 2019, pp. 125.

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[10] A. Maknouninejad, Z. Qu, Realizing unified microgrid voltage profile and loss minimization: A cooperative distributed optimization and control approach, IEEE Trans. Smart Grid 5 (4) (2014) 16211630. [11] M. Rathbun, Y. Xu, Z. Qu, W. Sun, et al., Impact studies and cooperative voltage control for high pv penetration, IFAC-PapersOnLine 51 (28) (2018) 684689. [12] K.N. Miu, H.-D. Chiang, Existence, uniqueness, and monotonic properties of the feasible power flow solution for radial three-phase distribution networks, IEEE Trans. Circuits Syst. I: Fundam. Theory Appl. 47 (10) (2000) 15021514. [13] J.A.P. Lopes, C.L. Moreira, A.G. Madureira, Defining control strategies for microgrids islanded operation, IEEE Trans. Power Syst. 21 (2) (2006) 916924. Available from: https://doi.org/10.1109/TP-WRS.2006.873018. ISSN 0885-8950. [14] B. Kroposki, B. Johnson, Y. Zhang, V. Gevorgian, P. Denholm, B. Hodge, et al., Achieving a 100% renewable grid: operating electric power systems with extremely high levels of variable renewable energy, IEEE Power Energ. Mag. 15 (2) (2017) 6173. [15] C. Guan, P.B. Luh, L.D. Michel, Z. Chi, Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation, IEEE Trans. Power Syst. 28 (4) (2013) 38063817. [16] Y. Yu, T. Choi, C. Hui, An intelligent quick prediction algorithm with applications in industrial control and loading problems, IEEE Trans. Autom. Sci. Eng. 9 (2) (2012) 276287. [17] S.M. Ismael, S.H.E. Abdel Aleem, A.Y. Abdelaziz, A.F. Zobaa, State-of-the-art of hosting capacity in modern power systems with distributed generation, Renew. Energy 130 (2019) 10021020. [18] M. Rylander, J. Smith, W. Sunderman, Streamlined method for determining distribution system hosting capacity, 2015 IEEE Rural Electric Power Conference, IEEE, 2015, 39. [19] T. Walla, J. Widén, J. Johansson, C. Bergerland, Determining and increasing the hosting capacity for photovoltaics in Swedish distribution grids, in: 27th European Photovoltaic Energy Conference (EU-PVSEC), 2428 September 2012, Frankfurt, Germany, 2012.

CHAPTER FOUR

Advances of wholesale and retail electricity market development in the context of distributed energy resources Qin Wang Department of Power Delivery & Utilization, Electric Power Research Institute, Palo Alto, CA, United States

Contents 4.1 Introduction 4.2 Modern wholesale electricity market 4.2.1 Driving forces of the electricity market development 4.2.2 Operation process of wholesale electricity markets 4.2.3 New products and designs in wholesale electricity markets 4.2.4 Running the electricity market with 100% renewables 4.3 Modern retail electricity market 4.3.1 State-of-the-art for retail electricity market development 4.3.2 Design of retail electricity market operation framework 4.3.3 Implementation of blockchain technology in electric power systems 4.4 Conclusion References

99 100 100 102 104 117 130 130 133 138 140 140

4.1 Introduction The supply of variable wind and solar generation with zero marginal operating cost is increasing rapidly around the world. Emerging customer behavior and public policy is driving the growth of smaller scale, behindthe-meter (BTM) distributed energy resources (DERs) such as solar photovoltaic (PV) and battery storage in many systems. The increasing penetration of utility-scale renewables and BTM DERs is changing the way how power system is operated in both transmission and distribution grids. In the wholesale electricity markets, many profound questions have New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00004-3

© 2021 Elsevier Inc. All rights reserved.

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been raised about whether the current market design can provide both long-term and short-term price signals to support efficient investment and maintain reliable operations. For example, when most of the resources in the system have zero marginal costs, the resource owners will have to recover their cost for participating into the market either from the capacity market or by operational scarcity price. The short-term energy and ancillary service market, which was built upon middle 20th-century models of optimal pricing and investment, should be modified to consider the variability and uncertainty of intermittent generation at scale. From the retail side the current distribution grid operators are not designed to integrate and coordinate large volume of flexible customer, merchant DER, and microgrids capable of seamless islanding. There is an increasing need to develop retail electricity markets for DER products and services. The retail market operators are responsible for three major functions, including integrated system planning, distribution grid operation, and retail market operation. They balance the supply and demand variations at the distribution level and link the wholesale and retail market agents. This chapter introduces the recent advances of wholesale and retail electricity market development in the context of DERs. From the wholesale perspective, the new market design and models to address the challenges faced by the bulk grid is discussed. In particular, the changes of power system operation paradigm at 100% renewable energy penetration level are discussed. From the retail perspective, a framework to construct retail market platform is proposed and the use of blockchain technology to facilitate retail transactions is introduced.

4.2 Modern wholesale electricity market 4.2.1 Driving forces of the electricity market development During the past half century the power industry has experienced several transformations. Prior to 1960s, almost all the electricity was supplied from vertical integrated power utilities. In the United States, it was generally accepted that the first time the power industry introduced competition was in 1978, when the Public Utility Regulatory Policies Act (PURPA) was passed [1]. The most significant progress of this law was creating a market for power from nonutility power producers (which is

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called independent power producer or IPP today). Before PURPA, only utilities are authorized to own and operate electricity generation plants because energy companies were classified as natural monopolies. The PURPA enabled utilities to buy power from nonutility generators, which could produce power at a lower cost to server customers attached to the utilities’ grids [2]. The second wave of the electric power industry reform started in the middle of 1990s. On April 24, 1996 the Federal Energy Regulation Commission (FERC) issued two landmark orders— Order 888 and Order 889 [3]. The Order 888’s primary goal was to establish and promote generation competitions in the wholesale side by ensuring equal access and market treatment of transmission customers. The Order 889 conceived the establishment of the Open Access SameTime Information System and set standards for how utilities and customers would come to share information in the transmission system. On December 20, 1999 FERC issued another landmark order—Order 2000 [4], to encourage the establishment of regional transmission organizations (RTOs) and further prompt the goals in previous orders by bringing all transmission control under independent entity’s management. These orders led to the creation of three independent system operators (ISOs), including California ISO (CAISO), New York ISO, and the Electric Reliability Council of Texas and four RTOs, including PJM Interconnection, ISO New England, Midwest ISO, and the Southwestern Power Pool. In the past decade the driving forces for the new development of electricity markets are the increasing penetration of renewable and DERs, along with the implementation of smart grid technologies such as the smart meters and phasor measurement units, and the participation of electricity consumers into the market. From the market design perspective, the fast growing of renewables and DERs could lead to several significant challenges. First, renewable and DERs are variable and uncertain by nature. As a result, additional actions are required to balance the system, including higher operating reserves, manual operations from system operators, and commitment of fast start units. Second, the system will see higher frequency of extreme events on net load ramping, so there is greater need of flexibility in the system. Insufficient flexibility may lead to area balance violations, price volatility, and significant renewable energy curtailments. Third, many DERs are located behind the meter, so there are increasing needs to perform transmissiondistribution coordination in system operations. In New York State the utilities are encouraged to form

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distributed system platforms (DSPs) that will serve as neutral gatekeeper between the energy producers and energy consumers [5].

4.2.2 Operation process of wholesale electricity markets The objective of establishing an electricity market is to facilitate an economical operation while ensuring the security of the system. Two major components are included for the operation of today’s ISO-based electricity markets: day-ahead market clearing process and real-time market operation. Their structures are indicated in Figs. 4.1 and 4.2, respectively. Fig. 4.1 shows the clearing process of day-ahead electricity market. The market participants submit their supply and demand bids to the ISO. At first, a pure unit commitment problem is solved in the resource commitment (RSC) application procedure. This generally involves solving a mixed integer programing problem. The result of RSC (on and off status) is sent to the market clearing engine (MCE) to identify the optimal dispatch (MW and PAR angle) of resources. Then the dispatch result is sent to the network security analysis procedure to check if there is overload violation on the circuits for both normal and postcontingency states. This is called the simultaneous feasibility test (SFT) [6] process. If there are no violations, the market clearing result is obtained. Otherwise, the SFT will Inputs: Supply and demand bids from market participants

Unit commitment (resource commitment application)

Extra resources committed

No

Daily resource schedules

Generation scheduling (market clearing engine)

Yes

Reschedule resources

Are resources sufficient to achieve feasibility?

Publish market clearing result Hourly resource schedules

Yes

Inputs: Network model, network conditions and contingency list

Network security analysis (simultaneous feasibility test)

Is network loading feasible?

No

Figure 4.1 The clearing process of day-ahead electricity market.

Create network constraints (SFT constraints)

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Figure 4.2 Real-time operation of power system and wholesale electricity market.

generate generic constraints that will eliminate the violations. The generic constraints are fed back to the MCE to redispatch the resources. If the MCE finds that the current RSCs are not sufficient to support the secure dispatch, the unit commitment problem is solved again to recommit the available resources. Otherwise, the MCE will find the optimal result thus the market clearing results could be obtained. Fig. 4.2 demonstrates how the real-time power system and wholesale electricity market are operated [7]. The system and the market are two interconnected components in the operation of ISOs. Historically, ensuring secure operation of the system has always been a critical task. Thus the system conditions are sent to a so-called security assessment (SA) procedure to check if the current system is secure. The SA includes static SA, which includes contingency analysis (CA), and dynamic SA (DSA), which includes transient stability analysis and voltage stability analysis. The static SA will generate thermal constraints, which are able to protect the transmission facilities from thermal overload. The DSA will generate generic constraints, which are able to protect the transmission system from

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transient instability and voltage collapse. On the other hand, the CA result will be sent to a security enhancement procedure to generate control recommendations to operators. The purpose of security enhancement is to implement control actions to enhance the system’s security level, which is realized through the security-constrained optimal power flow (SCOPF). The SCOPF formula is usually divided into two parts: the active power subproblem and the reactive power subproblem. The SCOPF is a nonlinear optimization problem, with the objective function of minimizing the control changes from the base-case. The constraints generated in security enhancement procedure are selected by the system operators and then sent to the transmission constraints management (TCM), along with the thermal and generic constraints generated in SA. The TCM will determine activated constraints for the security-constrained economic dispatch (SCED). Based on market participants’ offers, SCED produces a least cost dispatch of resources to meet the system requirements, including the transmission constraints and resource limit constraints. The SCED generates locational marginal price (LMP) for the market and base points for system operation.

4.2.3 New products and designs in wholesale electricity markets 4.2.3.1 Considering flexibility in resource adequacy In the past the electric power system was considered to have sufficient resources if the generator capacity was sufficient to meet the expected peak demand with a certain margin or risk tolerance. This was typically based on a capacity margin such as 115%120% of annual peak demand which in turn was a heuristic representation of a loss-of-load expectation level equivalent to, for example, an expected reliability loss during no greater than one peak load day in 10 years. In recent years, there is an increasing trend to include the ability to supply load during challenging ramping conditions. This is rooted from the fact that with more variability and uncertainty in the system, mainly driven by wind and solar penetration, the grid may still face reliability issues in term of insufficient ramping capability, even though there is enough generation capacity it the system. While variability and uncertainty have typically been managed in the operational time frame, planning decisions may need to be made to ensure that there is sufficient flexibility available from a system’s resources that can be called on in operation, in the same way that capacity must be planned for to be called upon when needed in operations. There may be a need for resource adequacy analysis, which has traditionally been

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focused on ensuring sufficient capacity to meet peak demand, to also consider flexibility issues. The CAISO, under mandate from the California Public Utilities Commission, developed a new resource adequacyrelated procurement target. Load Serving Entities (LSEs) are required not only to procure sufficient capacity to meet forecasted peak load as they currently do but also to meet additional flexibility requirements with their capacity. The requirement is split into three categories: base flexibility, peak flexibility, and super peak flexibility, which are determined based on the time of the day and as a percentage of largest and second-largest 3-hour net load ramps, contingency reserves, and a peak demand factor. Once the total procurement requirement has been calculated, the next step is to distribute the total requirements among each LSE based on the contribution of the LSE to the underlying components. The effective flexible capacity (EFC) is calculated for each resource based on their capability to ramp in 3 hours. The contribution from quick start units is their maximum ramp from cold start in 3 hours, whereas for longer start units, the EFC is calculated as the 3-hour ramping capability when the resources are online and at minimum generation (even if these are not online when ramps happen, it is assumed they would be if the ramps were extreme cases). Each LSE must procure sufficient EFC to meet their seasonal requirement and also ensure that the capacity is dispatchable in the day-ahead and intraday markets. The FRAC-MOO process, originally useful to identify flexibility needs, is currently under revision to ensure it procures the right type of flexibility. This may include more detailed consideration of minimum generation levels, overgeneration issues when solar power is high and understanding what a generator is likely to be doing when its flexibility is needed. In CAISO the EFC for each resource is calculated. The EFC quantifies the effective MW a resource contributes toward avoiding reliability events caused by incapability to meet the ramping needs. For conventional generation units the EFC is calculated with the following formula [8]: 8 min NQC 2 Pmin ; T 3 RRaverage ; > > > < for SUT . 90 minutes or P # 0 min EFC 5 (4.1) > min NQC; P ; 1 ðT 2 SUT Þ 3 RR > min average > : for SUT # 90 minutes and Pmin . 0

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where NQC is the unit’s net qualifying capacity, which equals to the maximum output the unit can operate for 4 consecutive hours; Pmin is the minimum capacity of the unit, which is energy storage or demand response (DR) resource (DRR) when its value is less than 0; T is the ramping period, which is set to 3-hour in CAISO; RRaverage is the average ramp rate; and SUT is the start-up time. n o FlexibilityNeedMTHy 5 Max 3RRHRx MTHy (4.2) 1 Max MSSC; 3:5%E PL MTHy 1 ε where MTH FlexibilityNeed is the flexibility capacity ny is the yth month, o need, Max 3RRHRx MTHy is the maximum 3-hour ramp rate in month y, MSSC is the most severe single contingency, E(PL) is the expected peak demand, and ε is the error adjustment item and represents the load forecasting errors. 4.2.3.2 Flexible ramping products To address the operational challenges of maintaining system balance, especially the increasing need of generation ramp capacity, two major RTOs/ ISOs in the United States—CAISO and Midcontinent ISO (MISO)— have proposed a new market design called flexible ramp products (FRPs) [9,10]. The FRPs include both flexible ramp-up (FRU) and the flexible ramp-down (FRD) capabilities. The FRPs are a new market design to address the operational challenges of maintaining balance. It is expected that the implementation of FRPs in the electricity markets can reduce the chance for ramp shortages and price spikes and maintain reliable system operations. Generally, the FRPs are provided from conventional generation such as coal and natural gas units, because their generation outputs can be more easily dispatched. Renewable energy resources such as wind and solar are deemed not suitable for providing ramping service because their output is more uncertain and reducing this uncertainty relies on accurate forecasting. This feature makes it difficult to be relied upon by the system operator. However, in recent years, with the development of advanced forecasting [1113] and control [14] technologies, the generation output and ramping capability of wind turbines or PV panels can be better controlled to meet the system dispatch requirements. Thus it is possible to allow renewables to provide FRPs in modern electricity markets. In order to integrate the FRPs into the electricity market, there is a need to set up the system-wide ramp capacity requirement (RCR), which is calculated before the resource dispatch process. It represents the

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expected system net load movement between the current dispatch interval and the posttime (e.g., 10 minutes) beyond the dispatch interval. The RCR includes both ramp-up and ramp-down capacity requirements. They are composed of two parts: the one captures the forecasted net load variability and the other captures the forecasted net load uncertainty, as shown in the following equation: 8 9 < NDt1m 2 NDt 1 UTt1m ; 0= |ﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄ} URRtSystem 5 max |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} (4.3) : ; Uncertainty Variability

8 9 < NDt 2 NDt1m 1 DTt1m ; 0= DRRtSystem 5 max |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} |ﬄﬄﬄ{zﬄﬄﬄ} : ; Uncertainty

(4.4)

Variability

System

System

(DRRt ) is the system-wide up (down) ramping capawhere URRt bility requirement at t, m is the time beyond the current dispatch interval, NDt is the net demand at t, and UTt1m (DTt1m) is the uncertainty of the net load ramp-up (down) at t 1 m. The variability part of the RCR is calculated by the difference in net load forecasting between time t 1 m and t. The uncertainty part of the RCR can be calculated based on historical data using statistical approaches. In MISO the uncertainty part is calculated from the historical net load probability distribution function using the Gaussiansigma rule, that is, 2.5 standard deviations for 99% confidence level [15]. An alternative method to integrate FRPs is adopting the ramp demand curves (RDCs), which are defined to represent the value for the ramp capability service. They provide a mechanism for limiting the clearing of ramp capacity and determining the clearing price when ramp capacity is in short supply. The RDCs include both the upward and downward flexible ramping demand curves. They generally have a stair-step shape with several MW-price segments. In general, the amount of unexpected ramp procurement will be less if the supply price is lower, and vice versa. A sample RDC is shown in Fig. 4.3. Two slack variables su and sd, which represent the FRU and ramp-down surplus respectively, are constructed as a ramp DR (i.e., ramp requirement reduction) in response to the price. The objective function of the economic dispatch problem with FRPs model is: ( NG X X X Min Ci ðPi Þ 1 CiRU ðRUi Þ 1 CiRD ðRDi Þ i51

1

X iAISP

iAIRU

CiSP ðSPi Þ 1

iAIRD

X iAINS

CiNS ðNSi Þ 1

X s

) (4.5) UPs Usus 1 DPs Usds

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Figure 4.3 Ramp demand curves.

where the symbols are defined as follows: NG is the number of generation resources; NL is the number of transmission lines; IRU is the set of resources providing regulation up service; IRD is the set of resources providing regulation down service; ISP is the set of resources providing spinning reserve service; INS is the set of resources providing nonspinning reserve service; s is the segment of the ramping capacity demand curve; Pi is the generation MW of resource i; RUi is the cleared regulation up capacity of resource i; RDi is the cleared regulation down capacity of resource i; SPi is the cleared spinning reserve capacity of resource i; NSi is cleared nonspinning reserve capacity of resource i; Ci ðPi Þ is the energy cost function for resource i; CiRU ðRUi Þ is the regulation up cost function for resource i; CiRD ðRDi Þ is the regulation down cost function for resource i; CiSP ðSPi Þ is the spinning reserve cost function for resource i; CiNS ðNSi Þ is the nonspinning reserve cost function of resource i; UPs is the price at sus in the ramp capacity demand curve; DPs is the price at sds in the ramp capacity demand curve; sus is the FRU surplus; and sds is the FRD surplus. The objective function in (4.5) contains seven terms, including energy cost, regulation up cost, regulation down cost, spinning reserve cost, nonspinning reserve cost, ramp capability demand price, and transmission line violation penalty cost. Since no bids/offers for FRC are submitted, the FRU and FRD are priced with the opportunity costs. The ramp capability demand price term in (4.5) is formulated with the RDC in Fig. 4.3, wherein the surplus variables are the decision variables of the model. The constraints of the model are shown as follows: System-wide power balance constraint:

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Pi 2

iAIGen

X

Di 2 Loss 5 0

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(4.6)

iAILoad

where IGen is the set of generation resources, ILoad is the set of load resources, Di is the load at bus i, and Loss is the system loss. Transmission constraints: N X

0 PTDFl2i 3 ðPi 2 Di Þ # Pl0 ; for lAfall linesg

(4.7)

i51 0 is the power factor shift factor where N is the number of buses, PTDFl2i to line l from bus i at normal state, and Pl0 is the normal limit of transmission constraint l. Market-wide regulating up reserve requirement: X RUi $ REQRU (4.8) iAIRU

where REQRU is the market-wide regulating up reserve requirement in the system. Maximum and minimum requirements for market-wide regulating down reserve requirements: X REQRD RDi $ REQRD (4.9) MAX $ MIN iAIRD RD where REQRD MAX (REQMIN ) is the market-wide maximum (minimum) regulating down reserve requirement in the system. Market-wide regulating up plus spinning reserve requirement: X X RUi 1 SPi $ REQRU 1 REQSP (4.10) iAIRU

iAISP

where REQSP is the market-wide spinning reserve requirement in the system. According to ancillary service cascading, regulation up can be used as spinning reserve after the regulation up requirement is met. Market-wide regulating up plus spinning plus nonspinning reserve requirement: X X X RUi 1 SPi 1 NSi $ REQRU 1 REQSP 1 REQNS (4.11) iAIRU

iAISP

iAINS

where REQNS is the market-wide nonspinning reserve requirement in the system.

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System-wide requirements for FRU and FRD procurements: X RiUP 1 sus $ URRSystem

(4.12)

iAIFRU

X

RiDN 1 sds # DRRSystem

(4.13)

iAIFRD

DRRSystem # sds # 0 # sus # URRSystem

(4.14)

where IFRU (IFRD) is the set of resources providing flexible ramping up (down) capacity, RiUP (RiDN ) is the cleared up (down) ramping capacity at resource i, sus (sds ) is the FRU (FRD) surplus at segment s of the demand curve, URRSystem (DRRSystem ) is the system-wide up (down) ramping capability requirement, and Ri;tDN is nonpositive. Capacity limit constraints for eligible resources: Pi 1 RUi 1 SPi 1 RiUP # PGiMAX

(4.15)

Pi 2 RUi 1 RiDN $ PGiMIN

(4.16)

where PGiMAX and PGiMIN is the maximum and minimum capacity of resource i, respectively. Note that RUi is greater than or equal to 0 and RiDN is less than or equal to 0. Resource ramping capability constraints: Pi;t 2 Pi;t21 # Lt UUPRATEi;t

(4.17)

Pi;t 2 Pi;t21 $ 2 Lt UDNRATEi;t

(4.18)

where Lt is the length of time interval t, UPRATEi, t is the ramp-up limit of resource i at time t, and DNRATEi, t is the ramp-down limit of resource i at time t. 4.2.3.3 Energy storage market modeling A number of energy storage techniques currently exist in the wholesale electricity market. The most common one is the pumped storage hydro (PSH), which is over than 18 GW capacity in the United States. The PSH can participate into the market in either generation participation mode or load participation mode. When in generation model, it is usually allowed to provide regulation and spinning reserves. When in load model, it is generally not allowed to provide regulation and/or spinning reserves, because PSH is generally installed with fixed speed pumps [16]. Some other energy storage techniques include battery (particularly lithium-ion), flywheels, and compressed

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air. In recent years, the dominant gird-connected energy storage technique is the lithium-ion battery. In the United States the Federal Energy Regulatory Commission (FERC) issued the Order 841 to direct all the RTOs/ISOs to make changes to their market design and market clearing software to enhance the participation of electric storage into the market. According to the requirement of this order, the ISOs must develop a unique participation model for energy storage resources (ESRs) so that they can act as either a generator or a load, and they can participate in energy, ancillary service, and capacity markets. Although the order requires the ISOs to account for the physical parameters of ESRs, it does not make a hard requirement on whom should be responsible for the state-of-charge (SOC) management. As a result, different markets will develop their own SOC management strategies. Some ISOs would require the ESR asset owners to provide the cost/quantity offer curves and self-manage the SOC of the ESRs. Other ISOs would prefer to manage the SOC through the system operator, that is, the ISO makes scheduling decisions and optimizes the SOC levels to minimize the system’s operating costs. In what follows, we will give the detailed formulations for the two SOC management strategies on the ISO’ perspective. When the ESR asset owner self-manages the SOC, the day-ahead security-constrained unit commitment (SCUC) formulation in the ISO will incur additional equations that correspond to other resources and system constraints. For the sake of simplicity the following formulation only includes equations that are related to the subset of ESRs. The objective function is to minimize the system operation costs ( ) X X REG REG G L SPIN SPIN Min (4.19) c i PG i 2 c i PL i 1 c i Ri 1 c i Ri i

i

Subject to the following constraints: Power balance constraints: X ðPGi 2 PL i Þ 1 G other 5 Load

(4.20)

i

Cleared energy and reserve constraint: X max ðPG i 2 PL i Þ 1 Ri # uG i PG i

(4.21)

i

X i

ðPG i 2 PL i Þ 2 Ri $ 2 uLi PL max i

(4.22)

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Ramp constraints for reserve deployment: Ri # T 3 RAM P i

(4.23)

Resource limit constraints: PG i $ 0; PL i $ 0; Ri $ 0

(4.24)

When the SOC of ESRs is managed by the ISOs instead of the ESR owners, the SCUC model is more complicated. We assume that ESR can provide energy, regulation, and spinning reserves in the market. For simplicity the following formulations only include equations related to the subset of ESRs. The objective function of SCUC when ISO manages the SOC is: Minimize " X X ESR ESR ESR CG ESR P 2 CL L i;t i;t i;t i;t tAT

iAESR

X ESR ESR ESR 1 CREGESR i;t REG i;t 1 CSPIN i;t SPIN i;t

#

(4.25)

iAESR

1 υðGDi 2 LDi Þ Subject to: Power balance constraints: X X ESR Pgi;t 1 P ESR 2 L 5 Load t i;t i;t iAG;i= 2ESR

(4.26)

iAESR

Define four variables to represent the reserves provided by ESR: r G;1 i;t is the upward reserves provided by ESR in generation mode, r G;2 is the i;t downward reserves provided by ESR in generation mode, r L;1 is the i;t upward reserves provided by ESR in load mode, r L;2 is the downward i;t G;2 L;1 L;2 reserves provided by ESR in load mode, and r G;1 i;t ; r i;t ; r i;t ; r i;t $ 0. The capacity limits constraints with reserves: ESR ESR r G;1 i;t 1 P i;t # ui;t M Di;t

(4.27)

ESR r G;2 i;t # P i;t

(4.28)

ESR r L;1 i;t # L i;t

(4.29)

ESR ESR r L;2 i;t 1 L i;t # ð1 2 ui;t ÞM Di;t

(4.30)

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Note that Eqs. (4.27)(4.30) ensure that the ESR could be either in generation mode or in load mode. When it is in generation mode (uESR i;t 5 1), the right-hand side in (4.30) is zero. Since all the variables are L;2 L;1 greater than or equal to 0, we have L ESR i;t 5 r i;t 5 r i;t 5 0. When it is in generation mode (uESR i;t 5 0), the right-hand side in (4.27) is zero, and G;1 G;2 there exists P ESR 5 r i;t 5 r i;t 5 0. i;t Upward reserve equation: G;1 L;1 ESR REG ESR i;t 1 SPIN i;t 5 r i;t 1 r i;t

(4.31)

Downward reserve equation: G;2 L;2 REG ESR i;t 5 r i;t 1 r i;t

(4.32)

Energy ramp-down constraints: ESR ESR ESR 2 L 2 L P ESR 2 P # RRi 3 I DA i;t21 i;t21 i;t i;t

(4.33)

Energy ramp-up constraints: ESR ESR ESR ESR # RRi 3 I DA P i;t11 2 L i;t11 2 P i;t 2 L i;t

(4.34)

Reserve ramp constraints: REG ESR i;t # RR i 3 T REG

(4.35)

SPIN ESR i;t # RRi 3 T SPIN

(4.36)

SOC i;1 5 SOC Initial

(4.37)

SOC i;24 5 SOC End 1 GDi 2 LDi

1 ESR SOC i;t11 5 SOC i;t 1 I DA 2 P ESR 1 ηL i;t η i;t

(4.38)

SOC constraints:

Reserve activation constraints:

t X 1 G;2 L;2 SOC i;t 1 κI DA r i;t 1 ηr i;t # SOC max i η 0 t 51

t X 1 G;1 L;1 r i;t 1 ηr i;t SOC i;t 2 κI DA $ SOC min i η t 0 51

(4.39)

(4.40)

(4.41)

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The constraints (4.40) and (4.41) ensure that in the worst scenario when the activation of all reserves is in one direction, the ESR still has sufficient capability to provide reserves.where the meaning of the symbols is shown as follows: T is the time horizon; CG ESR is the cost when the i;t ESR ESR is on generation mode; CL i;t is the cost when the ERS is on load mode; P ESR is the cleared quantity of generation when the ESR is on i;t generation mode; L ESR is the cleared quantity of load when the ESR is i;t on generation mode; CREG ESR is the offer for providing regulation i;t reserve; CSPIN ESR is the offer for providing spinning reserve; REG ESR is i;t i;t ESR the cleared quantity of regulation reserve; CSPIN i;t is the cleared quantity of spinning reserve; υ is the penalty price for SOC violations; GDi is the positive deviations of SOC at the last time interval (MWh); LDi is the negative deviations of SOC at the last time interval (MWh); Pgi;t is the generation output from non-ESR resources; Loadt is the system demand at hour t; uESR is the binary variable to indicate the ESR mode. 1 refers i;t to generation mode, and 0 refers to load mode; M Di;t is the maximum discharge limit (MW); M C i;t is the maximum charge limit (MW); RRi is the ramp rate of ESR i (MW/min); I DA is the day-ahead resolution (usually 1 hour); T REG is the regulation reserve activation time (hour); T SPIN is the spinning reserve activation time (hour); SOC Initial is the initial SOC; SOC End is the desired SOC at the last hour; SOC i;t is the SOC for ESR i at time t (decision variable; MWh); η is the charging/discharging efficiency; κ is the assumed percentage of reserve deployment in the SOC throughput constraint. It equals to 1 by default; SOC max is the maximum i SOC; and SOC min is the minimum SOC. i 4.2.3.4 Demand response resources market modeling From the system operator’s perspective, there are two types of DRRs: • Interruptible load (Type I DR). The Type I DRRs are capable of supplying a specific quantity of energy through physical load interruption. These generally include curtailable or interruptible loads. In the wholesale electricity market, they are modeled as generation resources with block loaded for a specific quantity of energy. • Controllable load (Type II DR). The Type II DRRs can provide energy and/or operating reserves over a dispatchable range through controllable load or BTM generation. In contrast to the Type I DRRs, they have more flexibility in that they can adjust the load consumption based on system needs. In the wholesale electricity markets, the Type II DRRs are modeled as generations with (negative) dispatchable ranges.

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Figure 4.4 Type II DR is modeled as a generator. DR, Demand response.

The Type II DRRs are modeled as generators in the electricity markets. Different from conventional generators, they have negative output ranges between the minus of the maximum capacity and zero. For instance, the daily load curve of a Type II DRR is shown on the left half of Fig. 4.4. The maximum power usage of the resource is 4 MW. In most hours of the day the energy usage is between 3 and 4 MWh. The daily energy used is 76.8 MWh. The corresponding generator model is shown on the right half in Fig. 4.4. The maximum and minimum generation capacity is 0 and 24 MW, respectively. The total daily energy generation is 276.8 MWh. When the net output of the generator is 23 MW, it means the DRR is supplying 1 MW of DR, which is the difference between the net output (23 MW) and the minimum limit (24 MW). The DRRs will incur additional items to the objective function and additional constraints to the SCUC in the electricity market. In what follows, we only include the equations related to the subset of Type II DR. The objective function is: Minimize " # X X CG DRR2 P DRR2 1 CSU DRR2 u1DRR2 i;t i;t i;t i;t tAT iADRR2 (4.42) X 1α ðDailyEnergyViolationi Þ iADRR2

Subjective to: Generation capacity limits for Type II DRRs: 2P DRR2;Max uDRR2 # P DRR2 #0 i i;t i;t Ramp-up and ramp-down constraints:

(4.43)

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DRR2 P DRR2 # RRi 3 I DAC i;t21 2 P i;t

(4.44)

P DRR2 2 P DRR2 i;t i;t21 # RR i 3 I DAC

(4.45)

Energy balance on each day: I DAC 3

24 X

P DRR2 1 DailyEnergyViolationi 5 2 DailyEnergyi i;t

(4.46)

t51

where the meaning of the symbols is shown as follows: T is the time interval; CG DRR2 is the cost of Type II DRR i at time t; P DRR2 is the i;t i;t DRR2 cleared output of Type II DRR i at time t; CSU i;t is the start-up cost for Type II DRR i at time t; u1DRR2 is the binary decision varii;t able to indicate if resource starts up; uDRR2 is the binary decision varii;t able to indicate if resource i is committed; α is the penalty cost; DailyEnergyViolationi is a decision variable to control the daily energy violation for DRR i; P DRR2;Max is the maximum capacity for Type II i DRR i; RRi is the ramp rate of resource i; I DAC is the time interval for day-ahead commitment; and DailyEnergyi is the daily energy consumed for Type II DRR i. Based on the previous equations, the input parameters for Type II DRR needed for the simulation include • cost of Type II DRRs to provide energy service • start-up cost • ramp rate (MW/min) • maximum capacity (MW) • daily energy used (MWh) As mentioned earlier, the Type I DRRs are also modeled as generators but with block loaded for a specific quantity of energy. A Type I DRR has only two output states (either “on” or “off”), whereas a Type II DRR can deliver output over a continuous range of values. Again, we only include the equations related to the subset of Type I DRRs in the following formulations. Objective function: Minimize: " # X X (4.47) HourCurtOffer DRR1 P DRR1 1 CSDDRR1 u1DRR1 i;t i;t i i;t tAT

iADRR1

Subject to the following constraints:

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Commitment availability for Type I DRR i at time t: uDRR1 # CommitAvailabilityDRR1 i;t i;t

(4.48)

Hourly blocked capacity quantify to be curtailed: P DRR1 5 uDRR1 3 MWAvailabilityi;t i;t i;t

(4.49)

where the meaning of the symbols is shown as follows: HourCurtOffer DRR1 is the hourly curtailment offer for Type I DRR i i;t DRR1 at time t; P i;t is the cleared quantity of Type I DRR i at time t; CSDDRR1 is the shut-down cost of Type I DRR i; u1DRR1 is the i;t i;t binary decision variable to indicate if resource is shut down; uDRR1 is i;t the binary decision variable to indicate if resource is on service; CommitAvailabilityDRR1 is an input binary variable, which is used to i;t control the hours to allow curtailment; and MWAvailabilityi;t is the quantity of curtailable capacity for resource i at time t. Note that the ramp-up capability on a Type I DRR equals to zero, so there is no need of ramping constraints in the model. Based on the earlier equations, the input parameters for Type I DRR needed for the simulation include • availability of curtailment in each hour • quantity of curtailable capacity in each hour • shut-down cost ($) • price to curtail the load ($/MW)

4.2.4 Running the electricity market with 100% renewables 4.2.4.1 Motivations Increasing amounts of DERs—such as solar, storage, energy efficiency, and demand management—have sparked worries in the gird operators over grid reliability and balancing issues. Grid-scale wind and solar plants across the country are spreading swiftly down the electric power value chain, as grids in many regions have become increasingly decentralized. In today’s gird operation paradigm, wind and solar power are labeled as “nondispatchable” compared with conventional generators, since their output is with high variability and uncertainty. While their marginal operation cost is low, integration of these resources can be challenging for grid operations, which must ensure that generation and load remain in dynamic balance and power quality is not compromised.

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Many utilities and independent system operations are trying to prepare the grid for rapid growth of renewable energy, and some have successfully demonstrated how large amounts of DERs can be integrated and sustain system reliability as successfully as conventional generation plants. The CAISO is a leading example. On May 16, 2017 the CAISO hit a new renewable energy record with renewables serving nearly 42% of electricity demand [17]. Similar is for the Southern Power Pool (SPP) system, at 3:45 a.m. On March 16, 2015, wind accounted for 13,928.94 MW of the system’s total load of 22,998.71 MW, a penetration level of 60.56% [18]. In EriGRID an Irish leading utility, the percentage of wind power could account for 60% of electricity usage over a typical day [19]. In Colorado, Senator Matt Jones and Representative Mike Foote submitted a bill to Colorado General Assembly in January 2018 to require 100% renewable energy by 2035 [20]. In July 2017 Hawaii Public Utilities Commission approved Hawaiian Electric Companies’ Power Supply Improvement Plan, which contains the recipe for hitting the state’s 100% renewables mandate by 2040 [21]. In order to remove barriers to the participation of electric storage resources and DER aggregations in the capacity, energy, and ancillary service markets, the Federal Energy Regulatory Commission opened a rulemaking for the nation’s six grid operators in 2016 [22] and issued the final rule on February 28, 2018 [23]. In PJM, it has been recognized that inverter-based resources (IBRs) can participate into the wholesale and capacity markets and provide synchronized reserves, but their dispatch parameters are different from synchronized resources and the corresponding rules need to be updated. In CAISO, new market designs such as the flexible ramping products and the flexible resource adequacy and must offer obligation (FRAC-MOO) have been implemented to address the challenges from increased penetration of renewables. In July 2017 the Hawaiian Electric Company secured regulatory approval for a plan to power multiple island grids with 100% green energy by 2045 [24]. Though many ambitious renewable energy targets have been set up, none of today’s utilities and system operators are designed to operate their girds under all (100%) or extremely high (greater than 80%) IBRs. Traditional rules and concepts for operating thermal resourcebased system do not apply to systems with all IBRs. This section sought to close the gaps between the existing grid and market design and the new operation paradigm in the all IBRs systems, as demonstrated in Fig. 4.5. In addition, we will propose corresponding models to illustrate how those

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The system has all zero-marginal cost resources. What is the bidding curve like and will minimizing cost still be the objective?

Increased uncertainty and variability because inverter-based resources are more dependent on the weather.

100% or extremely high inverterbased resources system operations

Understand the control strategy of the inverter-based resources.

Research on how to provide ancillary services by inverter-based resources.

What changes should be made to existing market rules which were built under thermal generationdominated systems?

Figure 4.5 Bridging the gaps in perspectives. Table 4.1 Features of gird-following and grid-forming inverters. Grid-following inverters Grid-forming inverters

• • • •

Current source (PQ bus) Follow the grid Inject active and reactive power Fast response to the intermittent irradiation levels with no buffers

• Voltage source (PV bus) • Set grid voltage and frequency • Provide active and reactive power to the load via voltage control • Slow response due to large inertia

resources are dispatched to meet the generation and demand balancing requirement. 4.2.4.2 Bulk electric power grid dispatch in the all inverter-based resources systems Two popular types of inverters connected to the transmission and distribution systems are the grid-following and grid-forming inverters, and their control schemes are different. A grid-following inverter can follow the desirable set points for its real power (e.g., MPP for renewables) under normal conditions, and within the maximum power limit its real power can be changed for power sharing purposes upon a disturbance. A gridforming inverter should have bidirectional power-transfer capability (e.g., an inverter connects to a battery or microgrid system). A comparison of the features of the two inverters is shown in Table 4.1. From an ISO or the utility’s perspective, an inverter-based facility can be either front-of-the-meter (FTM), which normally refers to the utilityscale plants (such as PV, wind and storage) that generate power and feed

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Battery storage

Combined heat and power (CHP)

Community solar PV

Bulk grid reliability operations

The distributed energy resource management system (DERMS)

Electrical and thermal storage

Demand response Whole electricity market operations

Rooftop solar

Electric vehicles

Microgrid

Figure 4.6 Inverter-based resources that are located behind the meter.

into the grid to supply a utility with energy, or BTM, which refers to the inverter-based systems that produce power intended for on-site use in a house, office building, or other commercial facility. The BTM inverterbased systems are usually on the owner’s property, not on the side of the electric power grid/utility. The strategies to dispatch the FTM- and BTM inverterbased resources are different. The FTM resources can participate into the wholesale electricity market directly and provide energy and ancillary services. The BTM resources, however, are treated as negative loads on zonal levels based on the generation output forecasting. IBRs that are located behind the meters may include rooftop solar, electric vehicles, microgrid, DR, electrical and thermal storage, community solar PV, combined heat and power (CHP) plants, and battery storage, as shown in Fig. 4.6. These resources can be controlled and operated by the DER management system. In addition, the BTM resources can interact with the bulk transmission systems in two different layers. First, they can provide reliability services to the grid through active and reactive power dispatch and scheduling, phase balancing, and loss minimization. Second, they can provide electricity market services through supply of capacity, energy, ancillary services, and frequency regulation. 4.2.4.3 Increased uncertainty and variability Since the generation of renewable energy highly depends on the weather, the challenge to operate a 100% IBRs (mostly are renewables like wind

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and solar) system is much greater than to operate today’s gird. Forecasting will play a significant role in the operation of the 100% IBRs system. In order to participate into the wholesale market, the utility-scale IBRs should either provide the generation forecasting to the ISO or provide related meteorological data to let the ISO do the forecasting. For example, the following information are required to be provided by the New York ISO: • Provide the plane of array irradiance and back panel temperature to the ISO. • Employ a sufficient quantity of measuring stations such that all panels are within a certain number of kilometers (e.g., 5 km) of a measuring station. • The frequency of data transmitting must be sufficient (e.g., at least once every 30 seconds). 4.2.4.4 Increased complexity for the controls of utility-scale inverterbased resources The control strategy of a utility-scale PV or wind plant, which includes a combination of inverters as well as many other devices, may be quite different from that of a synchronized generator. This session does not aim at designing the control model of the plant-level inverters. Instead, we will discuss the control functions of the plant and how they can be adapted to the operation procedure of the grid. According to Ref. [25], a typical IBR plant has the following plant-level control functions: • load-following control; • real power output curtailment of the solar power plant when required so that it does not exceed an operator-specified limit; • ramp-rate controls to ensure that the plant output does not ramp up or down faster than a specified ramp-rate limit, to the extent possible; and • frequency control (governor-type response) to lower plant output in the case of an overfrequency situation or increase plant output (if possible) in the case of an underfrequency situation. These control functions can be transferred to corresponding dispatch functions in the operation procedure of the all or extremely high IBRs system. Table 4.2 shows the control functions and dispatch functions for utility-scale IBRs. The fact that utility-scale inverters can provide loadfollowing control, real power output curtailment, and ramp-rate control makes it possible to run unit commitment and economic dispatch models in the all IBRs systems. Thus lots of concepts in running today’s

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Table 4.2 Control functions versus dispatch functions for utility-scale inverter-based resources. Plant-level control Dispatch functions functions

Real power output curtailment Ramp-rate controls Frequency control

Control the real power output of the plant for ED Ramping limit constraints in the UC, ED, and AGC models Different from synchronized generators, the frequency of inverters can be recovered very quickly after a disturbance or contingency. New AGC design is required, as is detailed in Chapter 3, Renewable energy integration and system operation challenge: control and optimization of millions of devices

AGC, Automatic generation control; ED, economic dispatch.

synchronized electric power grid can be used for running the all IBRs systems—for example, dividing the operating procedure into day-ahead unit commitment and real-time economic dispatch processes, conducting energy and ancillary services cooptimization during the dispatch, and adopting new market design such as flexibility ramping products to increase the system’s operation flexibility. The frequency control and automatic generation control (AGC) operation of inverter-based plants are significantly different from those of synchronized machines. The voltage and reactive power control of inverters is important for maintaining the grid operation reliability in the 100% inverter system. In today’s electric power grid, voltage and reactive power are coordinated by three different parties: (1) reliability coordination, which monitors pre- and postcontingency voltage and reactive power and manages them by adjustment of transfers, generation redispatch, and unit commitment; (2) transmission operators, which monitor, analyze, and control voltage and reactive power flows as stated in NERC Standard VAR-001; and (3) generator operators, the primary responsibility of which is to improve voltage and reactive power flow controls as required by NERC Standard VAR-002. We will discuss how frequency control and AGC operation will be shifted in a later section in this chapter. 4.2.4.5 All zero marginal cost resources The pricing mechanism in today’s wholesale electricity market such as LMP and market clearing pricing reflects the marginal cost to supply

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energy or provide ancillary services. Typical marginal price for energy in the CAISO is about $50/MWh. In the all inverter system, energy and ancillary services are provided mostly by renewable energy such as wind, solar and energy storage, the marginal cost of which is close to zero. While many publications have discussed this issue from the market design or the utility’s revenue sufficiency perspectives [25,26], this session mainly focuses on the operation side for a system with all zero marginal cost resources. SCUC and SCED are the main models to commit and dispatch resources in today’s electric power grids, in which the way a synchronous generator is committed and dispatched depends on many factors. For example, a coal unit is usually much cheaper but has longer minimum run time and minimum shut-down time than an oil unit. Thus the coal unit is more suitable to serve baseload, and the oil unit is usually treated as fast starting resource to serve peak load. In the 100% inverter system, all the resources are with close to zero marginal cost, and the physical operation requirements such as minimum on time, minimum off time, start-up time, and shut-down time are neglectable. The SCUC and SCED models can still be applied to commit and dispatch the IBRs to balance load and meet transmission line requirements. Since the marginal costs of the resources are close to 0, the magnitude of the LMPs should be trivial. However, the resource owners can recover the variable operation cost through capacity market and/or scarcity price. In addition, it is expected that ancillary services will play a significant role in the 100% inverter system, and the price to provide ancillary services might be higher than the price to provide energy. 4.2.4.6 Unit commitment and economic dispatch for up to 100% renewables A 100% renewable energy power system has been proved to be feasible, and has been realized in some places. While lots of previous work was focused on how individual inverters impact the operation of the grid, less was focused on how the balancing paradigm will be changed for the whole system. This section does not intent to propose a totally new operating diagram for the 100% renewables system1. Instead, we focus on how the existing unit commitment and economic dispatch framework might be changed to run a 100% renewables power system. In addition, we will also discuss the new method to determine the reserve requirements and 1

From the system balancing perspective, it is more general to use “100% renewables” rather than “100% inverter-based resources”. But they refer to the same thing in this report.

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the new design of frequency response mechanism in the following sections. The SCUC and SCED models are the key models in today’s electricity markers to manage the resources and balance the demand. The SCUC is used in the day-ahead and intra-day market to meet the forecasted demand and ancillary service requirements subject to transmission constraints. The SCED is used to achieve real-time reliable grid operation at the lowest costs. With more IBRs being integrated into the grid, the operation paradigm will have significant changes. The IBRs have almost zero start-up and shut-down time, zero minimum generation level, zero minimum on and off time, and almost zero start-up and shut-down costs. These features are significantly different from those of conventional thermal generators. When a system has 100% IBRs, the UC is no longer required for the system operations. In today’s market operations, fast start units can be called up during the intraday to meet the load imbalance caused by uncertainty. In the 100% IBR system the logic to call up quick start units should be different because all the resources can be turned on and off very quickly. The SCED model should also have significant changes. During intervals where only the zero-cost resources need to provide energy, there is no cost for SCED to minimize. The optimization engine may randomly select resources to be curtailed. As a result, there may be multiple solutions to the SCED model. It is important to identify new rules for determining which units are selected for curtailments. 4.2.4.7 Determination of reserve requirements In a power system where the demand is 100% supplied by renewables, solar and wind resources form the baseload. In the case of the scenario when the sun is mostly blocked and the wind is weak, the system must build sufficient extra capacity so that there is enough capacity to power the grid under all operating scenarios. This requires building variable energy resource (VER) capacity well beyond the reserves required from thermal plants. With so much extra solar and wind on the grid, the system operators have to deal with overgeneration when the weather conditions are optimal, and VER curtailment is unavoidable. Besides overbuilding capacity, other measures to ensure sufficient reserves in the grid are to build a whole lot of storage and to improve the regional import and export of electricity. Operating reserves refer to the generating capacity available to meet demand in case the scheduled or forecasted supply of power is disrupted.

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A shortage of deployable active power reserve may cause frequency response deterioration in the system with all inverters. This may not only lead to underfrequency load shed but also cause transient stability issue in the system. In the steady state, VREs increase the variability and uncertainty of power supply and hence impact the amount of operating reserves that need to be held to maintain system reliability. It is a challenge for system operators to determine how much each reserve product needs to be increased with high VRE penetration levels. In particular, when the system has 100% renewable energy, the grid operators should find out the answers to the following key questions: • How to determine the requirement for each reserve product? • Who provides those reserves? • Can IBRs provide reserves? If yes, how much can be counted toward reserve? Traditionally, the operating reserves have been provided by conventional generators such as thermal and hydro units. In the 100% renewable energy systems, those reserves have to be provided by DR, energy storage, and other IBRs (such as wind and solar units). According to the FERC definition, the types of ancillary services and the associated timescales are shown in Table 4.3. In the US ISOs the system operating reserves mainly include the following categories: • Regulation reserves, which are used for the balancing of fast secondto-second and minute-to-minute random variations in load or generation. They generally include regulation up and regulation down reserves. • Regulation mileage reserve, which is a reserve product to measure regulation movement required by FERC Order 755. • Spinning reserve, also referred to synchronized reserve, is intended to help the system respond quickly to forced outages or other contingency events.

Table 4.3 Type of market products and the associated timescales. Market products Timescale

Reactive supply and voltage control Scheduling, system control, and dispatch Regulation and frequency response Synchronized operation reserve Supplemental operation reserve Energy imbalance

Seconds Seconds to hours Minutes Seconds to less than 10 min Greater than 10 min Hours

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•

Nonspinning reserve, also referred to supplemental reserve, is intended to help the system recover from unplanned contingencies and can be provided by offline generators. • Ramping product, or flexibility ramping capacity in MISO and CAISO electricity market, which allows the ISO to procure sufficient ramping capability via economic bids. In this section, we are going to discuss the reserve requirements in the system under two scenarios: (1) the system has sufficient thermal generators and has 100% renewable generation only in some hours of a day. (2) The system has only few (or none) thermal generators, and the demand is supplied by renewables almost all the time. The first scenario is an extreme case of today’s market operation paradigm. Four types of reserves are generally considered, including regulation up reserve, regulation down reserve, spinning reserve, and supplemental reserve. The requirement for each type of reserve is calculated as follows: • Regulation up reserve: We adopted the method used in SPP market to calculate the regulation up reserve. The regulation reserve is determined by four components: load magnitude, load variability, renewable energy resource magnitude, and renewable energy resource variability. The total regulation reserve requirement is calculated by the summation of each component times a coefficient, as shown in the following equation: RegRequp 5 aup LFðtÞ 1 bup ½LFðt 1 1Þ 2 LFðtÞ 1 c up RFðtÞ 1 d up ½RFðt 1 1Þ 2 RFðtÞ

(4.50)

where LF(t)is the load forecasting at time t; RF(t) is the renewable energy resource forecasting at time t; and aup, bup, cup, and dup are coefficients to determine the weight of each component. One typical value of aup, bup, cup, and dup is 1%, 1%, 5%, and 10%, respectively. • Regulation down reserve: The regulation down reserve requirement can be calculated in the following equation: RegReqdown 5 adown LFðtÞ 2 bdown ½LFðt 1 1Þ 2 LFðtÞ 1 c down RFðtÞ 2 ddown ½RFðt 1 1Þ 2 RFðtÞ

(4.51)

where aup, bup, cup, and dup are coefficients to weight each component.

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Spinning reserve: The spinning reserve is a 10-minute product in the electricity market. We set the spinning reserve requirement equal to the largest single contingency (in MW) in the system. • Supplemental reserve: Supplemental reserves must be able to become synchronized with the grid and ramp to a specified output level within 30 minutes. In the electricity markets, its requirement can be calculated based on historical data. For example, in ERCOT it is calculated by using the 70th95th percentile (depending on the risk of net load ramping) of hourly net load uncertainty (load minus the estimated uncurtailed total output from intermittent renewable resource) from the same month of the previous 3 years. In ISO New England, it is set as 50% of the secondlargest system contingency. In the second scenario, when there are few or no thermal generators in the system, all the reserves should be provided by IBRs or DRRs. One significant difference from the first scenario is that there is no need to have various types of reserve products. In conventional power systems, different types of reserves need to be defined because the thermal generators have different response time. For example, those units equated with AGC devices are qualified to provide regulation reserve, because they can respond to ISO’s set point signal in a few seconds. Units with longer start-up (or ramp-up) time may be qualified to provide spinning or supplemental reserves. However, the inverters have total different features than the conventional generators. They can respond to the set points quickly in a few seconds. In addition, they have very short start-up or ramp-up time. Considering these features of IBRs, we only propose two types of operating reserves to the system: • Load-following reserve: It refers to the capacity available during normal conditions for assistance in active power balance to correct future anticipated imbalance in the system. • Contingency reserve: It refers to the capacity available for assistance in active power balance during infrequent events that are more severe than balancing needed during normal conditions and are used to correct instantaneous imbalances. The load-following reserve should consider the variability and uncertainty of the load and renewable energy generations. There are three central needs for operating reserves: 1. hold capacity now to meet the variability that occurs within the scheduling time interval (intrainterval variability need);

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Figure 4.7 Net load is negative when VER penetration level is high.

2. hold capacity now to meet the variability that occurs beyond the scheduling time interval (interinterval variability need); and 3. hold capacity now to meet the uncertainty that occurs within and beyond the scheduling time interval (uncertainty need). Normally the requirement of load-following reserve is determined based on the system net load. In a system with very high VER penetration, the net load can be negative in most hours, as shown in the example in Fig. 4.7. Thus it would be better to use the VER forecasting data to calculate the reserve capacity need for intrainterval variability, interinterval variability and uncertainty. The contingency reserve requirement can be defined as the capacity of the largest inverter in the system, in case it is lost under an event. Since the respond time of inverters is very fast, both online and offline inverters can provide the contingency reserve. 4.2.4.8 New frequency response mechanism design In the current industry practice the balancing and frequency control occur over a continuum of time using different resources, as shown in Fig. 4.8 [27]. The primary control is also called frequency response, which occurs within the first few seconds following a change in system frequency (disturbance) to stabilize the interconnection. The frequency response can be provided by the governor action of synchronized generators or system

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Quantum

Unit commitment mid-term scheduling Generation redispatch

Inertial response Primary control

Secondary control

Tertiary control

Timescale Seconds

Minutes

Hours

Figure 4.8 Control continuum in the power industry.

load (such as motors). The secondary control maintains the minute-tominute balance of the system and is used to restore frequency to its scheduled value following a disturbance. The AGC is the major means to exercise the secondary control. By setting proper control schemes for inverters, the system’s frequency can be back to normal in just a few seconds after a disturbance. As a result, instead of control the governors in the primal frequency response block in Fig. 4.8, the primary control in 100% IBR systems will control the power electronic devices so that the system frequency can recover to the target value quickly. Traditionally, secondary control relies on the AGC, logic of which contains the calculation of Balancing Authority’s area control error (ACE). The equation of ACE is shown in the following equation: ACE 5 ðNIA 2 NIS Þ 2 10BðFA 2 FS Þ 2 IME

(4.52)

where NIA is the actual net interchange, NIS is the scheduled interchange, B is Balancing Authority bias, FA is actual frequency, FS is scheduled frequency, and IME is interchange metering error. When the system has 100% IBRs, the frequency-related term in (4.52) can be dropped because the system frequency can be quickly adjusted from inverters. The new ACE formulation is shown in the following equation: ACEnew 5 ðNIA 2 NIS Þ 2 IME

(4.53)

The calculated ACE is then allocated to each inverter based on their capacity (MW). For each inverter the set point has three components: the load-following set point, which is calculated by allocating the ACE from (4.53) to each inverter; the energy set point, which is from the economic dispatch result (5 minutes); and the contingency reserve set point (5 minutes). A new AGC design framework for 100% IBR is shown in Fig. 4.9.

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Net acutual interchange

Bulk power grid

Active power Output 1

Active power Output 2

ACE calculation

Set point 1

Inverter 1 Load-following set points (s)

Set point 2

Inverter 2

. . . Active power Output n

ACE allocation

Inverter n

+ Energy set points (5 min)

Set point n

Contingency reserve set points (5 min)

Figure 4.9 New AGC design for 100% inverter-based resource system.

4.3 Modern retail electricity market 4.3.1 State-of-the-art for retail electricity market development Historically, retail customers were subjected to electricity rates that are determined by electric utilities with governmental regulations. With the deregulation of the power industry, many states in the United States have enabled the retail electricity choice that allows end use consumers (including residential, commercial, and industrial customers) to buy electricity from competitive retail suppliers. The purpose of the retail electricity choice is to increase retail competition so that customers would enjoy lower prices, improved service, and innovative product offerings. By the end of 2017, there are 13 states in the United States that have fully restricted retail electricity market [28]. Some of them have full retail electricity choice, such as Texas, New York, and Illinois; some only have partial retail electricity market choice, such as California, Oregon, and Virginia. In order to attract customers and differentiate themselves from the competitors, some retail electricity suppliers would turn to renewable energy offerings. In six states of United States, community choice aggregation (CCA), also known as municipal aggregation, is formed to allow local governments to procure power on behalf of their residents, businesses, and municipal accounts from an alternative supplier while still receiving transmission and distribution service from their existing utility [29]. Many CCAs have chosen

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renewable energy for their supply. The retail electricity can exist regardless of the existence of wholesale electricity market in many states of the United States. For example, some states have both wholesale market and retail customer choice, such as Texas, New York, and Pennsylvania. A number of states that are part of wholesale markets do not have retail market, such as Iowa, Minnesota, and Indiana. A few states have retail electricity choice but do not participate into the wholesale market, such as Georgia and Oregon (although Oregon utilities partially participate into the wholesale market through CAISO’s Energy Imbalance Market program). There is a long debate if competition should be restricted to the wholesale side or be extended fully to the retail side [30]. However, with the massive integration of BTM DERs and increased need of customers’ participation, more states have realized the importance of establishing the full retail competition markets. The policymakers would pursuit a reliable, efficient, resilient, and clean energy supply within the state to increase the social welfare. The utilities are changing from only providing conventional transmission and distribution services to evolving roles of integrated energy service suppliers with innovations in financing infrastructure, management, and technologies. The end users are expecting new market rules, system monitoring, and legal framework to ensure a fair, transparent, and competitive retail market. The leading states for retail market development in the United States include Nevada, Texas, and New York. The Nevada legislature would be required to provide by law for an “open, competitive retail electric energy market with protections that entitle customers to safe, reliable, and competitively priced electricity” no later than July 1, 2023 [31]. In Texas, 85% of the power consumers can choose electricity service from different retail electric providers (REPs), which are approved by the Texas Public Utility Commission to sell electric services to residential and commercial customers. Each REP operates independently but competes with each other to entice potential customers. They offer a variety of rates and plan options, including green energy option, and are responsible for customer service, billing, and electricity sells. The New York State has proposed the Reforming the Energy Vision (REV) for the purpose of building a clean, more resilient, and affordable energy system for all customers within the state. The REV was driven by a transformation of the electric power industry moving to a new and sustainable energy future, with a proliferation of DERs and a focus on customer choice and participation. To achieve the goals

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(A)

(B) TSO/ISO (wholesale market) Step 1

Step 4

DER aggregator Step 2

Step 3

DSO (retail market)

DER aggregator Step 1

Step 4

TSO/ISO (wholesale market) Step 2

Step 3

DSO (retail market)

Figure 4.10 Two frameworks for the coordination of ISO, DSO, and DER. DER, Distributed energy resource; DSO, distribution system operator; ISO, independent system operator.

proposed by REV, many utilities plan to develop the DSP with the functionalities of forecasting, interconnecting, monitoring, controlling, and effectively managing the integration of DERs into the existing electric distribution system. The coordination among the ISO, DER aggregator, and distribution system operator (DSO) is key for retail electricity market development. Fig. 4.10 describes two popular frameworks for the coordination process. One framework as shown in subplot (A) is that the DER aggregator can coordinate with both wholesale and retail markets, and the coordination process is as follows [32]: (1) the ISO/TSO requests for grid services from DER aggregators based on the DER status, availability, range of adjustability, and other information; (2) the aggregator informs the DSO about the services it is going to bid in the ISO/TSO; (3) the DSO determines the acceptable services and schedules by running economic dispatch and other security-check models; and (4) the aggregator updates the schedules and services and rebids into the ISO/ TSO. The other framework as shown in subplot (B) is that the DER aggregator can only coordinate with the wholesale market. The corresponding coordination process is as follows: (1) the DER aggregator submits the commitment status and services to be provided in the wholesale market; (2) the ISO sends the DER aggregator’s requests to the DSO; (3) the DSO conducts economic dispatch and security check to determine desired services and schedules for the DER aggregator; and (4) ISO updates the schedules and requests based on the decisions in step 3 and runs the system operation models and sends the final results to DER aggregator.

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4.3.2 Design of retail electricity market operation framework Increasing amounts of DERs located on the customer side of the electric grid represent both a challenge and an opportunity for grid operators. Modernizing and monetizing the distribution system requires the construction of a novel DSO, which operates and regulates the distributed grid independently, similar to an RTO/ISO on the transmission grid. The DSO operates a comprehensive decision-making platform with centralized and distributed resources, including generation, energy storage, distributed control devices, sensing devices, power flows on the transmission interfaces, as well as thermal energy storage. The DSO operates an auto-steered informationdecision platform with four key functionalities: (1) fast and comprehensive data analytics and informatics, (2) monitoring real-time conditions and predicting near-future trends of the distributed grid with advanced demand management system (DMS)/SCADA systems, (3) forming the auto-steered decision-making framework to optimize distributed resources and power flow, and (4) increasing the utilization of renewable power and mitigating the load (both heat and electrical) variability by cooptimizing the electric and gas and heating/cooling (GHC) systems. These functionalities will form an intelligent network platform that will provide safe, reliable, and efficient electric services by integrating diverse resources to meet customers’ and society’s evolving needs. A proposed DSO framework and its connection with the transmission and GHC are shown in Fig. 4.11. The construction of retail market under the DSO framework is a solution to address the challenges faced by today’s power systems. Traditional power systems are designed and operated according to two fundamental assumptions: (1) the predominant flow of electric power is unidirectional, from central stations to customers and (2) electricity cannot be stored in significant quantities. However, the current state of affairs is beginning to rapidly change, especially in terms of electric distribution. With the emergence of large-scale distributed generation (DG) resources (e.g., rooftop PV, CHP, and microgrids) coupled to DR programs (e.g., load shedding and distributed storage), the bidirectional flows can originate from (and terminate at) nearly any point in the system. In New York State, utility companies are encouraged to shift their roles to “DSP providers”. A “DSP” mainly plays two roles: (1) the aggregator of DG and other DERs, including energy efficiency, DR, energy storage, and electric vehicles and (2) the interface between the wholesale bulk power system and

Traditional system

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Power plant

Electricity

Boiler

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Step-up transformer

Transmission lines

Step-down transformer

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EVs EMS/SCADA

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Home energy management

ICCP data

75%

T-D Interface Distribution system (DSO and Retail Markets)

Electricity, Gas and Heat cooptimization

District heating networks (DHNs)

Integrated renewable power, gas and heat systems

Figure 4.11 The proposed DSO framework and its connection with the transmission and gas systems. DSO, Distribution system operator.

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increasingly diverse retail local markets, which are a mixture of burgeoning customer generation, modern net loads, and emerging energy services. In recent years the CAISO has proposed a similar idea called “integrated distributed electricity system (IDES)” to recognize that energy resources and operating decisions will be broadly decentralized and localized, and customers, microgrids, and larger DER continue to benefit from connections to the transmission grid [33]. Changes in the operational power system paradigm, with focus on the distribution grid, have brought about three fundamental needs for future, smart power grids: • Fast and comprehensive data analytics and informatics: The evolution of smart meters and grid applications have provided utility companies with unprecedented capabilities for forecasting demand, scheduling outages, and shaping customer usage patterns. Technology innovations offer greater customer choice and functionality at decreasing costs that lead to changes in behavioral patterns on distribution systems. Utility companies need to develop a better decision-making platform capable of high-volume data management and advanced analytics designed to transform data into actionable insights. • Integrated renewable power, GHC coordination: Currently, the electricity and natural gas sector are operated individually in most utilities. With the increasing utilization of DG technologies such as cogeneration, CHP, and reformer-based fuel cell generation, the coupling between electricity and natural gas becomes more common. Many utilities have built up the district heating system for distributing heat generated from one or more sources via a network of insulated pipes carrying steam or hot water to heat buildings. Large-campus district cooling systems are experiencing fast growth in recent years. On the other hand, many utilities in the United States (such as those in Texas and Colorado) are suffering from excessive generation of wind power and negative electricity price in the midnight, when thermal storage can be charged to consume the power. There are needs to construct an integrated platform to coordinate the renewable power and the GHC systems. • Novel DSO construct (from ISO to DSO): During the last round of power industry disruption, the ISOs were instructed to manage the interface between IPPs and the wholesale grid. However, as grid operators, the ISOs have limited visibility and control over resources sited behind their meters. Today a new era has arrived for constructing operational models, that is, the DSO, which operate an IDES with a

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mixture of centralized and distributed resources, for example, generation, energy storage, power flow and stability control devices, and control systems, including sensing devices and load management capabilities The reliable and resilient operation of a DSO requires tight integration of the people, processes, and technology used to operate the distribution system. This is due to the highly dynamic nature of power flow and constantly changing local area topology on present distribution systems. In addition, the DSO would establish a secure clearing system to aggregate all participants’ service offers and a transparent process for coordinating DER dispatch, including a pricing mechanism to reflect the dynamic constraints on the distribution system. The DSO framework is helpful for the accelerated implementation of advanced technology for the grid, along with an evolution of distribution system designs to create a node-friendly or “plug-n-play” grid that enables seamless integration of DERs and independently owned and operated microgrids. The benefits involve at least three aspects: • better alignment of DER and GHC locational adoption to shape net load profiles and thereby improve system efficiency and load predictability; • DERs providing services to the distribution grid and bulk power system; and • localized controls of responsive devices and DERs to better balance the generation and dynamically evolving load. One significant challenge for constructing the DSO is dynamically managing the large volume of two-way communication data. For example, fast distribution state estimation method is required to evaluate the system condition and provide starting points for the retail market optimization engine. The DSO’s data management system has the ability to dynamically incorporate additional data into an executing application, and in reverse, ability of an application to dynamically steer the measurement process. A typical DSO framework has four levels to realize different functionalities, as shown in Fig. 4.12. The techniques in each level are detailed as follows: • Level 1: data management and analytics. The new paradigm of dynamic data-driven simulation asks for new approaches to incorporate real-time data in an effective and efficient manner. The operation condition in the distributed grid is highly stochastic, driven by the

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Level 2: Distribution system monitoring

DDDAS framework

ADMS/SCADA

Sensor

Internet

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Wide area network Fiber

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Network analysis - Power flow - State estimation - Limit violation monitor - Loss analysis

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- Volt/Var management - Feeder reconfiguration - Outage coordination - Self-healing

Price signal, dispatch results, and set point instruction

microgrid schedule merchant DER Behind-the-meter DER End-user customer

Cooptimization

Level 1: Sensor data measurement

Combined heat and power (CHP) plants

Building-level demand response with thermal heating

Gas and heat systems dispatch

Figure 4.12 Functionalities in different levels of the DSO framework. DSO, Distribution system operator.

•

•

•

stochastic nature of renewable energy outputs, circuit faults, customer behaviors, and interchange flows. Thus it is difficult to apply inference techniques that rely on equation-based model representations. To overcome these challenges the Sequential Monte Carlo (SMC) method is selected as the data assimilation algorithm for supporting dynamic data-driven simulation. Level 2: advanced DMS/SCADA system. State estimation is a key function in DMSs. Traditionally, the least-squares estimation method has been widely used in the DMS engine. Advanced DMS techniques should not only measure the state of the current condition but also be able to predict the near-future states in a look-ahead manner. A promising approach is to use SMC-based method with Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from given observations and predicted near-future conditions. Level 3: resource dispatch system. As a dispatch coordinator, the proposed DSO will consolidate and coordinate the energy transactions and ancillary or reliability services offered by individual DER aggregators, services firms, and customers. A secure market optimization and clearing system is operated by the DSO to schedule operations, make real-time controls, and process settlements. Level 4: comprehensive energy system. DSO is a comprehensive platform to align the gas, heat, and electric schedules on the distribution system level. The cooptimization framework will increase the utilization of renewable power when the demand is low and enable the CHP to purchase power from the grid during high demand

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periods/off-peak prices or sell surplus power to the grid during peak hours/high electricity prices.

4.3.3 Implementation of blockchain technology in electric power systems Traditionally, the power system is operated in a centralized manner in which the transmission of power is carried over long distances before the generated power is available to consumers through distribution networks. In the past decade, with the rising share of DG resources such as PV, wind, storage and microgrid, the power system is becoming more decentralized. The complexity of the grid has increased significantly, which causes challenges for the grid operation. First, the scale of integrated devices such as DER, smart meters, inverters, and IoT equipment will expand exponentially. Ensuring efficient and reliable communication among them and managing the mass amount of data created by them is a great challenge to power companies. Second, the monitoring and control of broad distributed assets and the link of them with SCADA/DMS and other enterprise systems require new operating paradigm of utilities. New tools are needed to enable optimized control of the grid and DERs to realize efficient grid management with technologies such as Volt/VAR optimization, power quality management, and DER coordination. Finally, protecting the grid and customer privacy by enhancing the cyber security of grid-edge devices and networks is significant but difficult because many parties are involved and mutually impacted, including the utilities, customers, owners of home energy management devices, solar installers, and companies that use cloud-based software to monitor gridedge devices [34]. The blockchain technology could be a promising way to address these challenges. A blockchain is a system in which the transaction records are made in cryptocurrency and maintained across several computers that are linked in a peer-to-peer network. Fig. 4.13 compares the centralized versus distributed transaction platforms. In the distributed transactional platform, every member in the network keeps its own coy of the ledger and can access in in the open cloud. As a result, all the members can access the log of the historical transactions and verify their validity. The process of a transaction on the blockchain is shown in Fig. 4.14. When a transaction is requested in the system, it will be broadcasted to a peer-to-peer network that contains many distributed nodes. The transaction information needs to be validated on each node. The validated

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Figure 4.13 Centralized versus distributed transactional platform.

A transaction is requested The request is broadcasted to a peer-to-peer network with distributed nodes The transaction information and the requester’s status are validated on the nodes The validated transaction is combined with other transactions to create a new block, which is then added to the existing blockchain

The transaction is completed

Figure 4.14 The process of a transaction on the blockchain.

transaction is combined with other transactions to create a new block, which is then added to the existing blockchain. Different algorithms can be used confirm transactions and produce new blocks to the chain, such as consensus algorithms, mining algorithms, and traceability chain algorithms [35]. The transaction completes after it is added to the existing blockchain. In the retail electricity market, customers can buy or sell electricity at real-time prices based on their location. They may also be able to provide various types of grid service such as voltage and reactive power support in response to price signal. The blockchain technology can be used to develop smart contracts to facilitate the peer-to-peer transactions between retail customers. With smart contracts, transactions are made automatically in response to price signals and real-time renewable energy production information in the network.

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4.4 Conclusion The electric power industry is undergoing profound changes on all entities involved, including generation resources, transmission system, wholesale market, distribution system, retail merchant, and end use consumers/prosumers. This changing landscape leads to challenges for grid operations on both wholesale and retail level. This chapter discussed new market design and products on the wholesale level to deal with high renewables penetration. The bulk system and wholesale market operation under 100% renewables scenario is examined. Finally, the design of modern retail electricity market as well as how to implement blockchain technology on peer-to-peer transaction are proposed.

References [1] Department of Energy, Public Utility Regulatory Policies Act of 1978 (PURPA), 1978. ,https://www.energy.gov/oe/services/electricity-policy-coordination-andimplementation/other-regulatory-efforts/public.. [2] Union of Concerned Scientists, Public Utility Regulatory Policy Act (PURPA). 2002. , https://www.ucsusa.org/clean_energy/smart-energy-solutions/strengthenpolicy/public-utility-regulatory.html . . [3] Federal Energy Regulatory Commission, 1996. Promoting Wholesale Competition Through Open Access Non-discriminatory Transmission Services by Public Utilities, and Recovery of Stranded Costs by Public Utilities and Transmitting Utilities (Order No. 888) and Open Access Same-Time Information System (formerly Real-Time Information Networks) and Standards of Conduct (Order No. 889). , https:// www.ferc.gov/legal/maj-ord-reg/land-docs/rm95-8-0aj.txt . . [4] Federal Energy Regulatory Commission, 1999. Regional Transmission Organizations (Order No. 2000). , https://www.ferc.gov/legal/maj-ord-reg/land-docs/RM992A.pdf . . [5] Consolidated Edison, Distributed System Implementation Plan. July 31, 2018. , https://www.coned.com . . [6] J. Zhu, K. Cheung, Flexible simultaneous feasibility test in energy market, in: IEEE Power and Energy Society General Meeting, Minneapolis, MN, July 2010. [7] X. Luo, O. Obadina, Security assessment and enhancement in real-time operations of ERCOT nodal electricity market, in: 2010 IEEE Power and Energy Society General Meeting, July 25July 29, 2010, Minneapolis, MN, 2010. [8] J. Gubman, Draft proposed qualifying capacity and effective flexible capacity calculation methodologies, 2013. ,https://www.cpuc.ca.gov/WorkArea/DownloadAsset. aspx?id 5 6529.. [9] N. Navid, G. Rosenwald, Ramp Capability Product Design for MISO Markets, 2013. [10] California ISO, Business Requirements Specification: Flexible Ramping Product, Version 1.2, 2016. [11] S. Lu, et al., Machine learning based multi-physical-model blending for enhancing renewable energy forecast—improvement via situation dependent error correction, in: Proc. 2015 Europ. Cont. Conf., Linz, Austria, July 15-17, 2015.

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[12] Q. Wang, et al., The value of improved wind power forecasting: grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales, Appl. Energy 184 (2016) 696713. [13] Q. Wang, C. Brancucci, H. Wu, A.R. Florita, B.M. Hodge, Quantifying the economic and grid reliability impacts of improved wind power forecasting, IEEE Trans. Sustain. Energy 7 (4) (2016) 15251537. [14] L.R. Chang-Chien, C.C. Sun, Y.J. Yeh, Modeling of wind farm participation in AGC, IEEE Trans. Power Syst. 29 (3) (2014) 12041211. [15] C. Wang, P. Luh, N. Navid, Ramp requirement design for reliable and efficient integration of renewable energy, IEEE Trans. Power Syst. (2016). Available from: https://doi.org/10.1109/TPWRS.2016.2555855. [16] V. Koritarov, et al., Modeling and Analysis of Value of Advanced Pumped Storage Hydropower in the United States, 2014. [17] California ISO, Renewables Watch, May 16, 2017. , http://content.caiso.com/ green/renewrpt/20170516_DailyRenewablesWatch.pdf . . [18] Southwest Power Pool (March 16, 2018). SPP set a new wind-penetration record of 60.56 percent at 3:45 a.m. on March 16. , https://spp.org/ . . [19] EirGrid Group, All Time Record for Wind Generated Electricity Broken, Jan. 2017. , http://www.eirgridgroup.com/newsroom/wind-record/ . . [20] Colorado General Assembly (2018), Require 100% Renewable Energy By 2035 (SB18-064). , http://leg.colorado.gov/bills/sb18-064 . . [21] Hawaiian Electric, Maui Electric and Hawai'i Electric Light, Planning Hawaii's Grid for Future Generations, in: Integrated Grid Planning Report, Mar. 1, 2018. [22] Federal Energy Regulatory Commission, Notice of Proposed Rulemaking, Electric Storage Participation in Markets Operated by Regional Transmission Organizations and Independent System Operators, 2016. [23] Federal Energy Regulatory Commission, Electric Storage Participation in Markets Operated by Regional Transmission Organizations and Independent System Operators, 2018. [24] Hawaiian Electric Companies, Planning Hawaii’s Grid for Future Generations: Integrated Grid Planning Report, 2018. [25] C. Loutan, et al., Demonstration of essential reliability services by a 300-MW solar photovoltaic power plant, in: Technical Report, NREL/TP-5D00-67799, 2017. [26] V. Gevorgian, B. O’Neill, Advanced grid-friendly controls demonstration project for utility-scale PV power plants, in: Technical Report/TP-5D00-65368, National Renewable Energy Laboratory, 2016. [27] NERC, Balancing and Frequency Control, NERC, Princeton, NJ, 2011. [28] The 21st Century Power Partnership, An introduction to retail electricity choice in the United States, in: NREL/BR-6A50-68993, 2017. [29] Environmental Protection Agency (2017). What is a Community Choice Aggregation (CCA)? ,https://www.epa.gov/greenpower/community-choiceaggregation. [30] D.R. Bohi, K.L. Palmer, Relative Efficiency Benefits of Wholesale and Retail Competition in Electricity: An Analysis and a Research Agenda. Prepared by Resources for the Future, National Renewable Energy Laboratory, Washington, DC, Golden, CO, 1996. [31] State of Nevada, Statewide ballot questions, 2016. ,https://www.nvsos.gov/sos/ home/showdocument?id 5 4434.. [32] Brian Seal, Understanding DERMS, in: EPRI Tech. Report, Palo Alto, CA, 2018. [33] L. Kristov, P. De Martini, 21st Century Electric Distribution System Operations, 2014.

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[34] Scott Sowers, EPRI to Develop Secure ‘Architectures’ for Distributed Energy Resources, EPRI Journal, Jul./Aug. 2017. , http://eprijournal.com/securing-thegrids-edge/ . . [35] Zee Ali, A. Simple Introduction to Blockchain Algorithms, Mar 9, 2019.

CHAPTER FIVE

Wide-area monitoring and anomaly analysis based on synchrophasor measurement Shutang You1, Yu Su1, Yong Liu2 and Yilu Liu1,3 1

University of Tennessee, Knoxville, TN, United States Pacific Gas and Electric Company, San Francisco, CA, United States 3 Oak Ridge National Laboratory, Oak Ridge, TN, United States 2

Contents 5.1 Synchrophasor measurement technology introduction 5.1.1 Situational awareness 5.1.2 Advanced control 5.2 Wide-area measurement system example—FNET/GridEye 5.3 FNET/GridEye wide-area measurement system applications overview 5.3.1 Visualization 5.3.2 Disturbance detection and location 5.3.3 Interarea oscillation detection and event-data-based oscillation modal analysis 5.3.4 Online ambient-data-based oscillation modal analysis 5.3.5 Islanding detection 5.3.6 Event replay and postevent analysis 5.3.7 Statistical analysis of historical data 5.3.8 Model validation and parameter verification 5.3.9 Machine learning based inertia estimation References Further reading

144 146 146 147 151 152 152 153 154 154 156 156 157 157 160 161

Power system is one of the most complex, large-scale dynamic systems in the world. Modern power grids usually ramify across a county and even a whole continent to convey electric power from generators to consumers. Consequently, a wide-area measurement network is the prerequisite of modern power grid monitoring and operation. In addition, due to the increasing deployment of intermittent renewable generators and other new power grid components, power grid dynamic characteristics become more and more complicated. Conventional monitoring technologies, such New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00005-5

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as those used in the existing supervisory control and data acquisition (SCADA) system that measures every 2 4 seconds without precise time synchronization, can only help operators understand the steady state situation of power grid. Comparatively, the synchrophasor technology-based wide-area monitoring measurement can monitor the power system dynamics in an unprecedented way. In this chapter, wide-area monitoring measurement and its various applications in modern power grids will be introduced.

5.1 Synchrophasor measurement technology introduction The concept of phasor was first introduced by Charles Proteus Steinmetz in 1893. In one of his publications, he presented “phasor” to describe the waveforms of alternating current (AC) electricity. Mathematically, a phasor can be considered as a vector rotating about the origin in a complex plane, as shown in Fig. 5.1. In electrical engineering, phasor is usually utilized to represent the measurement of three-phase AC voltage or current using a common time source for time synchronization. Therefore phasor measurement sensors are also commonly referred to as synchrophasors. The early prototype synchrophasor device to measure phasor, referred to as a phasor measurement unit (PMU) later, was invented by Dr. Arun G. Phadke and Dr. James S. Thorp in 1988 at Virginia Tech for transmission line protection purposes [1]. A PMU measures both AC voltage and current waveforms with accurate time synchronization at different locations of power grids. Fig. 5.2 shows the major components of a PMU

Figure 5.1 Phasor and sinusoidal/cosine function.

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Figure 5.2 Major components of a PMU. PMU, Phasor measurement unit.

Figure 5.3 WAMS structure. WAMS, Wide-area measurement system.

device [2]. The three-phase analog inputs (voltages and currents) are digitized by analog-to-digital converters. A global positioning system (GPS) receiver connected with an outdoor antenna provides pulse-per-second signals with 1 µs or even better accuracy for both waveform sampling and time synchronization. A PMU calculates the voltage and current phasors and sends them to data centers via communication network, at the rate of 10 60 per second depending on the application. Currently, a PMU-based wide-area measurement system (WAMS) usually consists of PMUs deployed across the power grid, phasor data concentrators collecting and aligning synchronized phasor data from PMUs, and data centers performing data storage and applications. A typical WAMS structure is shown in Fig. 5.3. Since its birth 30 years ago, a wide variety of synchrophasor applications have been developed not only for transmission line protection purposes but also for enhancing power grid operators’ situational awareness and control capabilities. All of these applications are further improving the security, reliability, and elasticity of modern power grids.

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5.1.1 Situational awareness With the invention of PMU in the late 1980s the PMU-based WAMS is able to reveal important insights into power grid dynamics by real time, synchronized, high-resolution measurement data. The difference between power grid monitoring using conventional SCADA systems and innovative PMU-based WAMS can be compared to a myopic people before and after vision correction, as shown in Fig. 5.4.

5.1.2 Advanced control The increasing deployment and application of synchrophasor technology enables wide-area fast-response controls in power systems as well. Traditionally, controls are developed using mathematical models obtained off-line. In general, locally derived signals are used as the feedback, and controls based on SCADA are relatively slow, which are unable to track fast-system dynamics. For instance, frequency control (automatic generation control) is a periodic control at the rate of every several seconds. On the contrary, synchrophasors provide a great opportunity to bring the remote, synchronized, high-resolution, and real-time measurements of system variables to the controllers, and, thus, the advanced control becomes possible, which will significantly enhance system stability and security.

Figure 5.4 Metaphorical comparison of power system monitoring using SCADA and PMU/WAMS. (A) Before vision correction and (B) after vision correction. PMU, Phasor measurement unit; SCADA, supervisory control and data acquisition; WAMS, widearea measurement system.

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5.2 Wide-area measurement system example—FNET/ GridEye FNET/GridEye is a pioneering wide-area monitoring network operated by the authors of this chapter. It aims to help prevent power system blackouts and facilitate renewable energy integration. A typical FNET/GridEye system consists of the hardware component, which is a set of highly accurate monitoring devices, called frequency disturbance recorders (FDRs), and the software component, which is a high-volume data collection and processing center that supports both real time applications and off-line data analytics [3,4]. In this section, FNET/GridEye will be introduced as an example of WAMS in order to showcase how a WAMS will benefit the electric power grids in multiple aspects. Fig. 5.5 demonstrates the key FNET/GridEye components. Measurements from the power grids (including frequency, voltage phase angle, and magnitude) are collected by the FDR sensors (which are equipped with GPS receivers in order to be GPS-synchronized) and are Internet-transmitted to the data center for processing, storage, and utilization. In the data center the measurement data first come to the data concentrator. It conditions measurement data, including cleaning and aligning data. The conditioned data go to the data storage server and real time

Figure 5.5 Architecture of FNET/GridEye.

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application server simultaneously. The data storage server stores data for future retrieval and non real time applications. The real time application server will execute multiple application modules, such as event location, oscillation monitoring, and event report. The real time application server can also provide visualized results to clients using the data from concentrator and generate event logs and event replay to the non real time applications and data storage. It is noted that FNET/GridEye’s FDR sensors can be deployed at the distribution level. Since frequency and voltage phase measurement can be used in monitoring a variety of grid dynamics disturbances, including generation trip, load shedding, islanding, line tripping, and oscillations. The frequency and voltage phase measurement at the distribution level can enable many situational awareness applications of bulk power systems while significantly reducing WAMS deployment costs. FNET/GridEye is now monitoring North American grids and over 20 major power grids worldwide, including United States, Canada, Mexico, Chile, Bahamas, Brazil, Finland, Sweden, The Netherlands, Italy, Spain, Russia, Denmark, Germany, Egypt, Turkey, United Kingdom, India, China, Taiwan, Japan, South Korea, Australia, and South Africa, as shown in Figs. 5.6 and 5.7. Similar to its transmission level counterparts, phasor measurement at distribution level can be obtained from household outlets at variable sampling rates depending on specific applications. Then the phasor measurement will be GPS-time-stamped and transmitted to the data center through the Internet. FDR is now in its second generation (as shown in Fig. 5.8) and is based on an embedded system with GPS timing and Ethernet functions. FDR components are shown in Table 5.1 [5]. Fig. 5.9 is the hardware architecture of the first-generation FDR. Although advances are made in hardware and layout, both generations of FDR share the same principal design. First, the AC voltage signal from common wall outlets goes through a step-down transformer in the FDR and becomes 10 V. A low-pass filter subsequently blocks the high frequency noises. Then, an ADC module samples the 10 V signal based on a timing signal provided by GPS-synchronized oscillator pulses. The sampled values are fed to the central processing units to obtain phasor measurements including voltage magnitude, phase angle, and voltage frequency. Finally, the data are transmitted to the device server via Internet. The second-generation FDR separated the hardware for phasor calculation and communication. A digital signal processor is used for

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Figure 5.6 Map of FDR deployment in North America.

Figure 5.7 Worldwide power grid monitored by FNET/GridEye.

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Figure 5.8 The second generation of FDR.

Table 5.1 FDR components. No. Component

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Figure 5.9 Hardware block diagram of first generation of FDR.

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Figure 5.10 Illustration of FDR installation.

phasor calculation and a microcontroller to integrate GPS and Ethernet. The second-generation FDR also has better accuracy performance due to the active filter design replacing the LC filter in the first-generation FDR. Low installation cost and high measurement accuracy are two important characteristics of FDR. Each FDR interfaces with the electric grid at low, easily accessible, voltage levels. The installation costs for an FDR are nearly nonexistent. This feature gives FDR an extreme cost advantage over PMU technology, which is designed to instrument high-voltage substations. In addition, taking the Internet as data collection solution makes the wide-area information communication and exchange possible and cost-effective. A typical FDR installation is illustrated in Fig. 5.10. FDR can achieve high accuracy in measurement. For frequency measurements the error is less than 0.0005 Hz. A comparison study between an FDR and four commercial PMUs from 2003 was conducted by Wang et al. [5], showing that FDR is more accurate in measuring frequency than commercial PMUs.

5.3 FNET/GridEye wide-area measurement system applications overview After more than 10 years of development and field testing, multiple function modules have been developed in FNET/GridEye to improve operators’ situational awareness, as discussed in the following subsections.

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5.3.1 Visualization Frequency and phase angle are important indicators of power system health. By overlaying real time measurements from FNET/GridEye on a geographic information system, an intuitive visualization tool is created to help operators interpret system behavior in real time (Fig. 5.11). The FNET/GridEye system is not bound by electric power utility territories and provides system frequency observability of the entire North American power system.

5.3.2 Disturbance detection and location Many disturbances, such as loss of generation or transmission lines, occur daily in a large power system. While the power system is resilient against most disturbances, it is still of great importance to have the ability to quickly detect and locate disturbances as they happen, to prevent an escalation into large-area blackouts under extreme conditions [6 8].

Figure 5.11 Real time visualization of measurement data.

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Figure 5.12 Disturbance detection and location.

By continuously screening real time measurement data the FNET/ GridEye disturbance detection and location module (Fig. 5.12) sends alerts to operators and other reliability entities once any abrupt frequency or angle change is detected. Through the automated analysis of disturbances detected by the FNET/GridEye system, the geographic location of disturbances can be estimated. This location estimation technology is based on the premise that FDRs closest to the disturbance witness the frequency or angle change earlier than the others. Furthermore, after losing a generator or transmission line, operators are eager to know the magnitude of power loss so that they can take corrective actions. To that end, this function module also gives an estimate of such a generation-load mismatch in the disturbance alert.

5.3.3 Interarea oscillation detection and event-data-based oscillation modal analysis Interarea oscillation is one of the potential threats to grid stability. Through a frequency/angle-based oscillation detection algorithm, FNET/ GridEye monitors event-triggered interarea oscillations and provides grid operators with valuable oscillation modal information once an oscillation is detected (Fig. 5.13) [9].

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Figure 5.13 Interarea oscillation detection and event-data-based oscillation modal analysis.

5.3.4 Online ambient-data-based oscillation modal analysis Considering the constant changing of operational conditions, continuous oscillation modal information is highly desirable so that appropriate measures can be taken in real time to prevent any serious interarea oscillations. The online oscillation modal analysis module uses real time ambient data (when there is no significant disturbance in the power system) to compute system oscillation modes and can assess power grid stability continuously in real time [10,11] (Fig. 5.14).

5.3.5 Islanding detection An islanding event occurs when one or more generators lose synchronism with the rest of the power system. Critical intervention actions must be taken, or the islanding event can develop into blackouts. The FNET/ GridEye system uses frequency signals to detect islanding events and send out islanding warnings to grid operators if an islanding event happens (Fig. 5.15).

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Figure 5.14 Online ambient-data-based oscillation modal analysis.

Figure 5.15 Islanding detection.

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5.3.6 Event replay and postevent analysis Blackouts over large areas are often disastrous. FNET/GridEye measurements of a blackout can be used to reconstruct the event scenario for postevent analysis. A blackout occurred in Florida in 2008 where 22 transmission lines, 4300 MW of generation, and 3650 MW of load were lost [12 16]. FNET/ GridEye measurements captured the event and were used to construct a replay of the blackout event (The video of this event is available at http:// www.youtube.com/watch?v=H7y-oJYpDkM&feature=c4-overview&list= UU0uqG3I8AxZ8ZMgB5NlFpFg). Fig. 5.16 shows several screenshots of the event replay. Event-replays like this facilitates postevent analyses and aids the development of tools for avoiding similar blackouts in the future.

5.3.7 Statistical analysis of historical data Measurement data are collected from FNET/GridEye sensors on a 24/7 basis. With advanced data mining techniques, historical data can be extremely informative. For instance, disturbance statistical analysis performed with FNET/GridEye can reveal the distribution of disturbances

Figure 5.16 Event replay and postevent analysis. (A), 18:09:8.4 at UTC Time; (B), 18:09:8.7; (C), 18:09:9.0; (D), 18:09:9.4; (E), 18:09:9.8; (F), 18:09:10.6.

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Figure 5.17 Statistical analysis of historical data.

over a certain time period. Shown in Fig. 5.17 is the 2010 monthly distribution of disturbances in the US Eastern Interconnection.

5.3.8 Model validation and parameter verification Many of the advanced operation and planning functions in the power system’s control center depend on the availability of accurate power grid models. Unfortunately, accurate models (and parameters) for many of the devices connected to the power grid, particularly with respect to dynamic behaviors, are unavailable. Because FNET/GridEye sensors capture power grid behaviors accurately over the entire grid, FNET/GridEye measurement data can be used as the benchmark with which to validate power grid models. For instance, using the frequency measurement as the benchmark, FNET/GridEye has revealed that the current US Eastern Interconnection model is far from accurate for creditable simulation results and that a series of device parameters need to be tuned to match the actual system (Fig. 5.18). Moreover, FNET/GridEye can be used to tune the model to match the measurement.

5.3.9 Machine learning based inertia estimation With the development of machine learning technologies, many new applications using FNET/GridEye data become possible by taking full advantages of machine learning. An example of machine learning based applications is

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Figure 5.18 Model validation and parameter verification.

using FNET/GridEye data to estimate system inertia using ambient frequency measurement. Since the inertia level will influence the ambient frequency signatures, as shown in Fig. 5.19, system inertia can be estimated using FNET/ GridEye data by extracting features from ambient frequency measurement. Fig. 5.20 shows the actual and estimated inertia profiles in the western electricity coordinating council (WECC) system in heavy and light load seasons. It can be seen that combining machine learning and FNET/GridEye measurement can achieve accurate estimation of system inertia. Estimated inertia information will facilitate advanced frequency control functions and benefit the estimation of event magnitudes as well. In conclusion, as a WAMS, FNET/GridEye can increase the system situational awareness capability to a new level: (1) the FNET/GridEye sensor is a single-phase PMU with higher precision than commercial PMUs, but with reduced manufacturing and installation costs because it is installed in a common electrical wall outlet at the 120-V distribution level and (2) FNET/GridEye offers a full package of measurement data analytics functions for the purpose of improving operators’ awareness of system status. FNET/GridEye is a cost-effective, multifunctional power grid monitoring network based on quickly deployable sensors and advanced data-utilization techniques. By overcoming the installation and maintenance difficulty of traditional PMUs, FNET/GridEye sensors are suitable for wide deployment at much higher density.

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Figure 5.20 Comparison of measured and estimated inertia in WECC during heavy and light load seasons.

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References [1] A.G. Phadke, J.S. Thorp, History and applications of phasor measurements, in: IEEE PES Power Systems Conference and Exposition, Atlanta, GA, Oct. 29 Nov. 1, 2006. [2] A.G. Phadke, J.S. Thorp, Synchronized Phasor Measurements and Their Applications, Springer, New York, 2008, p. 4. [3] Z. Zhong, C. Xu, B.J. Billian, L. Zhang, et al., Power system frequency monitoring network (FNET) implementation, IEEE Trans. Power Syst. 20 (2005) 1914 1921. Available from: https://doi.org/10.1109/TPWRS.2005.857386. [4] Y. Zhang, P.N. Markham, T. Xia, et al., Wide-area frequency monitoring network (FNET) architecture and applications, IEEE Trans. Smart Grid 1 (2) (2010) 159 167. [5] L. Wang, J. Burgett, J. Zuo, C. Xu, B.J. Billian, R.W. Conners, Y. Liu, Frequency disturbance recorder design and developments, in: Proc. 2007 IEEE Power Engineering Society General Meeting, 2007, pp. 1 7. [6] J.S. Thorp, C.E. Seyler, A.G. Phadke, Electromechanical wave propagation in large electric power systems, IEEE Trans. Circ. Syst. I: Fundam. Theory Appl. 45 (6) (1998) 614 622. [7] S.S. Tsai, L. Zhang, A.G. Phadke, Y. Liu, M.R. Ingram, S.C. Bell, et al, “Study of global frequency dynamic behavior of large power systems,” IEEE PES Power Systems Conference and Exposition, 2004, vol. 1, 10 13 Oct. 2004, 328 335. [8] T. Xia, H. Zhang, R. Gardner, J. Bank, J. Dong, J. Zuo, Y. Liu, et al., Wide-area frequency based event location estimation, in: IEEE Power Engineering Society General Meeting, 2007, 24 28 June 2007, pp. 1 7. [9] J.K. Wang, R.M. Gardner, Y. Liu, Analysis of system oscillations using wide-area measurements, in: Proc. 2006 IEEE Power Engineering Society General Meeting, 2006, pp. 6. [10] R.M. Gardner, W. Li, J. West, J. Dong, Y, Liu, G. Zhang, Power system frequency oscillation characteristics, in: Proc. 2008 IEEE Power Engineering Society General Meeting, 2008, pp. 1 7. [11] W. Li, R.M. Gardner, J. Dong, L. Wang, T. Xia, Y. Zhang, Y. Liu, G. Zhang, Y. Xue, Wide area synchronized measurements and inter-area oscillation study, in: Proc. 2009 Power Systems Conference and Exposition, 2009, pp. 1 8. [12] T.J. Overbye, J.D. Weber, Visualizing the electric grid, Spectrum, IEEE 38 (2) (2001) 52 58. [13] G. Pires de Azevedo, C. Sieckenius de Souza, B. Feijo, Enhancing the humancomputer interface of power system applications, IEEE Trans. Power Syst. 11 (2) (1996) 646 653. [14] J.D. Weber, T.J. Overbye, Voltage contours for power system visualization, IEEE Trans. Power Syst. 15 (1) (2000) 404 409. [15] G. Zhang, P. Hirsch, S. Lee, Wide area frequency visualization using smart client technology, in: Power Engineering Society General Meeting, 2007. IEEE, 24 28 June 2007, pp. 1 8. [16] G. Zhang, S. Lee, R. Carroll, J. Zuo; L. Beard, Y. Liu, Wide area power system visualization using real-time synchrophasor measurements, in: Power and Energy Society General Meeting, 2010 IEEE, 25 29 July 2010, pp. 1 7.

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Further reading J.N. Bank, R.M. Gardner, S.-J.S. Tsai, K.S. Kook, Y. Liu, Visualization of wide-area frequency measurement information, in: Power Engineering Society General Meeting, 2007, IEEE, 24 28 June 2007, pp. 1 8. M. Paolone, Synchrophasor fundamentals: from computation to implementation, in: Tutorial Synchrophasor Fundamentals and Applications: Leveraging the Investment, IEEE PES General Meeting, July 21 25, 2013. S.-J.S. Tsai, J. Zuo, Y. Zhang, Y. Liu, Frequency visualization in large electric power systems, in: Power Engineering Society General Meeting, 2005, IEEE, Vol. 2, 12 16 June 2005, pp. 1467 1473.

CHAPTER SIX

Advanced grid operational tools based on state estimation Yu Liu1, Yuzhang Lin2 and Junbo Zhao3 1

School of Information Science and Technology, ShanghaiTech University, Shanghai, P.R. China Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA, United States 3 Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, United States 2

Contents 6.1 Introduction 6.2 Model validation 6.2.1 Largest Normalized Lagrange Multiplier test 6.2.2 Computationally efficient implementation of the Largest Normalized Lagrange Multiplier test 6.2.3 Detectability and identifiability of parameter and measurement errors 6.3 System monitoring 6.3.1 Motivations for dynamic state estimation 6.3.2 Problem formulation of dynamic state estimation 6.3.3 Unified framework for Bayesian dynamic state estimation through nonlinear regression 6.4 Protective relaying 6.4.1 Theoretical basis of dynamic state estimationbased protection 6.4.2 Numerical experiments 6.5 Conclusion remarks References Further reading

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6.1 Introduction Accurate knowledge of operating states and models is essential for reliable and economic operation of electric power systems [15]. As a result, traditional data-acquisition systems are widely installed in present power systems, to obtain measurements, including voltage magnitudes, current magnitudes, real power, reactive power, and circuit breaker status New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00006-7

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[611]. In addition, modern power systems are equipped with state-ofthe-art measuring devices to provide high-quality and high-density measurements, including GPS-synchronized phasor measurements from phasor measurement units (PMUs) and GPS-synchronized instantaneous “points on wave” measurements from merging units (MUs) [1214]. Nevertheless, even with these measurements, additional steps are still required for effective operation of electric power systems because of the following reasons. First, the models of the power system may not be accurate enough due to changes of operating conditions or even human errors. Second, the measurements are usually with errors due to imperfect instrumentation channels. Some measurements are with very large error (bad data) due to corrupted communication channels or incorrect configurations, cyberattacks, etc. Third, some important states and health conditions of the system could not be directly measured using today’s measuring devices. State estimation is a powerful method in solving the above issues. It systematically considers the measurement error and model uncertainty by taking full advantage of the measurement redundancy. State estimation can be further classified into static state estimation and dynamic state estimation (DSE) algorithms. For different applications the system of interest can be modeled using either static models with the assumption of pure sinusoidal waveforms and constant phasors while ignoring the dynamics governed by the differential equations, or the transient models using both differential and algebraic equations (DAEs). For the static models the static state estimator (SSE), tracking state estimator, and forecasting-aided state estimator have been widely investigated. By contrast, in the presence of large disturbances, the full consideration of DAEs is required, and this leads to the development of DSE methods. With increasing penetration of renewables and more complex power electronicsbased components in modern power systems, different modeling procedures as well as state estimation algorithms should be properly selected for different applications. This chapter focuses on advanced operational tools based on state estimation for modern power systems. Section 6.2 introduces the model validation tool, where the parameters of large-scale power system models can be effectively validated and calibrated in case they are identified as incorrect. Section 6.3 provides the system monitoring tool based on DSE, where the model uncertainties, measurement errors and bad data are systematically considered and filtered out, resulting in better situational

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awareness of the system operating states. Section 6.4 demonstrates the DSE-enabled protective relaying tool, where the health condition of the system is carefully examined in real time, and the component of interest can be reliably and quickly protected from faults.

6.2 Model validation Most advanced applications in power system operation are heavily model-based today. Just to name a few, the performances of static security assessment, optimal power flow, and various control and protection schemes can be severely degraded when the quality of network parameters cannot be guaranteed. Therefore maintaining an accurate network parameter database is always of paramount importance. In practice, reliable network parameter values do not come naturally. A number of reasons can lead to parameter errors: flawed manufacturing data, human entry errors, status update failures, ambient condition changes, and even malicious cyberattacks. Furthermore, cleaning up errors from a practical power network parameter dataset is not a trivial task. There are two major challenges to be addressed: 1. Identification of erroneous parameters from a large model dataset. Large-scale power systems have thousands or tens of thousands of parameters, and the original data from generating these parameters may not be owned by the system operators. The effort can be prohibitively large to conduct an exhaustive search for the erroneous parameters, or even a reliable suspect set. 2. Differentiation between model parameter errors and measurement errors. The most effective avenue to identify parameter errors is to observe the inconsistencies between measurements and the network model. However, when such inconsistencies are observed, it is often difficult to determine whether the errors lie in the measurements or the network model. Misidentification of the error sources can lead to mistaken correction of good quantities, leading to worse situations. In this section an effective tool for the detection, identification, and correction of model parameter errors based on state estimation will be presented. That is, the Largest Normalized Lagrange Multiplier (LNLM) test. Obviously, this is not the only avenue to tackle this problem. Since state estimation is the natural bridge between power system models and

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measurements, there has been a long history of exploiting its capability in improving the quality of power system models [1220]. The LNLM test is one of the few approaches which systematically address the aforementioned two major challenges, thus having great potential for implementation in large-scale real-world power systems [1520]. In this section the basic principles and procedures of the LNLM test will be presented. This will be followed by a computationally efficient implementation of the LNLM test, and the detectability and identifiability theory of model parameter errors.

6.2.1 Largest Normalized Lagrange Multiplier test 6.2.1.1 Extraction of Lagrange multipliers from state estimation problem Suppose the parameter vector in the model is p but the true parameter vector is pt . Write the parameter error vector pe as pe 5 p 2 pt

(6.1)

The measurement vector will then be written as a function of states and parameter errors as follows: z 5 h x; pe 1 e (6.2) where z is the measurement vector, x is the state vector, and e is the measurement error vector, and h is the nonlinear function linking x and pe to z. The widely applied weighted least squares (WLS) state estimation problem can be formulated as the following form: minx;

pe

T 1 J x; pe 5 z2hðx; pe Þ R21 z 2 hðx; pe Þ s:t: 2

pe 5 0 (6.3)

where R is the covariance matrix of the measurement vector. Initially, there is no reason to suspect any parameters, thus pe is set equal to a zero vector. Note that Eq. (6.3) is completely equivalent to the traditional WLS state estimation formulation where parameter errors are not considered [21] 1 minx J ðxÞ 5 ðz2hðxÞÞT R21 ðz 2 hðxÞÞ 2

(6.4)

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In Eq. (6.4) the constraints can be eliminated by forming the Lagrangian: T 21 1 L x; pe ; λ 5 z2h x; pe R z 2 h x; pe 2 λT pe 2

(6.5)

where λ is the Lagrange multiplier vector associated with pe . The firstorder necessary condition for optimality will yield: @L 5 HTp R21 z 2 h x; pe 1 λ 5 0 @p

(6.6)

where Hp is the Jacobian matrix of measurement function hðx; pe Þ with respect to network parameter vector p. At the solution point x^ , define the residual vector r as r 5 z 2 h x^ ; pe (6.7) Then the Lagrange multiplier vector can be recovered by λ 5 2 HTp R21 r

(6.8)

Mathematically, Lagrange multipliers show the increment of the objective function in response to disturbances of the constraints. Hence, if the Lagrange multiplier corresponding to a model parameter error is large, it infers that changing the estimate of this parameter error will lead to significant reduction of the objective function, that is, the initial assumption that this error is zero may be invalid. Therefore the Lagrange multipliers convey useful information for detection of model parameter errors [15,17]. 6.2.1.2 Normalized Lagrange multipliers and hypothesis testing In order to develop a rigorous procedure for parameter error detection using the information conveyed by Lagrange multipliers, the quantitative relationship between Lagrange multipliers and parameter errors should be derived. A linearized measurement model can be used for this purpose: Δz 5 HΔx0 1 Hp pe 1 e

(6.9)

where Δx0 is the increment of the erroneous state vector x0 when the parameter errors are not properly processed (initial assumption of zero parameter errors), H is the Jacobian matrix of measurement function

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hðx; pe Þ with respect to network parameter vector p. The WLS estimate is given by Δx^0 5 G21 HT R21 Δz 2 Hp pe (6.10) where G, the so-called gain matrix, is defined by G 5 HT R21 H

(6.11)

Hence, the estimated measurement vector can be written as Δz^0 5 HΔx^0 1 Hp pe 5 HG21 HT R21 Δz 2 HG21 HT R21 Hp pe 1 Hp pe 5 KΔz 1 SHp pe

(6.12)

where K 5 HG21 HT R21

(6.13)

S5I2K

(6.14)

Meanwhile, the true linearized measurement model, which is free of parameter errors, can be written as Δz 5 HΔx 1 e

(6.15)

Substituting Eq. (6.9) into Eq. (6.12) will yield r 5 Δz 2 Δz^0 5 Δz 2 KΔz 2SHp pe 5 SΔz 2 Hp pe 5 S HΔx 1 e 2 Hp pe 5 Se 2 SHp pe 5 Se 1 Bpe

(6.16)

B 5 2 SHp

(6.17)

where

The second to last step of Eq. (6.15) is due to the fact that SUH 5 ðI 2 KÞH 5 H 2 H 5 0

(6.18)

which can be easily verified. Eq. (6.16) gives insight into the relationship between measurement residual and the two types of errors: measurement errors and parameter

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errors. It is noted that besides the familiar term SUe from conventional bad data analysis [21], there is an extra term 2SUHp Upe that accounts for the impact of parameter errors on the measurement residual vector r. Combining Eq. (6.16) with Eq. (6.8) will yield λ 5 2 HTp R21 r 5 2 HTp R21 Se 2 SHp pe 5 HTp R21 SHp pe 2 HTp R21 Se 5 Λpe 1 Ae

(6.19)

A 5 2 HTp R21 S

(6.20)

Λ 5 HTp R21 SHp

(6.21)

where

Eq. (6.19) shows the relationship between Lagrange multipliers and the two types of errors. In particular, Λ is the sensitivity matrix linking λ to pe . Similar to measurement residuals, Lagrange multipliers are also linear combinations of the errors. The major difference lies in the fact that λ has one-to-one correspondence to pe , whereas r has one-to-one correspondence to e. The sensitivity matrix between λ and pe , Λ, and the sensitivity matrix between r and e, S, are both square matrices. This property implies that it is more convenient to use measurement residuals to analyze measurement errors and use Lagrange multipliers to analyze parameter errors. After deriving the relationship between Lagrange multipliers and parameter errors, hypothesis testing can be formulated for the detection of parameter errors. Unlike analog measurements that commonly carry random noise, network parameters are relatively constant. Therefore parameter errors can be considered as deterministic variables, and pe can be considered as a vector whose entries have either zero or nonzero deterministic values. When pe 5 0, Eq. (6.19) reduces to λ 5 2 HTp R21 Se

(6.22)

Assuming Gaussian measurement errors, Lagrange multipliers will also obey Gaussian distribution with zero mean, whose covariance matrix is given by

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covðλÞ 5 EλλT

5 E HTp R21 rrT R2T Hp

5 HTp R21 covðrÞR2T Hp 5 HTp R21 ðSRÞR2T Hp 5 HTp R21 SHp 5 Λ

(6.23)

The third to last step uses the result covðrÞ 5 SR [21]. It is interesting to note that the covariance matrix of λ happens to be the sensitivity matrix of λ with respect to pe . For a Lagrange multiplier (taking the ith one as example), normalization can be performed as follows: λi λN i 5 pﬃﬃﬃﬃﬃﬃ Λii

(6.24)

where Λii is the ith diagonal entry of Λ. The resulting variables are referred to as the normalized Lagrange multipliers (NLMs). They will obey standard normal distribution: H0 :

λN i BN ð0; 1Þ

(6.25)

This is the null hypothesis of the LNLM test. If pe 6¼ 0 but is deterministic, Lagrange multipliers will obey Gaussian distribution with nonzero mean values: (6.26) EðλÞ 5 E Λpe 1 E 2HTp R21 Se 5 Λpe and the covariance is given by covðλÞ 5 cov HTp R21 SHp pe 1 HTp R21 Se 5 cov HTp R21 Se 5 HTp R21 ðSRÞR2T Hp 5 HTp R21 SHp 5 Λ

(6.27)

Comparing Eq. (6.27) and Eq. (6.23) it is clear that the covariance is independent of parameter errors. Suppose only the ith parameter carries error. Normalizing λi by Eq. (6.24) yields pﬃﬃﬃﬃﬃﬃ H1 : λ N BN Λ ; 1 (6.28) p ii e; i i

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This is the alternative hypothesis of the test. NLMs should be checked against a critical value to determine whether the null hypothesis should be accepted. Since the distribution of H0 is symmetric with respect the vertical axis, a positive threshold t can be chosen and compared against jλN i j. 6.2.1.3 Detection, identification, and correction of model parameter errors The detection of model parameter errors is accomplished by differentiating between the null hypothesis H0 and the alternative hypothesis H1 . Choosing a desirably low probability of creating false alarms, α, the threshold for parameter error detection can be chosen as α (6.29) t 5 Φ21 2 where Φ is the cumulative distribution function of standard normal distribution. When an error is detected, the next step is to identify the source of the error, that is, which parameter carries the error. From Eq. (6.19), it is known that the Lagrange multipliers are correlated, so a single parameter error not only leads to a large value of its corresponding NLM but also causes large values of the NLMs corresponding to other parameters. Thus observing a large NLM does not always imply that its corresponding parameter is erroneous. For the identification of parameter errors the “LNLM” criterion can be used: the parameter corresponding to the LNLM is identified as erroneous. The theoretical foundation of the LNLM criterion is the theorem shown below. Theorem 6.1. Suppose there is a single erroneous parameter, and all other parameters and measurements are correct. The normalized Lagrange multiplier corresponding to the error will have the largest absolute value among all the normalized Lagrange multipliers of parameters in the system. The proof of the theorem can be found in Ref. [17]. As mentioned at the beginning of the subsection, one of the major challenges in the parameter error identification problem is the differentiation between parameter errors and measurement error. This problem can be addressed by applying the LNLM test jointly with the largest normalized residual (LNR) test. The LNR test is a well-known approach for measurement error detection and identification. At the solution point of

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the WLS state estimation problem, the normalized residual (NR) of a measurement (taking the ith measurement as example) can be obtained by ri riN 5 pﬃﬃﬃﬃﬃﬃ (6.30) Ωii where Ωii is the ith diagonal entry of the covariance matrix of the measurement residual vector [21]: Ω 5 SR

(6.31)

The measurement errors are identified using the “LNR” criterion, namely, the measurement corresponding to the LNR is identified as erroneous. The theoretical foundation is provided by the following theorem. Theorem 6.2. Suppose there is a single erroneous measurement, and all other measurements and parameters are correct. The normalized residual corresponding to the error has the largest absolute value among all the normalized residuals in the system. The proof of the theorem can be found in Ref. [21]. Combining the LNLM and LNR criteria, model parameter errors and analog measurement errors can be differentiated. In the combined approach the NLMs and NRs for all the model parameters and analog measurements are computed, respectively. Subsequently, the quantity corresponding to the largest normalized variable is identified as erroneous. In other words, if the largest normalized variable is a NLM, it is a parameter error; otherwise, it is a measurement error. This interesting property is given by the following two theorems. Theorem 6.3. Suppose there is a single erroneous parameter, and all other parameters and measurements are correct. The normalized Lagrange multiplier corresponding to the error will have a no smaller absolute value than any of the normalized residuals in the system. Theorem 6.4. Suppose there is a single erroneous measurement, and all other parameters and measurements are correct. The normalized residual corresponding to the error will have a no smaller absolute value than any of the normalized Lagrange multipliers in the system. The proof of the above two theorems can be found in Ref. [17].

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Figure 6.1 Flowchart for model parameter error processing.

After the erroneous parameter or measurement is identified, it needs to be corrected. It is known that an erroneous measurement can be corrected as follows [21]: zi;corr 5 zi;bad 2

Rii ri Ωii

(6.32)

where zi;corr is the corrected value of the ith measurement, zi;bad is the erroneous value of the ith measurement. For an erroneous parameter, following Eq. (6.19), the equation below can be used for correction [18]: pi;corr 5 pi;bad 2

1 λi Λii

(6.33)

Obviously, using the LNLM/NR criteria, only one erroneous quantity will be identified and corrected. After the correction of the identified error the WLS state estimation needs to be executed again to obtain the unbiased results. In the presence of multiple errors, this process should be repeated until no error is detected. The high-level flowchart of the entire procedure is illustrated in Fig. 6.1.

6.2.2 Computationally efficient implementation of the Largest Normalized Lagrange Multiplier test In the LNLM testbased approach described in the last subsection, the main computational effort is spent on the computation of the covariance

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matrix entries. Defining the weight matrix in the WLS state estimation problem as W 5 R21 , It can be known from Eqs. (6.13), (6.14), (6.19), and (6.21) that Λ 5 HTp WHp 2 HTp WHT G21 HT WT HTp

(6.34)

Note that the second term on the right-hand side of Eq. (6.34) involves inversion of the gain matrix G, and the product of the inverse and two Jacobian matrices. Both the matrix inversion and multiplication operations consume a significant amount of CPU time and memory. However, since only the diagonal entries of Λ are actually needed, and Hp , H, and W are all highly sparse, only a small subset of the entries in G21 are actually needed and used. In this subsection the structure of this subset will be determined first, and then a method for computing this subset efficiently will be presented. Denote the second term of Eq. (6.34) as Λ0 5 HTp WHT G21 HT WT Hp

(6.35)

Ω0 5 HG21 HT

(6.36)

Λ0 5 HTp WΩ0 WT Hp

(6.37)

and

such that

To obtain the necessary subset of G21 , first the necessary subset of Ω0 should be derived. Suppose that the system has n states, m measurements, and u parameters. Using Eq. (6.37), the diagonal entries of Λ0 can be expressed as Λ0ii 5

u X u X

Hp;ki Wkk Ω0kl Wll Hp;li

(6.38)

k51 l51

Since Hp is super sparse, only a few terms of the right-hand side are nonzero. Thus in Ω0 , only the entries in the nonzero terms need to be computed. Hence, the necessary subset of Ω0 can be written as Ω0nec 5 Ω0kl jHp;il 6¼ 0; Hp;ik 6¼ 0; i 5 1; 2; . . .; u (6.39) The required entries in Ω0 for computing the variance of λi are those corresponding to the covariance of measurements related to λi , that is, the measurements in whose equations pi is present. For example, if there are

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mi measurement equations associated with pi , for computing the variance of λi , only mi 3 mi 5 m2i entries in Ω0ðqÞ need to be computed. Since only the local measurements are associated with a specific parameter, the number mi is typically small and the number of necessary entries in Ω0 is also small. Following similar logic, the structure required subset of G21 entries can be found based on the structure of the necessary subset of Ω0 . Entries of Ω0 can be expressed as Ω0ij 5

m X m X

Hik G21 kl Hjl

(6.40)

k51 l51

Because H is highly sparse, only a few terms of the right-hand side is nonzero. Thus in G21 , only the entries in the nonzero terms need to be computed. This being given the necessary subset of G21 can be written as n o 21

(6.41) G nec 5 G21 kl jHil 6¼ 0; Hjk 6¼ 0; Ω0 ij AΩ0nec Note that G21 is the covariance matrix of the state variables x. The required entries in G21 for computing Ω0ij , that is, the covariance of ri and rj , are those corresponding to the covariance of states related to ri and rj , that is, the states that appear in the equations of ri and rj . For example, if there are ni states associated with ri , and nj states associated with rj , then for computing Ω0ij , only ni 3 nj entries in Ω0 need to be computed. Since only the local state variables are associated with a specific measurement, the number ni and nj are typically small, so the number of necessary entries in G21 is also small. From the procedure described above, it can be seen that the variances of Lagrange multipliers rely only on local information, that is, the covariance of neighboring state variables. Therefore the number of necessary entries in G21 per parameter is independent of the system size. Given ðG21 Þnec , Λ0ii can be obtained by Eqs. (6.40) and (6.38) in the reverse direction. Next, the procedure for efficiently computing ðG21 Þnec will be presented [20]. An efficient algorithm for computing a specific subset of entries in the inverse of a sparse matrix is presented in Refs. [22,23]. After minor modifications, this method can be used to compute any desired subset conveniently. In a fully observable system the gain matrix, G, is symmetric, nonsingular, and positive definite. Applying Cholesky factorization, it can be decomposed as

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G 5 L DL T

(6.42)

where L is a unit lower triangular matrix, and D is a diagonal matrix. It is easy to verify that G21 5 D21 L21 1 I 2 LT G21 (6.43) Note that on the right-hand side, D21 L21 is lower triangular, and I 2 L T is strictly upper triangular. Therefore D21 L21 is irrelevant to the computation of the upper triangular entries of G21 . Let us define the sparse inverse subset of G21 as n o 21

(6.44) G sp 5 G21 kl jZkl 6¼ 0; k; l 5 1; 2; . . .; n where Z 5 L 1 D 1 L T . It is proven in Ref. [22] that the elements of ðG21 Þsp can be computed in terms of the nonzero entries of L and other entries in ðG21 Þsp :

G21

52 ij

n X

Lki G21 kj

ði , jÞ

(6.45)

k5i11 n X 21

21 G ii 5 Dii 2 Lki G21 ki

(6.46)

k5i11

21

G ij 5 G21 ji

ði . jÞ

(6.47)

In other words the computation of the entries in ðG21 Þsp is independent of the rest of G21 . Starting from ½G21 nn , all the entries in ðG21 Þsp can be computed using Eqs. (6.45)(6.47) iteratively. In general, the subsets ðG21 Þnec and ðG21 Þsp are not equal, and there is no guarantee that entries of ðG21 Þnec necessarily belong to ðG21 Þsp . However, it is fortunate that all the nonzero entries in G have locations corresponding to entries belonging to ðG21 Þsp . In other words, if Gij 6¼ 0, we have ½G21 ij AðG21 Þsp . Hence, to let ðG21 Þnec AðG21 Þsp , simply let the entries in G with locations corresponding to elements in ðG21 Þnec be recorded as nonzeros (zeros in sparse matrices are generally not stored), while they may remain zero numerically. In this way, all the entries of ðG21 Þnec will belong to the expanded ðG21 Þsp and computed by Eqs. (6.45)(6.47). The algorithm of computing NLMs is summarized as below. 1. Perform state estimation Eq. (6.3).

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2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

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Compute λ by Eq. (6.8). Determine the structure of Ω0nec by Eq. (6.39). Determine the structure of ðG21 Þnec by Eq. (6.41). Add the entries in G corresponding to the elements of ðG21 Þnec as nonzero. Factorize G as Eq. (6.42). Compute the elements of ðG21 Þnec by Eqs. (6.45)(6.47). 0 Compute the elements of Ωrec by Eq. (6.40). Compute the diagonals of Λ0 by Eq. (6.38). Compute the diagonals of Λ by Eq. (6.34). Compute every entry of λN by Eq. (6.24).

6.2.3 Detectability and identifiability of parameter and measurement errors The general conditions for detectability and identifiability of parameter errors are derived in Ref. [17]. These can be viewed as natural extensions of the corresponding conditions derived for analog measurement errors in Ref. [21]. Definition 6.1. If Λ matrix in Eq. (6.21) has a “null” column, the corresponding parameter will be defined as “critical.” Theorem 6.5. Errors in critical parameters are not detectable. Definition 6.2. If two columns of Λ in Eq. (6.21) are linearly dependent, the corresponding parameters will be defined as a “critical parameter pair.” In a similar vein, if a column of Λ in Eq. (6.21) and a column of A in Eq. (6.20) are linearly dependent, the corresponding parameter and measurement will be defined as a “critical parameter-measurement pair.” Theorem 6.6. Errors in a critical pair are not identifiable. Definition 6.3. Assume that k 5 p 1 q, then p parameters and q measurements will be defined as a critical k-tuple, if the corresponding p columns of Λ defined by Eq. (6.21) and q columns of A defined by Eq. (6.20) are linearly dependent. Theorem 6.7. Errors in a critical parameter-measurement k-tuple are not identifiable.

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The detailed proof of the theorems given earlier can be found in Ref. [17]. When the error in a parameter is not detectable, it is impossible to evaluate the NLM, since the denominator of Eq. (6.24) will be zero. When the errors in a group of parameters and/or measurements are not identifiable, their NLMs and/or NRs will always have the same value; thus it is impossible to find the truly erroneous quantities using the LNLM/NR criteria.

6.3 System monitoring The widespread deployment of PMUs on power transmission grids has made possible the real-time monitoring and control of power system dynamics. However, these functions cannot be reliably achieved without the development of a fast and robust DSE method. In this subsection the motivations, the concepts, as well as the methods used for DSE are discussed.

6.3.1 Motivations for dynamic state estimation With the increasing penetration of distributed energy resources (DERs), flexible loads, and microgrids, the power system has been subject to emerging dynamics. For example, the stochastic and intermittent characteristics of DERs increase the probability of sudden changes in the bus voltage phasors in a short time-frame [24]. These changes are mainly driven by the abrupt changes in active and/or reactive power injections, which further cause changes in the generator’s state variables, that is, rotor speeds and rotor angles. The majority of today’s monitoring and control tools at the control center EMS are based on steady-state power system models, such as SSE, which cannot capture the system dynamics [25]. This limitation is primarily due to the fact that the EMS functions rely on the SCADA systems that have slow scan rates and no timestamps. Therefore the state estimates are updated every few seconds to minutes, and most of the control schemes associated with the generators or the FACTS devices are based on locally available information and measurements. To address these challenges the SSE needs to be revisited and new monitoring tool, that is, DSE is imperative. The benefits of using DSE include but are not limited to the following: • Improved oscillations monitoring: The estimated dynamic state variables can be used to carry out modal analysis [26], and the identified

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modes can then be utilized to adaptively tune power system stabilizers (PSS), thereby achieving better damping of interarea modes of oscillation and improving system stability. Recall that the effectiveness of conventional PSS using local measurements may be limited by the observability of the modes in the signal. Using the estimated states of entire regions as opposed to local measurements will increase the stabilizer’s response to interarea modes if the generator has significant influence on such modes. Note that there are several ways for monitoring the entire regions/systems via DSE, namely, the hierarchical and distributed DSE and the centralized DSE using high-performance computing technique [27,28] or reduced order model of the interested areas of the power system [29]. Hierarchical and distributed DSE methods are first implemented locally to monitor small areas, and their results are submitted to the control center for further processing. This is the widely used strategy in the current literature. The highperformance computing technique-based DSE for large-scale systems is in its infancy and merits further research. Enhanced hierarchical decentralized control [3032]: The availability of local and wide-area dynamic states obtained from DSE enables the design of effective local and wide-area controls; for instance, the estimated rotor speed and other states can be used as input signals to control excitation systems of synchronous machines [31,32] or of FACTS devices [30] so as to damp out oscillations. The implementation can be in either fully decentralized or hierarchically decentralized manner; on the other hand, the raw measurements are typically used as direct control inputs, and if the noise level is high, the controller performance could be significantly influenced; if the measurements are corrupted with gross errors, the controllers may even fail and provide misleading results. With the design of robust DSE the measurement noise can be effectively filtered out and the bad data are detected and processed, enhancing the reliability of the developed controllers. Improved dependability and reliability of protection systems [3335]: DSE combines the model outputs as well as the real-time measurements to obtain an optimal estimate of the most likelihood of the interested system states. The systems may be a synchronous machine, a transformer, and a DER unit. By checking the consistency between the model outputs and the measured response, the innovation vector is able to tell us if there is any abnormality. This criterion has been leveraged by researchers to design novel setting-less protection scheme. In particular, by testing the consistency between the PMU or MUs

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measurements and the dynamical model of the protection zone, both internal and external faults can be effectively detected without any a priori protection relay settings, yielding more reliable protection systems compared with the traditional coordinated settings-based schemes; the estimated online dynamic states can be utilized to initiate effective generator out-of-step protections [35] and transient stability monitoring based on the extended equal-area criterion or the energy function approach [35]; furthermore, fast state estimation is a prerequisite for the implementation of system integrity protection schemes that can prevent blackouts. Enhanced reliability of the system models and initial states utilized for dynamic security assessment (DSA) [36]: DSA requires the availability of accurate models of the generators and their associated controllers, of the composite loads and of the special protection schemes, to name a few. By developing DSE, both the static and dynamic models can be validated [37], and if incorrect parameter values are identified, they can be included as additional state variables in DSE for parameter estimation and calibration, yielding improved system models [38] and more reliable DSA; in addition, with the increasing penetration of DERs and flexible loads, the steady-state assumption may not yield reasonable results of the power flowbased state initiation for transient dynamic simulations. As a result, the DSA results can be misleading and lead to unsafe planning and control coordination. With DSE, the system dynamic states are continuously monitored and can be directly leveraged for DSA initialization. Other applications include but are not limited to improved synchrophasor data quality and cyber security leveraging the model information, such as filtering out measurement error that is modeled as Gaussian or non-Gaussian distribution [39,40], detecting bad and delayed measurements or recovering missing data [41]; enhanced synchronous generators coherency identification and dynamic model reduction [42] using the estimated dynamic states and parameters; enhanced bus frequency and center of inertia estimation [43,44]; and the detection of failures in controllers, such as exciter, PSS, and governor.

6.3.2 Problem formulation of dynamic state estimation For a power system, its characteristics can be described by the following DAEs:

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x_ 5 f ðx; y; u; pÞ 0 5 cðx; y; u; pÞ

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(6.49)

where x and y represent system dynamic and algebraic (or static) state vectors, respectively; note that the algebraic equations include those that are associated with power flow and generator stators [29]; u is the system input vector that drives the state transition; p represents system parameters; and f and c are nonlinear functions. An equivalent but more convenient form of (6.49) for state estimation is expressed as x_ 5 f ðx; y; u; pÞ (6.50) y 5 gðx; u; pÞ where g is the inverse function of y that is derived from Eq. (6.49). The use of Eq. (6.50) is because some of the algebraic variables can be measured by PMUs or intelligent electronic devices, and as a result, it is easy to build the measurement functions of dynamic state variables. For example, if the detailed two-axis model with IEEE-DC1A exciter and TGOV1 turbine-governor is considered, we will obtain a ninth-order model expressed by the following DAEs: Two-axis generator differential equations: 2Hi dωi 5 TMi 2 Pei 2 Dðωi 2 ωs Þ=ωs ωs dt dδi 5 ωi 2 ωs dt dE0qi T 0doi 5 2 E0qi 2 Xdi 2 X 0di Idi 1 Efdi dt dE0 T 0qoi di 5 2 E0di 2 Xqi 2 X 0qi Iqi dt

(6.51)

IEEE-DC1A exciter differential equations: dEfdi 5 2 KEi 1 SEi Efdi Efdi 1 VRi dt dVFi KFi KFi 5 2 VFi 1 TFi VRi 2 KEi 1 SEi Efdi Efdi dt TEi TEi dVRi 5 2 VRi 1 KAi Vrefi 2 VFi 2 Vi TAi dt

TEi

(6.52)

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TGOV1 turbine-governor differential equations: TCHi

dTMi 5 2 TMi 1 PSVi dt

! dPSVi 1 ωi TSVi 21 5 2 PSVi 1 PCi 2 RDi ωs dt

(6.53)

Algebraic equations: Vdi 5 Vi cosθi sinδi 2 Vi sinθi cosδi Vqi 5 Vi cosθi cosδi 1 Vi sinθi sinδi E0qi 2 Vqi Vdi 2 E0di Idi 5 I 5 qi X 0di X 0qi Pei 5 Vdi Idi 1 Vqi Iqi Qei 5 2 Vdi Iqi 1 Vqi Idi

(6.54)

where ωi and δi are generator rotor speed and angle; E 0di and E 0qi generator d-axis and q-axis transient voltages; Efdi , VFi , VRi , TMi and PSVi are field voltage, scaled output of the stabilizing transformer and scaled output of the amplifier, synchronous machine mechanical torque, and steam valve position, respectively; Xdi , Xqi , E0di , and X 0qi are generator parameters; T 0doi , T 0qoi , TEi , TFi , TAi , TCHi , and TSVi are time constants; KEi , KFi , and KAi are exciter gains; RDi is governor droop constant; V and θ are the terminal bus voltage magnitude and phase angle, respectively; Pe and Qe are the active and reactive electrical power outputs; Id and Iq are the d- and q-axis currents, respectively. Vrefi and PCi are the reference voltage and the governor input power. It is clear from Eq. (6.54) that instead of expressing the zero-equality constraints such as Eq. (6.49), the formulation Eq. (6.50) for the real and reactive power of generators can be directly measured by PMUs. This makes the construction of measurement equations for the state-space model much more straightforward. When the system is mainly driven by small changes of loads and DERs, the generators and other controllers are able to absorb almost “instantly” these slow changes, yielding negligible changes of the dynamic states x, that is, x_ D0. As a result, the system is mathematically characterized by

0Df ðx; y; u; pÞ y 5 gðx; u; pÞ

(6.55)

where slowly varying algebraic variables y are of interest. In real-time operation, since system inputs are usually not perfectly known and the

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parameters are always inaccurate to a certain extent, state estimators capable of processing measurement snapshots are developed, including static, forecasting-aided, and tracking state estimators. It is worth pointing out that at the distribution system levels, individual loads or DERs may change abruptly enough for the local controllers to be subject to disturbances, requiring the dynamic model Eq. (6.50) to be resorted to. For practical applications the continuous-time models are transformed into their discrete-time state-space forms through numerical integration methods, such as Euler, Trapezoidal, or RungeKutta-based ones [30]. For example, after time-discretization, Eq. (6.50) can be approximated as xk 5 f xk21 ; yk21 ; uk ; p 1 wk (6.56) yk 5 gðxk ; uk ; pÞ 1 vk where wk and vk are error terms. Specifically, wk includes model timediscretization errors, model uncertainties, as well as uncertain/ unknown inputs. vk may include model time-discretization errors, model uncertainties, and measurement errors. Except for the direct measurements of algebraic variables, there are other ones that show either linear or nonlinear relationship with the dynamic state variables, such frequency and current phasors, yielding a complete measurement vector zk . Then, a more general dynamic state-space model for state estimation is xk 5 f xk21 ; yk21 ; uk ; p 1 wk (6.57) zk 5 hðxk ; uk ; pÞ 1 ek where h is the nonlinear measurement function; ek accounts for measurement error. The noise/errors wk and ek are usually assumed to be normally distributed with zero means and covariance matrices, Qk and Rk . Due to the complex combination of various error terms, they typically do not follow a Gaussian distribution. Note that the conventional RTU measurements can only be used for quasisteady state estimation. For widearea estimation under transient conditions, synchrophasor measurements may be the only useful source of information. At the local level (e.g., a generator station, a power transformer, or a FACTS controller), digital recorders or dedicated protection devices can also provide the required synchronized information to carry out a DSE.

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6.3.3 Unified framework for Bayesian dynamic state estimation through nonlinear regression In this section the Bayesian state estimators for a general dynamic system DSE is formulated. Then, a unified DSE framework is developed that includes the well-known extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), particle filter (PF), and their variants. To further enhance their robustness to non-Gaussian noise, bad data, etc., a unified robust DSE framework is proposed. 6.3.3.1 Bayesian state estimators for nonlinear dynamic system models To estimate the system dynamic states a two-step procedure is applied, namely, prediction step using the first equation in Eq. (6.57), which is a Markov model, and a filtering/update step using the second equation in Eq. (6.57). Given a state estimate at time step k 2 1, x^ k21jk21 with its covariance matrix Pk21jk21 , the predicted state is calculated from the first equation in Eq. (6.57) directly or through a set of points drawn from the distribution that are following the probability distribution of x^ k21jk21 , which is dependent on the assumed probability distributions of wk . As for the filtering step, the predictions are used together with the observations at time sample k to estimate the state vector and its covariance matrix. This can be casted as a Bayesian estimation approach as explained next. Note that u and p have been dropped for simplicity in the sequel. From a Bayesian perspective the DSE problem consists of recursively calculating some degree of belief in the state vector xk at time k, given the data z1:k up to time k. Thus it is required to calculate the conditional probability density function (pdf), pðxk jz1:k Þ. In principle, pðxk jz1:k Þ may be obtained recursively in two steps, which are the prediction and the update step. Suppose that pðxk21 jz1:k21 Þ at time k 2 1 is available. Thus the prediction step involves applying the first equation in Eq. (6.57) to obtain the prior pdf of the state at time k using the ChapmanKolmogorov equation ð pðxk jz1:k21 Þ 5 pðxk jxk21 Þpðxk21 jz1:k-1 Þdxk21 : (6.58) The probabilistic model of the state evolution pðxk jxk21 Þ is defined by the system equation and the assumed probability distribution of wk . At

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time k, when the measurements/observations are available, the update step can be performed via Bayes’ rule expressed as pðxk jz1:k Þ 5

pðzk jxk Þpðxk jz1:k21 Þ ; pðzk jz1:k21 Þ

where the normalizing constant is given by ð pðzk jz1:k21 Þ 5 pðzk jxk Þpðxk jz1:k21 Þdxk ;

(6.59)

(6.60)

depends on the likelihood function pðzk jxk Þ, which is defined by the observation model in the second equation of Eq. (6.57) and the assumed probability distribution of ek . The recurrence relations Eqs. (6.58) and (6.59) form the basis for the optimal Bayesian solution. However, this recursive propagation of the posterior density is only a conceptual solution in general; it cannot be determined analytically for the nonlinear dynamic system model. It is in this sense that the EKF [45,46], UKF [4749], EnKF [50,51], PF [5254], and their variants only approximate the optimal Bayesian solution under the Gaussian noise assumption. Furthermore, when the dynamic system model is correct, the better the statistical efficiency (accuracy) and the robustness of the previous state vector estimates and its error covariance matrix, the better the efficiency and robustness of the state prediction. On the other hand, the quality of the filtered/ updated state vector relies heavily on the quality of the received observations. Therefore to obtain reliable state estimates for dynamic systems, robust state prediction and state filtering with good efficiency are required. 6.3.3.2 Proposed unified framework for nonlinear Bayesian dynamic state estimation Let us define x^ kjk21 5 xk 2 ζ k , where x^ kjk21 is the predicted state from iterated EKF (IEKF), UKF, EnKF, PF or their variants; xk is the true state at time sample

k; ζ k is the error between the true state and its prediction T and E ζ k ζ k 5 Pkjk21 is the prediction error covariance matrix. The objective of the maximum a posterior estimation is to find the state vector xk that maximizes the probability pðzk jxk Þpðxk jz1:k21 Þ, yielding the following maximum likelihood objective function under Gaussian assumption:

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T J ðxk Þ 5 ðzk 2hðxk ÞÞT R21 ^ kjk21 P21 ^ kjk21 k ðzk 2 hðxk ÞÞ 1 xk 2 x kjk21 xk 2 x (6.61) where x^ kjk21 is the predicted state that is calculated using Eq. (6.58) and Pkjk21 is the prediction error covariance matrix. Instead of solving for Eq. (6.61) as it is usually performed, we first put the regression model as

zk hðxk Þ e 5 1 k ; x^ kjk21 2 ζk xk

(6.62)

which can be put in matrix form as z~ k 5 h~ ðxk Þ 1 w ~ k: (6.63)

0 Rk Let E w ~ kw 5 Sk STk , where Sk is calculated using ~ Tk 5 0 Pkjk21 the Cholesky decomposition. We premultiply Eq. (6.63) by Sk 21 to get a new regression model yk 5 mðxk Þ 1 ηk with an uncorrelated additive error vector ηk . Finally we estimate the state vector xk by minimizing a quadratic objective function defined as T 2 (6.64) J ðxk Þ 5 yk 2mðxk Þ yk 2 mðxk Þ 5 :yk 2mðxk Þ: : Applying a GaussNewton’s iterative method to minimize (6.64), we get T 21 T x^ i11 5 x ^ 1 M M M y 2 m x^ ikjk ; kjk21 k k k k kjk

(6.65)

Hk , Hk 5 @[email protected] x5^xikjk , I is the where Mk 5 @[email protected] I identity matrix and i is the iteration counter. Upon convergence the state 21 error covariance matrix is calculated as Pkjk 5 MTk Mk . By using the inverse matrix lemma, we can show that Eq. (6.65) is the so-called IEKF [55]. All what we need to do is to apply, in the state filtering step, a first-order Taylor series approximation to the nonlinear measurement function hðxk Þ. If the statistical linearization [39,40] is applied using the sigma point, it can be shown this would yield the UKF method. While, if the statistical linearization is applied using the ensembles, it can be shown this would result in the EnKF method. x5^xikjk

5 S21 k

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A general nonlinear regression framework for IEKF, UKF, and EnKF: For these filters, we have the following general expression of the state vector estimate at sample time k: 21 i11 zz x^ kjk 5 x^ kjk21 1 Pxz kjk21 Pkjk21 1R k (6.66) T i 21 zk 2 h x^ ikjk 2 Pxz P x ^ 2 x ^ ; kjk21 kjk21 kjk21 kjk with its error covariance matrix given by 21 T zz xz Pkjk 5 Pkjk21 2 Pxz P 1R P : k kjk21 kjk21 kjk21

(6.67)

For IEKF, we have

T Pxz kjk21 5 Pkjk21 Hk T : Pzz kjk21 5 Hk Pkjk21 Hk

For UKF, we have 8 2n T X > > i i xz > ^ kjk21 P 5 w χ 2 x ^ 2 Ζ Ζ > i kjk21 kjk21 kjk21 < kjk21 i50

2n T X > > i i zz > ^ ^ P 5 w Ζ 2 Ζ 2 Ζ Ζ > i kjk21 kjk21 kjk21 kjk21 : kjk21

(6.68)

;

(6.69)

i50

where n is the number of state variables, χ ikjk21 are the propagated sigma points through the nonlinear function with their weights wi , Ζikjk21 are and the predicted observations and Ζikjk21 5 h χ ikjk21 , ^ kjk21 5 Ζ

2n P i50

wi Ζikjk21 .

For EnKF, we have

8 N T > 1X i i > xz T ^ > Ζ P 5 P H 5 ξ 2 x ^ 2 Ζ kjk21 k kjk21 kj k21 > kjk21 kjk21 < N i51 kjk21 N T ; > 1X > i i zz T > ^ ^ Ζkjk21 2 Ζkjk21 Ζkjk21 2 Ζkjk21 > : Pkjk21 5 Hk Pkjk21 Hk 5 N i51

(6.70)

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where N is the number of ensembles; ξ ikjk21 are the propagated ensembles through the nonlinear function expressed as x^ kjk21 5

N N X 1X ^ kjk21 5 1 ξ ikjk21 ; Ζikjk21 5 h ξikjk21 ; Ζ Ζi : N i51 N i51 kjk21

(6.71) As for the PF, it is based on Monte Carlo simulations and the resampling technique, which seems to be a different approach from that of EnKF. However, it has been shown in Ref. [56] that the adaptive Gaussian mixture filter can provide a bridge between the EnKF and the PF. Therefore the general framework can be resorted to as well. In the future work, we will investigate the specific form for PF. Remark:. The least squares estimator on which the foregoing filters are based on assumes Gaussian noise and is vulnerable to outliers. We will build next a framework that allows us to develop robust DSEs that exhibit good performance when the process and observation noises follow thicktailed probability distributions.

6.3.3.3 Proposed framework for robustifying the Bayesian dynamic state estimation If we directly premultiply Eq. (6.63) by the matrix Sk as proposed to derive the classical DSE, the outliers present in the data will strongly bias the state estimates [55]. Therefore it is necessary to detect and suppress the outliers before that step. This is carried out by calculating the projection statistics [55], PSi , for each of the two-dimensional row vectors lTi , ~ given by i 5 1; . . .; m 1 n of the innovation matrix Z

~ 5 zk21 2 hð^xk21jk21 Þzk 2 h x^ kjk21 x^ k21jk21 x^ kjk21 : Z (6.72) Recall that the projection statistics are defined as jlTi νj 2 medk lTk vj j T ; PSi 5 max T :vj : 5 1 1:4826medγ jlγ vj 2 medk lk vj j

(6.73)

where the maximum is calculated over j 5 1; . . .; m 1 n. Here vj is the vector of unit length that originates from the coordinate medians, M , and points in the direction of the jth data point, lj . Once the PSi are

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calculated, the outliers that are flagged by the decision rule, PSi . χ22;0:975 , are downweighted using the following weight function: d2 ϖi 5 min 1; 2 ; (6.74) PSi where the value of d is chosen to achieve a good statistical efficiency at the Gaussian distribution (i.e., d 5 1.5) while ensuring high robustness under contamination [39,40]. Now, we can execute the prewhitening step by premultiplying the regression model given by Eq. (6.63) by S21 k , yielding 21 ~ S21 ~ k 5 S21 ~ k; k z k hðxk Þ 1 Sk w

(6.75)

which can be rewritten in a compact form as yk 5 mðxk Þ 1 ηk (6.76)

where E ηk ηTk 5 I and I is the identity matrix. It should be noted that in the prewhitening step, it is the robust estimation of the covariance matrix Pk21jk21 based on the total influence function and PS that makes the prediction error covariance matrix Pkjk21 robust, which in turn results in a robust prewhitening matrix S21 k against outliers. At this stage a robust version of the IEKF, UKF, EnKF, PF, and their variants can be derived using the regression model Eq. (6.76). To estimate the state vector xk , we propose to use a generalized maximum (GM)-likelihood-estimator defined as the minimum of an objective function JðxÞ JðxÞ 5

m X

ϖ2i ρ rSi ;

(6.77)

i51

where ρð:Þ is a nonlinear convex function; ϖi is calculated by Eq. (6.74); rSi 5 ri =sϖi is the standardized residual; and ri 5 yi 2 mðxÞ is the residual. The scale estimator s is defined as s 5 bUmediani jri j for a symmetric probability distribution and as s 5 cU lomedi 5 1;...;m lomedj6¼i jri 2 rj j for an asymmetric probability distribution, where the constant b and c are correction factors to achieve Fisher consistency and unbiasedness at a given probability distribution. Here, lomed denotes the low median defined as the [(m 1 1)/2]-th order statistic out of m numbers and [.] is the integer operator. Regarding the ρðUÞ function, a good choice would be the Huber-ρ function [55] expressed as

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8 < ρ rSi 5 :

1 2 r ; 2 Si

for jrSi j , λ

λjrSi j 2 λ =2; 2

;

(6.78)

elesewhere

where the breakpoint λ between the quadratic and the linear segment of ρð:Þ will be chosen such that a good statistical efficiency is achieved at both the Gaussian and the Laplacian probability distribution. Note that the quadratic segment enables the estimator to discriminate between the good and the bad leverage point since in the case of a good leverage point, ϖ2i ρðrsi Þ 5 ðri =sÞ2 =2, which is the conventional least squares criterion. Recall that a leverage point is a data point distant from the bulk of the point cloud in the space spanned by the row vectors of the Jacobian matrix Mk . The reader is referred to Ref. [55] for further details. In power systems, leverage points are power injection measurements on buses with many incident branches and power flow measurements on lines with relatively small reactance compared to the others. To minimize Eq. (6.77), one takes its partial derivative and set it equal to zero, yielding m @JðxÞ X ϖi a i 5 ψ rSi 5 0; 2 (6.79) @x s i51 where ψ rSi 5 @ρ rSi [email protected] is the so-called ψ function and ai is the ith row of the Jacobian matrix Mk . By dividing and multiplying standardized residual, rSi , each term of the summation in Eq. (6.79) and putting the expression in a matrix form, we get

~ ðy 2 mðxÞÞ 5 0; Mk T Q (6.80) ~ 5 diag q rSi and q rSi 5 ψ rSi =rSi . Applying a first-order where Q Taylor series expansion to mðxÞ, we get the iteratively reweighted least squares algorithm [55]. As an example, the correction of the state vector at the jth iteration for the GM-IEKF is given by 21 j11 ~ ðjÞ M ~ ðjÞ y 2 m xj : Δ^x kjk 5 MT Q MT Q (6.81) A good stopping rule is :Δ^xkjk : 5 :^xkjk 2 x^ kjk :N # 1022 : j11

j11

j

(6.82)

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Upon convergence of the iterative algorithm Eq. (6.82) the error covariance matrix Pkjk needs to be updated so that the state forecasting for the next time sample can be performed. To this end the theorem proved in Ref. [55] can be used. It states that when the number of the observation is very large, Pkjk is approximately equal to the asymptotic covariance matrix of the total influence function IFðrS ; FÞ of the GM-estimator at the probability distribution F, that is Pkjk 5 E½IFðrS ; FÞIFðrS ; FÞT . Recall that the total influence function is defined as the derivative of the estimator, x^ k , with respect to the fraction of contamination, ε, at the assumed probability distribution F, of the good data points. In the derivations the ε-contamination model, F 5 ð1 2 εÞF 1 ε Δr , is used. Note that F can be chosen to be the Gaussian distribution, Φ, or the Laplacian distribution or the Cauchy distribution, or any other distribution of the process and observation noise that may be assumed. For instance, for the GM-IEKF, we get 2

21 T 21 E ψ rS Φ Pkjk 5 E IFUIFT 5 0 i 2 MTk Mk Mk Qϖ Mk MTk Mk ; EΦ ψ rSi (6.83) 2 where Mk is the Jacobian matrix at time sample k and Qϖ 5 diag ϖi . In the proposed GM-estimator framework for robustifying DSEs, the matrix ~ and the weights ϖi are used to bound the influence of the residual and Q of the structural outliers, respectively, guaranteeing a robust estimation of the state vector, while the matrix Qϖ contributes to the robust estimation of estimation error covariance matrix. 6.3.3.4 Decentralized versus centralized dynamic state estimation for power system There are two ways of DSE implementations, namely, the centralized and decentralized ones. Centralized DSE assumes that the system is observable by PMUs and Kron reduction can be carried out to reduce the system to the generators’ terminals. In addition, it requires accurate knowledge of each system component parameters as well as the real-time wide-area PMU measurements. By contrast the decentralized DSE is implemented using only local PMU measurements. It assumes that the terminal bus of the interested dynamic components is observed by PMU measurements and local observability of dynamic states is satisfied. If a generator terminal bus is not equipped with PMU, a PMU-based linear state estimator for

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that local system should be performed first. Then, the estimated measurements at the interested terminal buses can be obtained and the decentralized DSE is executed. Note that the local phasor data concentrator is in charge of communicating and processing the PMU measurements. Centralized DSE allows us to achieve global monitoring and control applications and has good robustness to data quality and security issues as the measurement redundancy is high. However, it has large computational burden and strong assumptions about the accuracy of the whole dynamic system models. By contrast, decentralized DSE only needs local measurements at the terminal buses and the dynamic model of interested components, which is fast to execute and not impacted by the model inaccuracy of other system components. However, the local measurement redundancy is low, and therefore decentralized DSE has difficulties in dealing with PMU data quality and security issues. On the other hand, with a decentralized DSE, only local controls are implemented. If coordinated control is deployed between different local DSEs, additional communication bandwidth is required, making the comparison with the communication cost of the centralized DSE difficult to assess. Choosing between the two implementation schemes depends on the applications and the communication infrastructures being used. It should be noted that for both centralized and decentralized DSE-based applications, time tags of the estimated dynamic state variables are required when the communication network is involved.

6.4 Protective relaying Sometimes faults may occur inside power systems. When the faults occur, protective relays should quickly operate to isolate the faulted devices (or the protection zone), to maximize service continuity, minimize the extent and time of the power outage, and ensure safety of human beings as well as the overall power system. The basic objectives of a protective relays mainly includes the following aspects. First, the relay should operate reliability, which means that the relay should operate corresponding to all kinds of internal faults in its protection zone (also known as “dependability”), and should not operate during all other scenarios such as external faults or system transients (also known as “security”). Second, the relay should isolate the faulted device as soon as possible (in the order tens of milliseconds, especially in high voltage power systems).

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Over the years, protective relays are evolving with higher reliability. Nevertheless, statistically speaking, the industry in the United States and abroad is still experiencing an average of 10% misoperations [57]. The challenges of the present protective relaying methods are summarized as follows: first, the protection functions are based on limited information, that is, the relays are lack of an overall picture of the health condition of the device under protection, which may result in compromised reliability of the relays. Second, complex coordination among different protection functions is often required, which increases the possibility of relay miscoordination and misoperation. Third, there are certain types of faults that are hard to be quickly detected using present protective relays, such as downed conductors in distribution systems, high impedance faults, and faults near the neutral of solid grounded systems. With the development of substation automation the data-acquisition system could be achieved using the MUs that are separated from the protective relays. Instead of being physically connected to the analog channels, the protective relays are able to directly access the digital measurement data from the MUs through the process bus. This brings new opportunities for improving the state-of-the-art protective relays. First, the protective relays could easily access more measurement channels from the process bus. Second, since the MUs obtain “points on wave” measurements instead of phasor measurements, the information inside each measurement channel is also enhanced.

6.4.1 Theoretical basis of dynamic state estimationbased protection Therefore with aforementioned challenges and opportunities, a new group of approaches has been proposed in literatures: DSE-based protection [5766]. The method detects internal faults of the protection zone by checking the consistency between the “points on wave” (instantaneous) measurements and the dynamic model of the protection zone through DSE method. The “points on wave” measurements can include many physical quantities such as voltage, current, torque, rotating speed, and temperature. The dynamic model is simply a set of DAEs describing all the physical laws that the protection zone should obey. With the DSE procedure, a high consistency indicates a healthy condition of the protection zone, while a low consistency indicates an internal fault. In fact, the well-known current differential protection is one of the most reliable and straightforward protection approaches, and it does not require complex coordination among different protection functions. The DSE-based

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protection method can be treated as a generalization of the current differential protection: instead of only checking the Kirchhoff’s Current Laws (KCLs) of the protection zone, the method systematically checks all the physical laws that the protection zone should obey through the DSE procedure. As a result, the method provides a more complete monitoring of the overall protection zone and therefore better protection performances. The DSE-based protection approaches have been successfully applied in many power system devices, including transmission lines [57,58,60,61], microgrid circuits [59,65], transformers [62,64,66], and capacitor banks [63]. 6.4.1.1 Dynamic model of the protection zone The dynamic model of the protection zone can be expressed using a set of DAEs describing all the physical laws that the protection zone should obey. If the equations exhibit nonlinearities higher than second order, they are “quadratized” by the introduction of additional variables. The quadratized model is then cast into a standard syntax, as follows: dxðtÞ 1 Ceqc1 dt dxðtÞ 1 Ceqc2 0 5 Yeqx2 xðtÞ 1 Deqxd2 dt 8 9 < = D ^ E 0 5 Yeqx3 xðtÞ 1 xðtÞT Fieqxx3 xðtÞ 1 Ceqc3 : ; ^ iðtÞ 5 Yeqx1 xðtÞ 1 Deqxd1

(6.84)

where xðtÞ and iðtÞ represent the states and the terminal measurements of the model, respectively; other matrices (Y, D, F, and C) are coefficient matrices. Usually terminal voltages are included in xðtÞ as well as other internal states. Next, the measurements are expressed as functions of the protection zone state: 8 9 ^ < = dxðtÞ T i zðtÞ 5 Yzx xðtÞ 1 xðtÞ Fzx xðtÞ 1 Dzx 1 Cz (6.85) : ; dt ^ where zðtÞ is the measurements of the protection zone, where the zeros (equality constraints) are included as well.

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6.4.1.2 Quantification of consistency through dynamic state estimation algorithm To solve Eq. (6.85), one can use EKF, constraint optimization method or unconstraint optimization method. Numerical experiments show that all three methods provide similar results. Here the unconstraint optimization method is selected as an example. The first step is to convert the set of DAEs in Eq. (6.85) to a set of algebraic equation without differential terms. Here the quadratic integration method [67] is adopted over a time step of 2Δt. Specifically, the differential equations are integrated over the two intervals ½t 2 2Δt; t and ½t 2 2Δt; tm , where Δt is the sampling interval, and tm 5 t 2 Δt. This process converts Eq. (6.85) into Eq. (6.86): 8 9 ^ < = zðt Þ 5 Y1m;x xðt; tm Þ 1 xðt; tm ÞT Fi1m;x xðt; tm Þ 1 C1m ðt 2 2Δt Þ : ; ^ C1m ðt 2 2Δt Þ 5 N1m;x xðt 8 2 2ΔtÞ 1 M1m iðt 2 2ΔtÞ 9 1 K1m (6.86) ^ < = zðtm Þ 5 Y2m;x xðt; tm Þ 1 xðt; tm ÞT Fi2m;x xðt; tm Þ 1 C2m ðt 2 2Δt Þ : ; ^ C2m ðt 2 2Δt Þ 5 N2m;x xðt 2 2ΔtÞ 1 M2m iðt 2 2ΔtÞ 1 K2m The compact notation can be obtained as follows: (6.87) zðt; tm Þ 5 hðxðt; tm ÞÞ zðtÞ xðtÞ where zðt; tm Þ 5 and xðt; tm Þ 5 . zðtm Þ xðtm Þ The second step is to solve the best estimates of state vector xðt; tm Þ. The estimation problem is

Min J 5 ðhðxðt; tm ÞÞ2zðt; tm ÞÞT Wðhðxðt; tm ÞÞ 2 zðt; tm ÞÞ (6.88) where W 5 diag 1=σ21 ; 1=σ22 ; . . . , and σi is the standard deviation of measurement i. Note that the zeros (equality constraints) in zðt; tm Þ are treated as measurements with a very small error (very small standard deviation). The best state estimate x^ ðt; tm Þ is computed by the following iterative algorithm until convergence: x^ ðt; tm Þν11 5 x^ ðt; tm Þν 2 ðHT WHÞ21 HT Wðhð^xðt; tm Þν Þ 2 zðt; tm ÞÞ (6.89) where the Jacobian matrix H 5 @hðxÞ[email protected]

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After solving x^ ðt; tm Þ the residuals r^ðt; tm Þ and NRs s^ðt; tm Þ are computed using the estimated state x^ ðt; tm Þ: r^ðt; tm Þ 5 hð^xðt; tm ÞÞ 2 zðt; tm Þ pﬃﬃﬃﬃﬃ s^ðt; tm Þ 5 WU^rðt; tm Þ

(6.90) (6.91)

Subsequently, the chi-square value ζðtÞ and the confidence level Pconf ðtÞ can be computed from the NRs s^ðt; tm Þ: ζðtÞ 5 s^ðt; tm ÞT s^ðt; tm Þ Pconf ðtÞ 5 P χ2 $ ζðtÞ 5 1 2 P ðζðtÞ; mv Þ

(6.92) (6.93)

where P ðζðtÞ; mv Þ is the probability of χ2 distribution given χ2 # ζðtÞ with mv 5 mz 2 mx degrees of freedom; mz and mx are total number of measurements and states in Eq. (6.87). In fact, both the chi-square value and the confidence level can be utilized to quantify the consistency between the measurements and dynamic model. A low chi-square value (or a high confidence level) indicates that the measurements and the dynamic model are consistent, while a high chi-square value (or a low confidence level) implies that, assuming no instrumentation error, there exists an internal fault. 6.4.1.3 Trip decision Here the confidence level is selected as an example to quantify the consistency between the measurements and the dynamic model, and for constructing the trip signal. For dependable and secure protection a user defined time delay for tripping is introduced. We use the following trip signal that includes a user defined delay τ delay and a reset time τ reset : ðt 1 2 Pconf ðt Þ dt (6.94) TripValueðtÞ 5 TripðtÞ 5

t2τ reset

1; 0;

if TripValueðtÞ $ τ delay if TripValueðtÞ , τ delay

(6.95)

Above equations guarantee that the trip signal is issued only when the confidence level remains consistently low for a time period until the trip signal goes to 1.0.

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6.4.2 Numerical experiments This section demonstrates the performances of the DSE-based protection approach in an example test system [57]. 6.4.2.1 Example test system: series compensated transmission line Here we use the protection function on a series compensated transmission line as an example. We compare the performance of the DSE-based protection method with typical legacy protection schemes for a series compensated power line. The compensation is located at one terminal of the line. The line ratings are 60 Hz, 500 kV, 83-mi long, 4330 MVA. The series capacitors are 0.148 mF (42.4% compensation). Three-phase voltage and current instantaneous measurements are installed at both terminals of the series compensated line, as shown in Fig. 6.2. The rest of the network is not shown. The measurement sampling rate is 80 samples per cycle. The source impedance is shown in Table 6.1. The R, L, G, and C matrices of the transmission line are provided in Table 6.2. The sequence parameters of the line are shown in Table 6.3.

3-Phase PTs

Legend 3-Phase CTs

Bypass switch Relay I

BUS A1

Buses

Three phase source

SCs Breakers Relay II

Fiber optic

SCs

Line under protection BUS A2 BUS A3

Figure 6.2 Example test system: series compensated transmission line.

Table 6.1 Source impedances. Parameter

Value

Positive sequence impedance Negative-sequence impedance Zero-sequence impedance

13:59+86:99 Ω 12:88+86:82 Ω 5:76+82:88 Ω

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Table 6.2 R, L, and C matrices of the line. Parameter matrices Value

3 0:104 0:089 0:089 0:088 0:088 6 0:089 0:104 0:089 0:088 0:088 7 6 7 6 0:089 0:089 0:104 0:088 0:088 7 Ω=mi 6 7 4 0:088 0:088 0:088 2:357 0:087 5 2:357 2 0:088 0:088 0:088 0:087 3 Series inductance (per 2:79 1:48 1:26 1:47 1:26 6 1:48 2:89 1:48 1:41 1:41 7 mile) 6 7 6 1:26 1:48 2:79 1:26 1:47 7 mH=mi 6 7 4 1:47 1:41 1:26 5:55 1:36 5 2 1:26 1:41 1:47 1:36 5:55 3 Shunt capacitance (per 9:01 22:11 20:75 21:33 20:50 6 22:11 mile) 9:55 22:11 20:94 20:94 7 6 7 6 20:75 22:11 9:01 20:50 21:33 7 6 7 nF=mi 4 21:33 20:94 20:50 5:52 20:58 5 20:50 20:94 21:33 20:58 5:52 Shunt conductance (per 05 3 5 mho=mi mile)

Series resistance (per mile)

2

Table 6.3 Sequence parameters of the line. Parameter

Value

Positive (negative) sequence series impedance (Z1 ) Zero-sequence series impedance (Z0 ) Positive (negative) sequence shunt susceptance (Y1 ) Zero-sequence shunt susceptance (Y0 )

42:53+88:34 Ω 142:71+74:56 Ω 0:341+90:0 mΩ 0:186+90:0 mΩ

6.4.2.2 Dynamic model of the series compensated transmission line The dynamic model of the series compensated transmission line consists of the following two parts, as shown in Fig. 6.3. The first part is the multisection transmission line model where each section is a π-equivalent transmission line model. The second part is the model of the three-phase series capacitors. 6.4.2.2.1 Dynamic model of section k in the multisection line

The dynamic model of section k in the multisection transmission line is shown in Fig. 6.4. Definitions of variables are shown as follows: vk ðtÞ and vk11 ðtÞ are the three-phase and neutral voltage vectors at both terminals of section k, iak ðtÞ and ibk ðtÞ are the three-phase and neutral current

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iSC1 (t )

iSC 2 (t ) ia1 (t )

Series capacitors vSC1 (t )

ib1 (t ) ia 2 (t )

ib 2 (t ) Section 2

Section 1

v2 (t )

vSC 2 (t ) v1 (t )

v3 (t )

ibn (t )

ian (t )

… … … …

Section n

vn (t )

vn+1 (t )

Figure 6.3 Dynamic model of the series compensated transmission line.

Side 1 i ak (t )

Matrix R

Side 2 i bk (t )

Matrix L

v k +1 (t )

v k (t ) i Lk (t )

Matrix G

Matrix C

Matrix G

Matrix C

Figure 6.4 Dynamic model of the section k in the multisection transmission line.

vectors at both terminals of section k, iLk ðtÞ is the three-phase and neutral current vector through the inductors; R, L, G, and C are series resistance, series inductance, shunt conductance, and shunt capacitance matrices of section k. The dynamic model can be formulated in compact form as follows: dvk ðtÞ 1 iLk ðtÞ dt dvk11 ðtÞ ibk ðtÞ 5 GUvk11 ðtÞ 1 CU 2 iLk ðtÞ dt diLk ðtÞ 0 5 2 vk ðtÞ 1 vk11 ðtÞ 1 RUiLk ðtÞ 1 LU dt

iak ðtÞ 5 GUvk ðtÞ 1 CU

(6.96)

In the syntax shown in Eq. (6.84) the corresponding matrices 2 3

vk ðtÞ G 0 In iak ðtÞ 4 5 , xðtÞ 5 vk11 ðtÞ , are iðtÞ 5 , Yeqx1 5 0 G 2In ibk ðtÞ iLk ðtÞ

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C 0 0 Deqx1 5 , Yeqx2 5 2In In R , and Deqx2 5 0 0 L ; 0 C 0 all other vectors and matrices are null and In is the identity matrix with dimension n, where n is the number of conductors of the transmission line. For three-phase transmission lines, n is equal to 4 or 5 with one or two neutral conductors.

6.4.2.2.2 Dynamic model of the three-phase series capacitors

The dynamic model of the three-phase series capacitors is shown in Fig. 6.5. Definitions of variables are as follows: vSC1 ðtÞ and vSC2 ðtÞ are the three-phase voltage vectors at both terminals; iSC1 ðtÞ and iSC2 ðtÞ are the three-phase current vectors at both terminals; CSC is the capacitance of the series capacitor per phase. The dynamic model can be formulated in compact form as follows: iSC1 ðtÞ 5 CSC UvSC1 ðtÞ 2 CSC UvSC2 ðtÞ iSC2 ðtÞ 5 2 CSC UvSC1 ðtÞ 1 CSC UvSC2 ðtÞ

(6.97)

In the syntax shown in Eq. (6.84) the corresponding matrices are

iSC1 ðtÞ vSC1 ðtÞ iðtÞ 5 ; xðtÞ 5 ; Yeqx1 5 0; Deqxd1 iSC2 ðtÞ vSC2 ðtÞ 3 2 0 0 2CSC 0 0 CSC 6 0 CSC 0 0 2CSC 0 7 7 6 7 6 6 0 0 CSC 0 0 2CSC 7 7; 6 56 0 0 CSC 0 0 7 7 6 2CSC 7 6 4 0 2CSC 0 0 CSC 0 5 0

0

2CSC

0

0

all other vectors and matrices are null.

Side 1 i SC1 (t )

CSC

vSC1 (t )

CSC

Side 2 i SC2 (t ) vSC2 (t )

CSC

Figure 6.5 Dynamic model of the three-phase series capacitors.

CSC

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6.4.2.2.3 Formulation of the overall dynamic model

After having the dynamic model for each specific part, the overall dynamic model can be obtained by simply combining the models of each part. The systematic procedure of combining them together is as follows: Step 1: Copy all the equations of each individual part. Step 2: Add equations corresponding to KCLs at each interface node. Step 3: Ensure the voltage variables at the same interface node are the same variable. 6.4.2.3 Legacy protection functions for comparison and corresponding settings We assumed the line is protected with two legacy protection functions: (1) distance protection (at side A1) and (2) line differential protection (alpha plane method [68]). The settings of the distance protection are as follows: line and ground distance zone 1 is 19:72+87:13 Ω, with 0.015 second delay and compensation factor k 5 2:39+ 2 19:48 Ω. Line and ground distance zone 2 is 30:80+87:13 Ω, with 0.15 second delay and compensation factor k 5 2:39+ 2 19:48 Ω. Line and ground distance zone 3 is 64:08+87:13 Ω, with 0.5 second delay and compensation factor k 5 2:39+ 2 19:48 Ω. The settings of the line differential protection are as follows. The zero-sequence current differential protection scheme with capacitive charging current compensation is used. The restraint region is between 1/ 6 and 6, with total angular extent 195 degrees. The relay trip logic is activated when at least one of the following thresholds is exceeded (1) phase current 6 kA, (2) 3I0 (zero-sequence) current 500 A, and (3) 3I2 (negative-sequence) current 500 A. The relay will trip when the trip logic is activated and the ratio falls outside the restraint region, with a delay of 0.015 second. The settings of the DSE-based protection are as follows. For consistency the intentional delay is also selected as τ delay 5 0:015 second and the reset time is τ reset 5 0:03 second. 6.4.2.4 Event study Here we use a high impedance phase A to ground internal fault as an example. The fault impedance is 300 Ω. It occurs at 60 miles from the side A1 and time 0.5 second. The three-phase voltage and current instantaneous measurements are shown in Fig. 6.6.

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Current_sideA1_A (A) Current_sideA1_B (A) Current_sideA1_C (A)

2403.0 A

–2384.9 A Current_sideA2_A (A) Current_sideA2_B (A) Current_sideA2_C (A)

1834.8 A

–1802.6 A Voltage_sideA1_A(bus_side) (V) Voltage_sideA1_B(bus_side) (V) Voltage_sideA1_C(bus_side) (V)

405.7 kV

–405.5 kV Voltage_sideA2_A (V) Voltage_sideA2_B (V) Voltage_sideA2_C (V)

413.5 kV

–422.1 kV 0.450 s

0.550 s

Figure 6.6 Current and voltage results of a phase A to ground high impedance internal fault.

Imaginary part of the impedance (ohm)

100

50

t = 0.515 s

t = 0.505 s t = 0.50 s

t = 0.52 s

0

t = 0.51 s

–50

0

50 100 150 200 Real part of the impedance (ohm)

250

300

Figure 6.7 Trace of impedance during a phase A to ground high impedance internal fault.

For the distance relay the impedance “seen” by the relay, superimposed on its characteristic, is shown in Fig. 6.7. The impedance stays outside of the relay characteristic. Thus the distance relay fails to detect this fault. For the line differential protective relay the phasor ratio trace of zero-sequence current, superimposed on the relay characteristic, is shown in Fig. 6.8. Along the trace the character “o” means the thresholds are not exceeded, while the character “x” means that the thresholds

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6

X Thresholds are exceeded O Thresholds are not exceed

Imaginary part of the ratio

4

2

0 2

–2

1

t = 0.505 s t = 0.51 s t = 0.52 s

0

t = 0.515 s

–4 –1

–6 –8

t = 0.50 s –1

–6

0

1

–4

2

–2

0

2

4

Real part of the ratio

Figure 6.8 Trace of ratio during a phase A to ground high impedance internal fault.

1005.2 A

Residual_Current_sideA2_A (A) Residual_Current_sideA2_B (A) Residual_Current_sideA2_C (A)

–1226.2 A 20.10 p.u.

Normalized_Current_sideA2_A (p.u.) Normalized_Current_sideA2_B (p.u.) Normalized_Current_sideA2_C (p.u.)

–24.52 p.u. 100.0 %

Confidence-level (%)

0.000% 1.000

Trip_Decision

0.000 0.450 s

0.550 s

Figure 6.9 Dynamic state estimationbased protection results of a phase A to ground high impedance internal fault.

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are exceeded. Prior to the fault, the ratio is in the restraint region, and none of the thresholds are exceeded (character “o”). During the fault the ratio exits the restraint region at 0.504 second and the threshold asserts (character “x”) at 0.520 second. The line is tripped at 0.520 1 0.015 second 5 0.535 second. For the DSE-based protective relay the results are depicted in Fig. 6.9. The first two sets of traces show the residuals and NRs of three-phase currents of side A2. The confidence level and the trip signal are given in the next two traces. The confidence level drops to near zero 0.2 ms after the fault initiation. The trip command is issued at 0.5154 second. Therefore for this high impedance internal fault, the distance relay fails to detect the fault, the differential relay and the DSE-based protective relay issue a trip command at 0.535 and 0.5154 second, respectively. We can observe that the DSE-based protective relay reliably detects this internal fault and trip this internal fault faster than legacy protection schemes.

6.5 Conclusion remarks In power system operation, state estimation serves as the core function for extracting reliable information from abundant but imperfect sensor measurement data. By fully exploiting the capabilities of estimation theories, operational tools for various objectives can be developed under the framework of state estimation. This chapter describes in detail the theories and implementations of such three state estimationbased applications for different aspects of system operation: modeling, monitoring, and protection. First, a systematic framework for the detection, identification, and correction of power system model parameters is presented. The core of this framework is the LNLM test. It provides a practical avenue for the cleaning of large-scale power system model dataset, and effective differentiation between modeling errors and measurement errors. A highly computationally efficient implementation of the approach is described, and the theoretical conditions for detecting and identifying model parameter errors are provided. Second, the real-time monitoring tool for a power system is developed based on DSE. Its motivations and concepts are discussed in detail. Then, the Bayesian framework motivated general DSE is formulated. Then, a

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unified DSE framework is developed that includes the well-known EKF, UKF, EnKF, PF, and their variants. To further enhance their robustness to non-Gaussian noise, bad data, etc., a unified robust DSE framework is proposed. Third, the DSE-based protective relay is demonstrated to ensure the safety and service continuity of the power system. It systematically examines the health condition of components of interest in real time. The relay can reliably and quickly detect internal faults in the protection zone by identifying any inconsistency between the “points on wave” measurements and the dynamic model of the protection zone. The dynamic model of the protection zone is a set of DAEs in time domain describing all physical laws that the zone should obey. Numerical experiments on an example series compensated transmission line prove its advantages toward legacy protection schemes.

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Further reading J. B. Zhao, A. Exposito, M. Netto, L. Mili, A. Abur, V. Terzija, I. Kamwa, B. Pal, A. K. Singh, J. Qi, Z. Huang, A. P. Sakis Meliopoulos, “Power System Dynamic State Estimation: Motivations, Definitions, Methodologies and Future Work,” IEEE Trans. Power Systems, vol. 34, no. 4, pp. 31883198, 2019.

CHAPTER SEVEN

Advanced machine learning applications to modern power systems Cong Feng, Mucun Sun, Morteza Dabbaghjamanesh, Yuanzhi Liu and Jie Zhang Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, United States

Contents 7.1 Introduction 7.2 Modern forecasting technology 7.2.1 Prior research work 7.2.2 Ensemble learning forecasting methodologies 7.2.3 Forecasting results 7.3 Machine learningbased control and optimization 7.3.1 Prior research work 7.3.2 A Machine learningbased network reconfiguration methodology 7.3.3 Network reconfiguration results 7.4 Advanced artificial intelligence and machine learning applications to building occupancy detection 7.4.1 Prior research work 7.4.2 The convolutional neural networklong short-term memory deep learning architecture 7.4.3 Experiments 7.4.4 Results 7.5 Conclusion References

209 212 212 216 222 228 229 231 234 235 236 238 243 245 251 252

7.1 Introduction Artificial intelligence (AI), the simulation of human intelligence by machines, has brought technological revolutions to the industry and has become part of our life. AI has surpassed human in many fields, including visual recognition, language processing, reading, and playing video games, New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00007-9

© 2021 Elsevier Inc. All rights reserved.

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and is assisting in autonomous driving, robotic surgery, and legal judgment. In the energy sector, AI is also making profound impacts. With the deployment of smart grid technologies, power systems have benefited from AI techniques, as shown in Fig. 7.1. The power system data has characteristics of high-volume, high-velocity, and high-variety. For example, sensors such as phasor measurement units (PMUs) take measurements at a millisecond resolution. The advanced metering infrastructure (AMI) in the New York state collects more than 127 TB of consumption data per day [1]. With the big data in power systems, AI provides new solutions to system planning, operation, maintenance, market monitoring, and risk management [2]. For example, reinforcement learning (RL) has been used for power system stability control [3], automatic generation control (AGC) [4], and optimal power flow control [5]. Deep learning has been applied to energy resource assessment [6], injection attack detection [7], and fault diagnosis [8]. A more comprehensive review on AI applications to power systems can be found in Ref. [9]. Among various AI applications in power systems, forecasting is one of the most popular use cases. Forecasting in power systems is to predict the future load, renewable generation output, or electricity and energy price, which are used to assist power system operations at different timescales [10,11]. Therefore forecasting has been widely studied and adopted in power systems. For example, most of the independent system operators (ISO) in the United States have adopted load, wind, and solar power forecasting to assist their system operations [12]. ISO New England utilizes day-ahead forecasts for the dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators [13]. A number of forecasting projects have been or is being conducted to promote renewable energy forecasting, such as Wind Forecast Improvement Project [14], WindView [15], Watt-Sun [16], and SUMMER-GO [17]. Another emerging topic in power systems that relies on AI techniques is system state estimation. Power system state estimation is to retrieve system dynamics, for example, voltage magnitude and phase angles, from available measurements. Traditional methods have challenges in solving state estimation problems with large scale and nonconvexity [18]. Therefore AI techniques have been introduced to help solve state estimation problems through a learning-based optimization manner [19]. For example, a feedforward neural network was used to initialize the GaussNewton algorithm to solve the distribution system state estimation in Ref. [20]. An autoencoder was adopted to estimate the voltage magnitude and angle in Ref. [21].

Transmission and distribution systems

Generation

Conventional generator Wind farm Solar farm Distributed storage Utility data center

Forecasting Renewable energy forecasting Behind-the-meter forecasting Load forecasting Price forecasting State estimation Fault diagnosis

Transformer

Industrial consumer

Transmission and distribution line Utility-level storage Distributed solar Energy management system

Commercial consumer Residential consumer Electrical vehicle Mobile device

Control Security control Stability control Automatic generation control Voltage control Reactive power control Optimal power flow control

Figure 7.1 Artificial intelligence applications to smart grid.

Demand

Optimization Ancillary service Unit commitment and economic dispatch Renewable resource assessment Asset management Network reconfiguration Microgrid optimal manageent

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In addition, a recurrent neural network (RNN) was trained to estimate the states of the IEEE 57- and 118-bus systems [18]. Bad data detection and cyberattack detection are also recognized to impact the system states and, thus, have also been widely researched. For instance, Hink et al. [22] successfully detected cyberattacks with several different machine learning methods. A support vector machine (SVM) model was used to detect the stealthy false data injection with nearly 95% accuracy [23]. AI has also been applied to building occupancy detection, which is critical to building energy management and building-to-grid integration. The building sector accounts for over 70% of the total electricity consumption in the United States, making the building integration a critical part of the smart grid. The building occupancy information helps building management in several ways, such as occupancy-driven demand response and building energy management. For example, an occupancy-based feedback control algorithm was applied to a heating, ventilation, and airconditioning system and achieved 29%80% energy savings [24]. Korkas et al. [25] developed a control optimization method for demand response management in microgrids considering occupancy information and reduced energy costs by 20%. Due to the remarkable benefits of the occupancy information, accurate occupancy detection is recognized as a crucial factor and has received growing attention [26,27]. To demonstrate the AI applications to power systems, this chapter reviews the state-of-the-art machine learning methods, including ensemble learning and deep learning, in renewable energy forecasting, power system network reconfiguration, and smart building occupancy detection.

7.2 Modern forecasting technology This section reviews the state-of-the-art forecasting methods and discusses the details of two types of ensemble learning-based forecasting techniques.

7.2.1 Prior research work Time series forecasting can be categorized based on different criteria, such as forecasting task (e.g., load, wind, solar, and price), time horizon (e.g., short-term, midterm, and long-term), or methods. Fig. 7.2 summarizes a

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rough classification of forecasting methods, which are mainly divided into deterministic forecasting and probabilistic forecasting. The former can obtain accurate forecasting results after a specific time horizon, while the latter can provide probabilistic and confidence levels for the uncertainty of desired forecasts. 7.2.1.1 Deterministic forecasting methods Generally, deterministic forecasting models can be further divided into three categories: statistical [28], intelligent [29], and ensemble models [30]. Statistical methods refer to the utilization of mathematical theory knowledge such as mathematical statistics, probability theory, and stochastic processes. Intelligent models refer to AI models using machine learning and deep learning. Ensemble models refer to the combination of two different algorithms or methods. Ensemble models can combine the merits and characteristics of different methods, which normally perform better than single models [31]. Conventional statistical models include the autoregressive model, the autoregressive moving average model, and the autoregressive integrated moving average model. The most popular machine learning algorithms in renewable energy forecasting are artificial neural networks (ANNs), support vector regression (SVR) model, random forest (RF), and gradient boosting machine (GBM). For example, Feng et al. [32] proposed a short-term solar forecasting method based on sky imaging and pattern recognition. A least squares SVR model was proposed in Ref. [33] to model the nonlinearity of electric load. A GBM model was developed to quantify the dataset diversity for short-term wind forecasting [34], which helps system operational scheduling such as economic dispatch and

Figure 7.2 Machine learningbased forecasting methods categorization.

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unit commitment. Xia et al. [35] proposed a short-term wind power forecasting method based on neuro-fuzzy network. Results showed that the trained neuro-fuzzy networks could significantly improve the wind forecasting accuracy. More deterministic forecasting models can be found in the latest review papers [3638]. 7.2.1.2 Probabilistic forecasting methods While deterministic forecasts are critical to power system operations, probabilistic forecasts provide more quantitative uncertainty information associated with desired forecasts and have become extremely important for reliable and economic power system operations. Probabilistic forecasts usually take the form of probability distributions associated with point forecasts, namely, the expectation. Existing methods of constructing predictive distributions can be mainly classified into parametric and nonparametric approaches in terms of distribution shape assumptions. A prior assumption of the predictive distribution shape is made in parametric methods, and the unknown distribution parameters are estimated based on historical data. Parametric approaches generally require low computational cost, while nonparametric approaches estimate the quantiles through a finite number of observations. Landry et al. [39] used GBM for multiple quantile regression to fit each quantile and zone independently and generate probabilistic forecasts. Sun et al. [40] proposed an aggregated probabilistic wind power forecasting method based on spatiotemporal correlation, where Copula was used to model the correlation among wind farms and Gaussian mixture model was used to model the marginal distribution. Zhang and Wang [41] developed a probabilistic forecasting method based on k-nearest neighbor (kNN) point forecasts through kernel density estimation. Lou et al. [42] used machine learning and multivariable regression to predict diffuse solar irradiance. Wang et al. [43] used deep convolutional neural network (CNN) and wavelet transform to quantify the wind power uncertainties with respect to model misspecification and data noise. Sun et al. [44] developed a probabilistic forecasting method based on pinball loss optimization among different types of predictive distributions. 7.2.1.3 Ensemble learning The ensemble of individual machine learning models is another efficient way to improve the forecasting accuracy. Methods of constructing

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ensemble forecasts can be mainly classified into competitive ensemble and cooperative ensemble methods. Competitive ensemble methods use induction algorithms with different parameters or initial conditions to build individual models. The final refined ensemble prediction is obtained from pruning and aggregating individual forecasts. Competitive ensemble methods generally require high data and parameter diversity to get different decisions from individual predictors. Therefore competitive ensemble methods usually require high computation cost, and they are usually used in mid-term to long-term forecasting. Bagging and boosting are two commonly used competitive ensemble methods. For example, to better account for the performance of weak models, ensemble forecasting approaches based on adaptive boosting (i.e., assign smaller weights to the models with larger errors) are used in Ref. [45]. Feng et al. [11,46] proposed a machine learningbased multimodel forecasting framework that consists of an ensemble of four singlemachine learning algorithms with various kernels to generate deterministic wind forecasts and solar forecasts. Machine learningbased competitive ensemble learning was also used in solar forecasting [47] and load forecasting [48]. For cooperative ensemble methods, the dataset is divided into several subdatasets and each subdataset is forecast separately, and the final forecasts are obtained by aggregating all the subforecasts. ANN-based autoregressive integrated moving average [49] and generalized autoregressive conditional heteroskedasticity-based autoregressive integrated moving average [50] are the two commonly used cooperative ensemble methods that combine suitable models for linear and nonlinear time series. Ye et al. [51] developed an AdaBoost-based empirical mode decomposition (EMD) ANN to generate ensemble wind speed forecasts. Wang et al. [52] developed a cooperative ensemble method to generate wind speed forecasts, which improved EMD through decomposing the original data into more stationary signals with different frequencies. These stationary signals were considered as different inputs to a genetic algorithm and backpropagationbased neural network, and the final forecast was the aggregation of each single prediction. In addition to wind speed forecasting, ensemble methods have also been applied to probabilistic wind power forecasting and probabilistic solar power forecasting. For example, Lin et al. [53] combined multiple probabilistic forecasting models based on sparse Bayesian learning, kernel density estimation, and beta distribution estimation. The weight

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parameters of the multimodel ensemble were solved by an expectation maximizing algorithm and continuous ranked probability score optimization. Kim and Hur [54] developed an enhanced ensemble method for probabilistic wind power forecasting. The wind speed spatial ensemble was built by using correlation-based weight and kriging models, and the temporal ensemble was built through an average ensemble of three models (i.e., an exogenous variable model, a polynominal regression model, and an analog ensemble model).

7.2.2 Ensemble learning forecasting methodologies Both competitive and cooperative ensemble learning rely on single models. Therefore single-machine learning algorithms, including ANN, SVR, GBM, and RF, are first introduced in this section. Then, two wind forecasting methodologies based on competitive or cooperative ensemble learning are described. 7.2.2.1 Single-machine learning algorithm models ANN is a popular machine learning algorithm in speech recognition, target tracking, signal analysis, and nonlinear regression problems (such as time series forecasting). ANN mimics the action of the human neurons, and each neuron is a weighted sum of its inputs and is connected to the neurons in the next layer. A weight decides how much influence the input will have on the output. The ANN architecture contains one input layer, one or more hidden layer(s), and one output layer. The output signal of ANN can be either 0, 1, or any real value between 0 and 1 depending on whether we are dealing with “binary” or “real valued” artificial neurons. The configuration of the ANN model needs to be well designed to avoid overfitting issues. ANN can be classified into different types with different activation functions and learning algorithms. Sigmoid and hyperbolic tangent are two commonly used activation functions. Deep learning is also a configuration of ANN, where multiple hidden layers are built. The mathematical description of ANN is expressed as: ! N X ðnÞ ðn;n21Þ ðn21Þ n yi 5 f (7.1) wij yj 1 θi j51

where i is a neuron of the nth layer, wij is the weight from the neuron j in the layer ðn 2 1Þ to the neuron i in layer n, and θni is the threshold of the neuron i in layer n.

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SVM is originally a supervised linear classifier proposed by Cortes and Vapnik [55]. As one of the most popular classification methods, SVM has been applied in text categorization, image classification, and other recognition tasks. When dealing with linearly inseparable data, nonlinear mapping-based kernel methods, KðxÞ:Rn !Rnh , are used to map the nonlinear data into the high-dimensional feature space. Then, a linear hyper plane can be found by maximizing the distance between support vectors and the hyper plane. The SVM algorithm can also be applied in regression problems, which is called SVR. However, the compute and storage requirements increase significantly with the data dimension. The hyper plane function, also called the SVR function, is described as [56]: f ðxÞ 5 ωT KðxÞ 1 b

(7.2)

where ω and b are variables solved by minimizing the empirical risk, which is given by: R ðf Þ 5

n 1X Θðyi ; f ðxÞÞ n i51

where Θε ðyi ; f Þ is the ε-insensitive loss function, expressed as: ( :f 2 y: 2 ε; if :f 2 y: $ ε Θε ðyi ; f Þ 5 0; otherwise

(7.3)

(7.4)

Then the optimal hyper plane can be found by solving the inequalityconstrained quadratic optimization problem. GBM is a highly customizable learning algorithm, which is widely used in the regression and classification fields. GBM model relies on the combination of “weak learners” to create an accurate learner and, therefore, is able to generate both deterministic and probabilistic results in time series forecasting. The combination is achieved by adding the weighted base learner to the previous model iteratively. The principle of GBM is illustrated by the pseudo-code in Algorithm 1. In each iteration the negative gradient of the chosen loss function is calculated and used to estimate the split variables a by Eqs. (7.5) and (7.6). Then the multiplier β is optimized by Eq. (7.7). The weak learner βhðx; aÞ is added to the previous model, where hðx;aÞ is a learning function. RF is another supervised ensemble learning method that consists of many single classification and regression trees (CARTs):

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Algorithm 1 Gradient boosting machine 1 2 3

P Initialize f0 ðxÞ to be a constant, f0 ðxÞ 5 argminρ ni51 Ψðyi ; ρÞ for i 5 1 to M do Compute the negative gradient of the loss function:

yi 5 2 4

@Ψ ðyi ; F ðxi ÞÞ 5 fi21 ðxÞ; @F ðxi Þ f ðxÞ

α;β

Calculate β t by:

β t 5 arg min β

6

n X

½yi 2βhðxi ; aÞ2

7

(7.6)

i51

n X Ψ yi ; ft21 ðxi Þ 1 βhðxi ; at

(7.7)

i51

Update the model by:

ft ðxÞ 5 ft21 ðxÞ 1 β t hðx;at 8

(7.5)

Fit a model to y by least squares to get at :

at 5 arg min 5

i 5 f1; 2; . . . ; ng

(7.8)

end for Output f^ ðxÞ 5 fT ðx

T 5 t X; sΛ1 ; t X; sΛ2 ; . . . ; t X; sΛn

(7.9)

where T is the set of CARTs, t is a single CART, X is the input to the RF model, and sΛi is the random vector to extract bootstrap samples that are determined by the bagging algorithm. The robustness of the RF model is enhanced by the randomness of the bagging algorithm and the best splits search process. Since RF is a combination of various regressions, the model is generally free from overfitting [57]. 7.2.2.2 Competitive ensemble learning A competitive ensemble methodology is developed for short-term wind forecasting with both deterministic and probabilistic forecasts, which is called the machine learningbased multi-model forecasting framework (M3), as shown in Fig. 7.3. The M3 deterministic model has two layers (note that this is different from ANN layers). The first layer consists of several machine learning models, that is, ANN, SVM, GBM, and RF, which are built based on the historical data. These models forecast wind

Figure 7.3 Competitive ensemble learning framework based on the M3 model.

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speed or wind power as the output. A blending model is developed in the second layer to combine the forecasts produced by different algorithms from the first layer, and to generate both deterministic and probabilistic forecasts. This blending model is expected to take advantage of different algorithms by canceling or smoothing the local forecasting errors. The mathematical description is shown as: yi 5 fi ðx1 ; x2 ; . . . ; xp Þ

(7.10)

y^ 5 Φðy1 ; y2 ; . . . ; ym Þ

(7.11)

where fi ð Þ is the ith algorithm and yi is the wind speed forecasted by fi ð Þ. y^ is the second-layer blending algorithm. The M3 deterministic forecasts can also be transferred to probabilistic forecasts. Specifically, after obtaining deterministic forecasts, a set of unknown parameters in the predictive distribution are determined by minimizing the pinball loss that is an evaluation metric of probabilistic forecasts. Note that the optimal distribution parameters are adaptively and dynamically updated based on the deterministic forecast value at each time stamp. The optimal adaptive predictive distribution parameters are first determined offline with the historical training data. Then a surrogate model is developed to represent the optimized distribution parameter as a function of the deterministic forecast. At the real-time forecasting stage the surrogate model is used together with deterministic forecasts to adaptively predict the unknown distribution parameters and thereby generate probabilistic forecasts. 7.2.2.3 Cooperative ensemble learning In addition to deterministic forecasts, the ensemble methods could also be applied to probabilistic forecasts directly. A cooperative ensemble methodology of probabilistic wind power forecasts from different type of predictive distributions is introduced in this section, and the overall framework is illustrated in Fig. 7.4. In the deterministic forecasting stage, instead of integrating multiple single-machine learning algorithms together to get a refined forecast, a Qlearning-enhanced deterministic forecasting method [58] is adopted to select the best forecasting model from a pool of state-of-the-art machine learningbased forecasting models (i.e., ANN, SVR, GBM, and RF) at each time step. To be more specific, the developed method trains Qlearning agents based on the rewards of transferring from the current model to the next model. For example, a Q-learning agent will receive a

Mult i-Distribut ion Ensemble (MDE)

Determinist ic forecasts Historical data

Gaussian cumulat ive distribut ion

Neural network Support vector machine

Q-learning

Gamma cumulat ive distribut ion

Pinball loss optimization

Gradient boosting machine

Random forest

Laplace cumulat ive distribut ion

Determinist ic forecasts

Probabilist ic forecasts Member quant ile forecasts Pinball loss analysis

Gaussian quant iles

Gamma quant iles

Pseudo opt imal parameters

Support vector regression

Persistence method Ensemble quant iles

Reliability and sharpness analysis

Surrogate model select ion

Laplace quant iles

Probabilistic forecasts

Optimal parameter

Pseudo optimal weights

Model of least mean absolute error

Figure 7.4 The overall framework of the cooperative ensemble probabilistic forecasting.

Radial basis function

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reward by transferring from the current forecasting model Mi to the next forecasting model Mj in each training step, from which the Q-learning agent will learn the optimal policy of the model selection. Then, this optimal policy will be applied to select the best model for forecasting in the next step based on the current model in the forecasting stage. The dynamic model selection process is expressed as: S 5 fsg 5 fs1 ; s2 ; . . . ; sI g

(7.12)

A 5 fag 5 a1 ; a2 ; . . . ; aI (7.13) R si ; aj 5 ranking ðMi Þ 2 rankingðMj Þ (7.14) h i (7.15) Qe11 ðse ; ae Þ 5 ð1 2 αÞQe ðse ; ae Þ 1 α Re ðse ; ae Þ 1 γmaxQe se11 ; a t

where S, A, R, and Q are state space, action space, reward space, and Qtable in the dynamic model selection Markov Decision Process, respectively. The parameters s and a are possible state and action, respectively. The parameter I is the number of models (M ) in the model pool, e is the episode index with the maximum of 100, α 5 0:1 is the learning rate that controls the aggressiveness of learning, and γ 5 0:8 is a discount factor that weights the future reward. The reward function is defined as the model performance improvement, which ensures the effective and efficient convergence of Q-learning. More details of this Q-learning-based dynamic forecasting model selection can be found in Ref. [58]. In the probabilistic forecasting stage, individual probabilistic forecasts are generated by using each single predictive distribution based on the training dataset [44]. The unknown parameters (i.e., standard deviations) of each predictive distribution are optimized. A weight parameter is assigned to quantile forecasts from each individual model, and these weight parameters are optimized again by minimizing the pinball loss. Then a surrogate model is developed to represent each optimal weight as a function of the deterministic forecast. During online forecasting a set of pseudo-optimal parameters of the ensemble model is estimated by the surrogate model and deterministic forecasts. Finally, the method with the minimum pinball loss is chosen to produce the final ensemble probabilistic forecasts.

7.2.3 Forecasting results Performance of the single-algorithm and ensemble machine learning models is evaluated in this section for both deterministic and probabilistic

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wind speed/power forecasting. The ensemble deterministic and multimodel probabilistic forecasting models are tested on wind speed forecasting. The Q-learningenhanced deterministic and ensemble probabilistic forecasting models are tested on wind power forecasting. Two evaluation metrics are utilized to evaluate the deterministic forecasting accuracy in both case studies: the normalized mean absolute error (nMAE) and the normalized root mean square error (nRMSE): 1 Xn x^ i 2 xi nMAE 5 j j (7.16) i51 n xmax sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ Pn ^ i 2xi Þ2 1 i51 ðx (7.17) nRMSE 5 xmax n where x^ i is the forecast value, xi is the actual value, xmax is the maximum actual value, and n is the sample size. Pinball loss, one of the most popular metrics, is used to evaluate the performance of probabilistic forecasting in both case studies. Pinball loss is a function of observations and quantiles of a forecast distribution. A smaller pinball loss value indicates better probabilistic forecasting. The pinball loss value of a certain quantile Lm is expressed as: 8 ! > m > > > < 1 2 100 3 ðqm 2 xi Þ; &xi , qm Lm ðqm ; xi Þ 5 (7.18) > m > > 3 ðxi 2 qm Þ; &xi $ qm > : 100 where xi represents the ith observation, m represents a quantile percentage from 1 to 99, and qm represents the predicted quantile. For a given m percentage the quantile qm represents the value of a random variable whose CDF is m percentage. 7.2.3.1 Case study I: wind speed forecasting based on competitive ensemble learning Both the single-algorithm models and ensemble algorithm are applied to the wind speed data collected near hub height with a 1-hour resolution at eight sites listed in Table 7.1. For all the eight locations the first 2/3 of data are used as training data, in which the first 11/12 is used to train ensemble algorithm and the remaining 1/12 of the training data is used to build the surrogate model of the optimal standard deviation. The accuracy

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of the forecasts is evaluated by the remaining 1/3 of data. The nMAE and nRMSE are compared in Tables 7.2 and 7.3, respectively. The multi-model framework includes multiple individual models in the first layer and several models in the second layer. Different algorithms are tested in both layers, which include (1) three SVR models with linear (SVR_l), polynomial (SVR_p), and radial base (SVR_r) kernels; (2) five ANN models with different numbers of hidden layers (nl ), neurons in each layer (no ), and weight decay parameter (nd ) values, and the selected models employ the feed-forwardbackpropagation learning function and Table 7.1 Data duration at selected sites. Name Data duration

Boulder_NWTC (C1) Megler (C2) CedarCreek_H06 (C3) Goodnoe_Hills (C4) Bovina50 (C5) Bovina100 (C6) CapeMay (C7) Cochran (C8)

2009-01-02 2010-11-03 2009-01-02 2007-01-01 2010-10-10 2010-03-03 2007-09-26 2008-06-30

Height (m)

to to to to to to to to

2012-12-31 2012-11-01 2012-12-31 2009-12-31 2012-10-08 2012-03-01 2009-09-24 2011-06-29

80 53.3 69 59.4 50 100 100 70

Note: The case notations (C1C8) are different from Sections 7.2.3.2 and 7.4.3.

Table 7.2 The normalized mean absolute error (nMAE) (%) of 1HA forecasts. Method C1 C2 C3 C4 C5 C6 C7

C8

SVR_r SVR_l SVR_p ANN1 ANN2 ANN3 ANN4 ANN5 GBM1 GBM2 GBM3 GBM4 RF1 RF2 M3

4.014 3.984 4.009 4.007 4.012 4.017 4.005 4.006 4.022 4.020 4.002 4.023 4.159 4.110 3.871

5.101 4.765 4.772 4.793 4.789 4.817 4.792 4.793 4.822 4.808 4.806 4.845 4.965 4.920 4.623

3.114 2.886 2.919 2.921 2.938 2.927 2.906 2.902 2.945 2.941 2.936 2.946 3.060 3.012 2.712

6.229 3.927 4.267 4.155 4.042 4.096 4.022 3.859 4.468 4.474 4.730 4.348 4.207 4.221 3.731

3.799 3.718 3.734 3.738 3.735 3.738 3.735 3.727 3.739 3.736 3.768 3.754 3.883 3.852 3.654

5.145 4.891 4.913 4.936 4.939 4.932 4.924 4.899 4.961 4.963 4.969 4.974 5.115 5.057 4.683

Note: The smallest nMAEs among all the models are in bold.

4.554 4.466 4.572 4.671 4.536 4.494 4.481 4.487 4.479 4.478 4.491 4.544 4.703 4.637 4.256

3.662 3.449 3.553 3.717 3.560 3.502 3.500 3.480 3.562 3.550 3.504 3.554 3.715 3.659 3.223

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Table 7.3 The normalized root mean square error (nRMSE) (%) of 1HA forecasts. Method C1 C2 C3 C4 C5 C6 C7 C8

SVR_r SVR_l SVR_p ANN1 ANN2 ANN3 ANN4 ANN5 GBM1 GBM2 GBM3 GBM4 RF1 RF2 M3

8.039 6.954 6.953 6.907 6.908 6.902 6.930 6.919 6.969 7.016 7.059 7.011 7.220 7.141 6.720

4.883 3.993 4.011 4.019 4.038 4.026 4.024 4.005 4.105 4.102 4.121 4.116 4.238 4.159 3.988

11.769 5.638 6.381 6.250 5.974 6.116 5.913 5.483 7.197 7.225 7.886 6.898 6.248 6.416 5.524

5.318 5.095 5.109 5.123 5.105 5.123 5.124 5.096 5.100 5.103 5.217 5.112 5.261 5.224 4.987

7.166 6.539 6.545 6.585 6.589 6.579 6.564 6.529 6.605 6.613 6.654 6.620 6.788 6.715 6.341

6.363 6.224 6.350 6.364 6.260 6.223 6.225 6.207 6.225 6.237 6.258 6.291 6.463 6.388 6.089

5.383 4.813 4.946 5.065 4.930 4.868 4.888 4.826 4.942 4.936 4.916 4.955 5.154 5.067 4.760

5.477 5.401 5.423 5.409 5.408 5.402 5.411 5.402 5.413 5.419 5.421 5.426 5.585 5.526 5.101

Note: The smallest nRMSEs among all the models are in bold.

sigmoid activation function; (3) four GBM models based on different loss functions (Gaussian and Laplacian) and parameters, that is, number of trees, learning rate (λ), maximum depth of variable interactions, and minimum number of observations in the terminal nodes; and (4) two RF models with different numbers of variables that are randomly sampled as candidates at each split. In the probabilistic forecasting stage, four widely used predictive distribution types (i.e., Gaussian, Gamma, Laplace, and noncentral t) are considered to model the possible shapes of the predictive distribution. The one with the lowest pinball loss is chosen as the final predictive distribution. It is seen that none of the single models performs better than the ensemble method. The ensemble models have improved the forecasting accuracy of the component models by up to 12.9% based on nMAE and 16.4% based on nRMSE. For the blending algorithms the models with nonlinear blending algorithms have better performance than the models with linear blending algorithms. This shows that the forecasts produced from the first-layer models exhibit a nonlinear relationship with the actual wind speed. Fig. 7.5 provides an example of the deterministic forecasts along with the confidence intervals in the form of interval plot at the C2 site from 2012-02-01 to 2012-02-04. The colors of the intervals fade with the increasing confidence level, ranging from 10% to 90% in a 10% increment. The intervals are symmetric around the deterministic forecasting curves with a changing width. When the wind speed fluctuates within a

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small range, the confidence bands are narrow. When there is a significant ramp, the uncertainty of the forecasts is increased and the bands tend to be broader, as shown by hours 5065. This further proves the necessity of probabilistic forecasting. The pinball loss values of the eight selected sites with different predictive distributions are summarized in Table 7.4. The sum of pinball loss is averaged over all quantiles from 1% to 99% and normalized by the maximum wind speed at each site. A lower pinball loss score indicates a better probabilistic forecast. Table 7.4 shows that the M3-Laplace with pinball loss optimization has the smallest pinball loss value at all locations. The M3-Laplace model has reduced the pinball loss by up to 35% compared to the M3 forecasts with other predictive distributions [i.e., Gaussian, Gamma, and Noncentral T (nCT)]. Therefore the Laplace distribution is finally chosen to generate probabilistic wind speed forecasts. Note that the models of M3-Gaussian, M3-Gamma, and M3-Laplace perform similarly,

Figure 7.5 Multi-model probabilistic forecasts at the C2 site. Table 7.4 Normalized averaged sum of pinball loss. Method C1 C2 C3 C4 C5

C6

C7

C8

M3-Gaussian M3-Gamma M3-Laplace M3-nCT

1.69 1.69 1.63 3.41

1.28 1.27 1.26 2.56

1.59 1.58 1.57 2.86

1.74 1.74 1.72 1.74

1.26 1.26 1.25 1.81

1.44 1.44 1.43 2.20

1.36 1.36 1.35 2.21

1.86 1.87 1.85 2.68

Note: The smallest normalized averaged sum of pinball loss at each location is in boldface.

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which indicates that the optimization can help achieve better accuracies with different predictive distribution types. 7.2.3.2 Case study II: wind power forecasting based on cooperative ensemble learning The Q-learning enhanced deterministic forecasting algorithm is applied to the wind power data collected from the WIND Toolkit with 1-hour resolution, which includes seven sites (as shown in Table 7.5) with meteorological information (e.g., wind direction, wind speed, air temperature, surface air pressure, and density at hub height). For all the locations the first 3/4 of the data is used as training data, in which the first 11/12 is used to train the deterministic forecast models and the remaining 1/12 of the training data is used to build the surrogate models of the optimal standard deviations and weight parameters. The effectiveness of the forecasts is validated by the remaining 1/4 of the data. The nMAE and nRMSE of 1HA (hour-ahead) wind power forecasting are listed in Table 7.6. It is shown that the 1HA nMAE and nRMSE from Q-learning model are in the ranges of 5%8% and 8%13%, respectively. To show the effectiveness of the Q-learning enhanced deterministic forecasting, the persistence method is used as a baseline. It is seen from Table 7.6 that overall the Q-learning performs better than the persistence method. Table 7.5 Data summary of the selected seven WIND Toolkit sites. Site ID (case notation) Latitude Longitude Capacity (MW)

4816 (C1) 8979 (C2) 10069 (C3) 10526 (C4) 1342 (C5) 2061 (C6) 9572 (C7)

2 100.37 2 95.62 2 98.26 2 100.55 2 97.86 2 97.40 2 100.18

29.38 31.53 32.31 32.44 27.12 27.95 31.99

80 53.3 69 59.4 50 100 100

State

TX TX TX TX TX TX TX

Note: The case notations (C1C7) are different from Sections 7.2.3.1 and 7.4.3.

Table 7.6 The normalized mean absolute error (nMAE) (%) and the normalized root mean square error (nRMSE) [%] of 1HA forecasts. Model Metric C1 C2 C3 C4 C5 C6 C7

Q-learning Persistence

nMAE nRMSE nMAE nRMSE

6.63 10.55 7.14 11.38

6.85 10.93 7.18 11.71

6.70 11.04 6.97 11.82

6.74 10.66 7.11 11.50

6.67 9.93 7.12 10.99

5.38 8.37 5.74 8.93

7.76 11.93 8.06 12.63

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Figure 7.6 Cooperative ensemble probabilistic forecasts at the C2 site. Table 7.7 Normalized averaged sum of pinball loss. Method C1 C2 C3

C4

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Q-learning-cooperative Q-learning without ensemble

3.01 3.38

3.05 3.42

1.98 2.30

3.67 3.94

2.23 2.46

2.58 2.83

2.42 2.67

To better visualize the ensemble forecasting model, Fig. 7.6 provides an example of the deterministic forecasts along with the confidence intervals in the form of interval plot at the C2 site from 2012-07-20 to 2012-07-24. The colors of the intervals fade with the increasing confidence level, ranging from 10% to 90% in a 10% increment. The intervals are symmetric around the deterministic forecasting curves with a changing width. Fig. 7.6 shows that the prediction intervals of the ensemble forecasting model are narrow, which show less uncertainty in the probabilistic forecasts. The pinball loss values of the seven selected sites with different predictive distributions are summarized in Table 7.7. Results show that the Q-learning-based ensemble probabilistic forecasting method has reduced the pinball loss by up to 16.1% compared with single probabilistic forecasting methods.

7.3 Machine learningbased control and optimization The powerful learning abilities also enable machine learning models to solve complex control and optimization problems in power systems.

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This section reviews the popular applications of machine learning in power system control and optimization. Specifically, a network reconfiguration optimization problem is solved by AI to demonstrate the “learn to optimize” capability of machine learning models.

7.3.1 Prior research work 7.3.1.1 Machine learningbased control Due to the increasing complexity and uncertainty brought by renewable energy, distributed energy resources, and varying loads, it is of significant importance for the electric power system to employ advanced control strategies to keep the system working reliably and efficiently. Studies have shown that machine learningbased control strategies are capable of addressing the high-dimensional complex nonlinear control challenges, and it could be more effective when properly combined together with conventional mathematical approaches [59,60]. In terms of power system stability, extensive studies have employed neural network to design power system stabilizer (PSS), and controller for AGC or frequency control [61]. For instance, a self-tuning PSS based on ANN was proposed by Segal et al. [62]. In this approach, ANN was introduced for tuning the conventional PSS parameters in real time, showing that the dynamic performance of the system with the ANNbased PSS is robust over a wide range of loading conditions and equivalent reactance. To improve the transient stability of power systems under different operating conditions and parametric uncertainties, Senjyu and Fujita [63] proposed an RNN stabilization controller for both automatic voltage regulators and the governor, where the weights of the controller are adjusted online. The proposed approach was applied to a singlemachine infinite-bus system and showed good damping characteristics over a wide range of operating conditions. However, due to the complexity of large-scale power systems, design of intelligent controllersbased ANN requires large training time and a large number of neurons. These drawbacks motivated Chaturvedi et al. [64] to utilize generalized neurons (GNs) to develop a GN-based adaptive PSS. Given the advantages of self-optimizing adaptive control and the quick response of the GN, the proposed GN-based PSS can provide good damping of the power system over a wide operating range and significantly improved the dynamic performance. To stabilize the system after severe disturbances and mitigate the oscillations afterward, Hadidi and Jeyasurya [65] employed a Q-learning RL algorithm to develop a real-time closed-loop

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wide-area decentralized PSSs. The proposed algorithm proved that the stability boundary of the system could be extended without losing any generator or load area. Due to the deviation between system load and power generation, there exist frequency fluctuations in the power system, which requires a supplementary control strategy to restore the system frequency to the nominal values. For example, Zeynelgil et al. [66] proposed an ANN controller with the backpropagation through time algorithm to study AGC in a four-area interconnected power system. Chaturvedi et al. [67] proposed a generalized NN-based decentralized controller to control the frequency deviations in power systems. Other modified NN algorithms include dynamic NN [68] and radial base function neural network [69]. Moreover, RL algorithms, that is, model-free Q-learning [70], hierarchically correlated equilibrium Q-learning [71], and deep distributed recurrent Q-networks-action discovery [72], have also been employed to develop frequency controllers for different conditions and purposes. 7.3.1.2 Machine learningbased optimization For a data-driven nonlinear optimization problem, the first step in general is to establish a mathematical regression model as the objective function in terms of the design variables based on the experimental or collected data. Since the physical or explicit relationships are unable to be observed directly or remain unknown, the modeling process is referred to as blackbox. Machine learning (i.e., surrogate prediction model) approaches are widely employed to solve the black-box problem, such as ANN, kriging/ Gaussian process (GP) regression, SVM, radial basis functions, and polynomial response surface. By cross-validation the best appropriate models are selected to build up the black-box model. The next step of the optimization process is to apply a gradient-based or heuristic algorithm to solve the problem. The machine learningbased optimization has also been extensively employed in power system studies. For example, Lucifredi et al. [73] compared the kriging and ANN statistical modeling techniques with a conventional linear multiple regression method for hydroelectric power system maintenance prediction. Pan and Das [74] proposed to use a fractional order control strategy for a microgrid, in which the controller parameters were tuned with a global optimization algorithm to meet system performance specifications. In this research, a kriging-based surrogate modeling technique integrated with GA was employed to alleviate the

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issue of expensive objective function evaluation for the optimization-based controller tuning. Luh et al. [75] utilized augmented Lagrangian relaxation to form and solve the market clearing prices subproblems by using a surrogate-based optimization framework.

7.3.2 A Machine learningbased network reconfiguration methodology 7.3.2.1 Literature review on network reconfiguration Over the past decades, power grids have become more prone to be overloaded due to the disproportionate increasing of the load demand and generation units (both dispatchable and nondispatchable units) [76]. Hence, the reconfiguration technique has been proposed to prevent overloading and provide an efficient power dispatch, especially in the emergency conditions when the line current approaches its maximum ampacity [77]. By definition the reconfiguration is the process of changing the topology of the power network by some prelocated sectionalizing and tie switches that can significantly improve the grid reliability [77], voltage profile [78], line loss [79], and load balance [80]. Up to now, many heuristics and mathematical algorithms have been used to solve the reconfigurable power grids, for example, collective decision-based algorithm [77], genetic algorithm [81], and mixed integer linear programming [77,78]. However, fast and effective reconfiguration response for electric service restoration is one of the critical requirements, which can be achieved by using state-of-the-art AI techniques to solve the reconfigurable power grids. Therefore in this section, a deep learning model is applied to solve the reconfigurable power grids. In this section a new AI technique, known as the deep learning gated recurrent unit (GRU) (DLGRU), is developed to solve the reconfigurable power grids by learning the topological patterns of buses/lines with their physical features. GRU was first designed in Ref. [82] to decrease the complexity of long short-term memory (LSTM) and also enhance the LSTM performance. Same as LSTM, GRU has gates that can control the flow of the information from the input to the output. DLGRU can potentially understand complex nonlinear topological characteristics of the grid. Moreover, as will be shown in the result section, the total operation cost of the power grid by utilizing the DLGRU technique to select optimal switching of the reconfiguration is almost similar to conventional optimization techniques. DLGRU can solve the reconfigurable power grids in real time. However, conventional optimization techniques require

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a certain amount of time interval to obtain the optimal results by running the optimal power flow. More information regarding the GRU method can be obtained in Ref. [82]. As mentioned, DLGRU is proposed to solve the reconfigurable power grid with the following objective function and constraints through a “learn to optimize” manner. Specifically, the main objective is to minimize the total operation cost as: X X X min ½Ci PitG 1 SUit 1 SDit 1 NRCS;kt λRCS (7.19) kAΩS

iAΩDG tAΩT

where Ci is the generation cost of the ith unit, PitG is the generated active power of the ith unit at time t, and SUit and SDit are the start-up and shutdown costs of the ith unit at time t, respectively. Also, NRCS;kt and λRCS represent the number of reconfigurable switching actions of kth remote-control switch (RCS) at time t and reconfiguration switching cost, respectively. Moreover, ΩDG , ΩT , and ΩS denote sets of the generation units, switches, and time, respectively. It should be noted that the first and second terms of Eq. (7.19) represent the generation and reconfiguration switching costs of the grid, respectively. The proposed reconfigurable power grid includes some significant constraints in the following paragraphs. Power balance constraints: the active and reactive power balances of each bus should be constrained as: 2 i X X h L L L Pnm;t 2 Rnm Inm;t Pnm;t 2 1 PitG 5 PtD ’iAΩDG ; ’tAΩT nmAΩL

X nmAΩL

nmAΩL

L QLnm;t 2 Xnm Inm;t

2

(7.20) 2

X h

i DG D QLnm;t 1 QG ; ’tAΩT it 5 Qt ’iAΩ

nmAΩL

(7.21) L where Pnm;t and QLnm;t are active and reactive power flow of line nm at time t, respectively. PtD and QD t are the total active and reactive power demand at time t, respectively. Rnm and Xnm are resistance and reactance L of line nm, respectively. Also, Inm;t represents the current flow of distribution line nm at time t. It should be noted that ΩL denotes as the set of distribution lines. To apply the KVL to the distribution lines, the following constraints should be considered:

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2 L L ðVmt Þ2 2 ðVnt Þ2 5 2 Rnm Pnm;t 1 Xnm QLnm;t 2 ðZnm Þ2 Inm;t 1 ΔVnmt ’nmAΩL ; ’n; mAΩN ; ’tAΩT (7.22) 2 2 2 L L ðVmt Þ2 Inm;t 5 Pnm;t 1 QLnm;t ’nmAΩL ; ’n; mAΩN ; ’tAΩT (7.23) where n and m are defined as the bus indices, and ΩN denotes the set of buses. Here, Vmt represents the voltage of bus m at time t, and ΔVnmt is the auxiliary variable that can be zero if line mn is switched on at time t. Otherwise, it can be positive or negative, which depends on the difference between the voltages of the sending and receiving ends of line nm. Finally, Znm denotes the impedance of line nm. Generation units constraints: the active and reactive powers of generation units are constrained to a minimum and maximum capacity as: PiG Iit # PitG # PiG Iit ’iAΩDG ; ’tAΩT

(7.24)

DG G G QG ; ’tAΩT i Iit # Qit # Qi Iit ’iAΩ

(7.25)

where PiG and PiG present the minimum and maximum active power G capacity of the ith unit, respectively. Similarly, QG i and Qi present the minimum and maximum active power capacity of the ith unit, respectively. It should be noted that Iit is the on/off status of the ith unit at time t, which can be zero (when unit is on) or one (when unit is off). Bus voltage and angle limits: the voltage and angle of each bus are limited as: Vn # Vnt # Vn ’nAΩN ; ’tAΩT

(7.26)

2π # θnt # π’nAΩN ; ’tAΩT

(7.27)

where Vn and Vn represent the minimum and maximum permissible voltage at bus n, respectively. Also, θnt denotes the angle of bus n at time t. Reconfiguration constraints: the number of reconfigurable switching per day is constrained as: NRCS;k;t # NRCS ’kAΩS ; ’tAΩT

(7.28)

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where NRCS denotes the maximum permissible switching per day. Also, the radiality of the network after each reconfiguration process is assured: Nloop 5 Nbranch 2 Nbus 1 1

(7.29)

where Nloop , Nbranch , and Nbus represent the total number of network main loops, the number of branches, and the number of buses, respectively. More details regarding the reconfigurable power grid as well as the conventional methods for solving the problem can be found in Ref. [77]. Fig. 7.7 illustrates the proposed DLGRU to solve the reconfiguration of the power systems. There are inputs (the load and the generation unit powers), output (the switching numbers of the reconfigurations of the power systems), and hidden GRU layers.

7.3.3 Network reconfiguration results In this part, as shown in Fig. 7.8, a modified IEEE 33-bus test system is selected to demonstrate the effectiveness of the proposed DLGRU for finding the optimal switching of the reconfigurable power grids. The model includes five tie switches (that are shown by the dotted lines), as well as 33 sectionalized switches (that are shown as the solid lines). Table 7.8 shows the characteristics of the generation units within the network. It is worth noting that in any time interval, five switches should be open to maintain the radiality of the network. The number of hidden layers in the simulation results is 4, and there are 50 units in each layer. The reconfiguration switching for both DLGRU and a conventional benchmark method [77] for a 24-hour time horizon is compared in

Figure 7.7 DLGRU block diagram. DLGRU, Deep learning gated recurrent unit.

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Figure 7.8 Single line diagram of the modified IEEE 33-bus system. Table 7.8 Generation units features. Generation type PiG (kW)

PiG (kW)

Ci ($/kWh)

SU=SD ($)

WT1 WT2 FC MT1 MT2

25 25 1000 1500 1500

1.073 1.073 0.294 0.457 0.457

0 0 0.95 1.65 1.65

0 0 80 100 100

FC, Fuel cell; MT1, microturbine 1; MT2, microturbine 2; WT1, wind turbine 1; WT2, wind turbine 2.

Fig. 7.9. It is observed that there is a small difference between the optimal result of the proposed DLGRU method and the conventional technique for generating the switching’s of the lines to reconfigure the network. Fig. 7.10 shows the contribution of the generation units for both DLGRU and the conventional technique. The total operation cost of the conventional method and the DLGRU method are $138,017.4 and $138,132.2, respectively. Based on the simulation results, the operation costs of the proposed machine learning technique and the conventional technique are very close. However, the proposed DLGRU technique can find the optimal switching in real time due to its fast convergence speed.

7.4 Advanced artificial intelligence and machine learning applications to building occupancy detection The occupancy detection is beneficial to improve building energy management and provide important information for demand response.

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Switch number

40

30

S1- conventional S1- DLGRU S2- conventional S2- DLGRU S3- conventional S3- DLGRU S4- conventional S4- DLGRU S5- conventional S5- DLGRU

20

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5

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Figure 7.9 Reconfiguration switching of both the conventional and DLGRU techniques. DLGRU, Deep learning gated recurrent unit. Conventional method

500 0

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Time (h)

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2000 1000 0

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Figure 7.10 Generation units’ power. Left-hand side: conventional method; righthand side: DLGRU method. DLGRU, Deep learning gated recurrent unit.

This section will first review the state-of-the-art of AI-based occupancy detection. Then, a novel deep learningbased method is developed and validated by publicly available dataset.

7.4.1 Prior research work Based on the monitored objects, occupancy detection can be grouped into intrusive and nonintrusive approaches. Intrusive sensors directly measure indoor environments, including motional, acoustic, or climatic parameters [83]. For example, Candanedo and Feldheim [84] compared occupancy detection methods with different indoor climate feature combinations, among which the best model had over 99% accuracy.

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Surveillance videos were used for occupancy detection with 95.3% accuracy in Ref. [85]. However, the intrusive occupancy detection is challenging to extensively deploy due to the high installation cost, additional operation requirements (e.g., the illumination condition for cameras), and privacy concerns. Therefore it motivated the development of nonintrusive occupancy detection. The nonintrusive occupancy detection relies on infrastructure sensors that monitor parameters such as Wi-Fi, Bluetooth, or radio-frequency identification (RFID) [8688]. For example, Wang et al. [89] developed a Wi-Fibased occupancy detection system with 72.7% accuracy, which helped save 26.4% of energy in cooling and ventilation demands. An RFID-based occupancy detection system was able to track the stationary and mobile occupants with 88% and 62% accuracy, respectively [87]. The nonintrusive methods have fewer privacy issues; however, they still suffer from possibly unsatisfactory accuracies, low infrastructure/device coverage, and extra occupant participation. The limitations of both intrusive and nonintrusive sensors provide an opportunity to infer building occupancy from AMI data. Nevertheless, AMIbased occupancy detection is still limited compared to other sensor-based detection. Regarding the detection algorithms, data-driven machine learning algorithms are widely applied, due to the growing penetration of sensors. For example, a collection of machine learning models, including a decision tree (DT) model, a SVM, and a Bayes network, were compared in Ref. [90], among which DT was the best model. In addition, SVM beat the hidden Markov model (HMM) and the kNN model with 80% accuracy [91]. Moreover, Jin et al. [92] developed an RF model, which outperformed HMM and SVM models with around 90% accuracy. In contrast with the wide deployment of shallow machine learning algorithms, deep learning is far from fully explored in occupancy detection problems. A CNN is the first deep learning model developed for occupancy detection, which provided occupancy information from indoor climate measurements with 95.42% accuracy [93]. Another deep learning architecture utilized an autoencoder long-term recurrent convolutional network, which identified the occupant activity from Wi-Fienabled Internet of Things devices [94]. To bridge the gap in occupancy detection using AMI data, a deep learning architecture is proposed in this section. The developed deep learning architecture stacks a CNN and a LSTM network sequentially. The developed CNNLSTM architecture is expected to capture both

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spatial and contextual representations of the AMI data. The problem formulation and methodology description are introduced in the rest of this section. The developed model is then compared with state-of-the-art classifiers with a publicly available dataset.

7.4.2 The convolutional neural networklong short-term memory deep learning architecture 7.4.2.1 Occupancy detection problem formulation The objective of the building occupancy detection is to identify the realtime occupancy condition, yARN 3 1 , of a house from its AMI data, XARN 3 F , by using a stacking CNN model and an LSTM model [95]: y^ 5 FðX; WÞ 5 FR FC ðX; WC Þ; WR (7.30) where N and F are the sample size and dimension, respectively. y and y^ are actual and detected occupancy conditions, respectively. FðÞ, FC ðÞ, and FR ðÞ indicate the developed CNNLSTM model and its CNN and LSTM components, respectively. W, WC , and WR are the parameters in the according models. With the condition of either occupied or vacant at every timestamp, that is, yA0; 1, the occupancy detection is a binary classification problem. Therefore the objective is to minimize the loss function, which is based on the weighted binary cross-entropy, given by: J ðWÞ 5 2

N 1X ωyn log y^ n 1 ð1 2 ωÞð1 2 yn Þlog 1 2 y^ n N n51

(7.31)

where ω is the binary cross-entropy weight, defined as ω 5 PY ½y 5 0jyAy. 7.4.2.2 Convolutional neural network With one or multiple feature learning blocks (FLBs) that consist of a convolutional layer and a max-pooling layer, CNN has powerful feature learning ability. A convolutional layer (indexed by l) contains Dðl11Þ filters. A convolution operation is performed in each filter to construct a set of feature maps as: Zl 5 Wl 3 Xl 1 bl

(7.32) ðl11Þ

ðl11Þ

where Xl ARH 3 D is the convolutional layer input. ZARH 3 D , ðl11Þ l ðl11Þ ðl11Þ ðl11Þ WARH 3 D 3 D , and bARH 3 D are the feature map matrix, l

l

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layer parameter tensor, and bias matrix, respectively. Eq. (7.32) can be expressed in detail as: zlhðl11Þ ;dðl11Þ

Hl X Dl h i X 5 wdl l ;hl ;dðl11Þ 3 xlhl ;dl 1 blhðl11Þ ;dðl11Þ

(7.33)

hl 51 d l 51

where (h, d) is a doublet index used to locate the element, x, in X. To add nonlinearity to the network an element-wise rectified linear unite (ReLU) is used to activate convolution outputs. The ReLU activation function is selected due to its computational efficiency, better convergence, superior performance, and amelioration of vanishing gradients compared to other functions [96]. The ReLU function is expressed as: zlhl ;dl 5 maxð0; xlhl ;dl Þ

(7.34)

A max-pooling layer is introduced as the last layer of an FLB to achieve more translation invariance during the spatial representation learning. In this study a unified nonoverlapping moving window is used to subsample the activated convoluted feature maps by a factor of 2. The max-pooling layer is expressed as: n o zlhðl11Þ ;dl 5 max xl jxl : 5 xlhl :hl 1ðH l =ðH l11 ÞÞ;dl (7.35) 7.4.2.3 Long short-term memory network RNNs have been effectively applied in time series data analytics due to its capability of capturing temporal correlations. The LSTM is a variant of RNN, which avoids the vanishing gradient problem by gated regulators. A typical LSTM block contains a memory cell, a forget gate, an input gate, and an output gate, shown as Fig. 7.11. The three gates have different functions, where the input gate determines the new information stored in the memory, the forget gate decides the useless old information to exclude, and the output gate exploits useful information to output from the memory cell. The tensor operations in the gates of a forward LSTM are expressed as: f t 5 σðWf ½ht21 ; Xt 1 bf Þ

(7.36)

it 5 σðWi ½ht21 ; Xt 1 bi Þ

(7.37)

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Figure 7.11 The LSTM building block. LSTM, Long short-term memory.

c~ t 5 tanhðWc ½ht21 ; Xt 1 bc Þ

(7.38)

ct 5 f t 3 ct21 1 it 3 c~ t

(7.39)

ot 5 σðWo ½ht21 ; Xt 1 bo Þ

(7.40)

ht 5 ot 3 tanhðct Þ

(7.41)

where f , i, o, and c are the activation vectors of the forget, input, output gates and the memory cell, respectively. Wf , Wi , Wc , and Wo are the input matrices of gates or memory cell. bf , bi , bc , and bo are the corresponding bias vectors. σðÞ and tanhðÞ are the sigmoid and hyperbolic tangent activation functions, respectively. h is the hidden state and the output of an LSTM hidden layer. c~ is the new state candidate vector. The bracket is the concatenation operator. The nodal connections in an LSTM cell are shown in Fig. 7.11. One typical issue of most recurrent models is that they can only deal with unidirectional dependencies in the data. To capture both forward and backward dependencies in the AMI data a bidirectional LSTM (BiLSTM) layer is included in the LSTM configuration. The nodal connection and the tensor calculations in a BiLSTM are almost the same with those in a unidirectional LSTM, except for the processing directions. The bidirectional operations within a BiLSTM can be expressed as: h i ~f ~ ~t 1~ ft 5σ W bf (7.42) h t21 ; X ’ h’ i ’ ’ ’ f t 5 σ Wf h t11 ; Xt 1 b f

(7.43)

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where - and ’ denote the forward and backward operations, respectively. Both ~ h t and ’h t are generated and concatenated as the output of the BiLSTM: - ’

ht 5 ½ h t ; h t

(7.44)

7.4.2.4 The developed convolutional neural networklong short-term memory architecture The overall framework of the developed CNNLSTM and the tensor manipulations are shown in Fig. 7.12. The developed framework contains three stacking components, which are a CNN network, an LSTM, and a dense layer configuration. The AMI data is first convoluted through the CNN network to generate spatial feature maps, as shown in the bottom left dashed box. Inspired by the VGGNet, two FLBs with four layers (i.e., two convolutional layers and two max-pooling layers) are consisted in the CNN. There are 128 and 64 filters in the two FLBs, respectively, which are used to extract the high-level abstract spatial features from the AMI data. The output of CNN configuration serves as the input to LSTM configuration. To better capture the contextual patterns in the AMI data, we increase the depth of the architecture by stacking three LSTM layers vertically [97]. As shown in Fig. 7.12, the first LSTM layer extracts temporal features from the previous CNN output and generates hidden states. Then, a BiLSTM layer takes both forward and backward dependencies into account, which is combined by the last LSTM layer. The developed BiLSTM configuration is expected to learn hierarchical representations of the convolutional time series of AMI data by operating hidden states at different timescales. The last component in the CNNLSTM model is a dense layer configuration with two fully connected layers and a final classification layer. The output of LSTM is first flattened and fed into the first dense layer, whose output is then fed into the second dense layer: Zl 5 Wl Xl 1 bl

(7.45)

where all the inputs are transmitted to the output. The last layer is a classification layer with sigmoid activation function due to the mutualexclusive character of occupancy detection results:

Figure 7.12 The developed end-to-end CNNLSTM occupancy detection framework. CNN, Convolutional neural network; LSTM, long short-term memory.

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Zl 5

1

(7.46)

l l l 1 1 e2W X 1b

where l 5 10. The occupancy condition could be determined by a applying a threshold to the last-layer output: y^ 5 Hðz 2 thÞ

(7.47)

where HðÞ is the Heaviside step function and th 5 0:5 is the threshold value.

7.4.3 Experiments The developed CNNLSTM model is tested with the largest publicly available dataset with both AMI and occupancy data, that is, the Electricity Consumption and Occupancy dataset [98]. The dataset has summer and winter data of five houses with 1-second resolution (aggregated from data with 1 Hz frequency). To avoid the noise and redundancy, basic features were extracted from the AMI data [99]. The minute data is flattened every hour to fit the CNN input format with W 5 60. The quality-control with two criteria was applied to the dataset: (1) the data length should be more than 900 and (2) both occupancy labels (i.e., occupied and vacant conditions) should be more than 10% of the total occupancy data. As a result, four periods of data from three houses were qualified and selected for case studies, which are summarized in Table 7.9. The ratio of training, validation, and testing data is 3:1:1 for each case study. Hyperparameters of the developed CNNLSTM model are determined by a trial-and-error manner with the training and validation dataset and are listed in Table 7.9. The model has a total of 240,489 trainable parameters (2.5 MB), which is a relatively small network, compared to other famous deep learning architectures (e.g., the 16-layer VGGNet has 528 MB weights). Before being trained, parameters in each layer are Table 7.9 Data summary of case studies. Case notations Data dimension (N, W , F)

Label (occupied/vacant)

C1 C2 C3 C4

774/161 831/272 771/308 1044/323

(935, 60, 30) (1103, 60, 30) (1079, 60, 24) (1367, 60, 29)

Note: The case notations (C1C4) are different from Sections 7.2.3.1 and 7.2.3.2.

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Table 7.10 Hyperparameters of layers in the developed convolutional neural networklong short-term memory (LSTM) model. Layer Type Hyperparameter Trainable parameters

C1

Convolutional

P1

Pooling

C2

Convolutional

P2

Pooling

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Input size: 60 3 F Filter size: 3 Filter number: 128 Window size: 2 3 128 Stride: 2 Input size: 30 3 128 Filter size: 3 Filter number: 64 Window size: 30 3 64 Stride: 2 Output length: 50 Output length: 200 Output length: 50 Neurons: 100 Neurons: 50 Neurons: 1

11,648

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0 23,000 120,800 50,200 5100 5050 51

initialized with the Xavier method, where biases are initialized as zeros and weights conform the Gaussian distribution [100]. Twenty percent of neurons in the convolutional, LSTM, and dense layers are randomly dropped to overcome the possible overfitting [101] (Table 7.10). In this study, mini-batch stochastic gradient descent (SDG) is selected as the optimizer to train the developed CNNLSTM model. The SDG minimizes the objective function JðW Þ in Eq. (7.31) by updating the parameters in the opposite direction of its gradients. The complete data is passed forward and backward through the network with 100 epochs. The mini-batches (B : 5 ½X;y), with a batch size of 30, are randomly generated to shuffle the data order in each epoch. Gradients in each iteration are calculated by averaging over the mini-batch. The learning rate scheduling is used to dampen training oscillations. Specifically, the training starts with a learning rate of 0.1 and reduces the learning rate by 50% when a plateau is reached for more than 10 epochs. To reduce the risk of convergence to local minima, momentum is adopted in the training. The weights are updated with the above techniques as: Vi11 5 γVi 1 ηi11 rW JðW;BÞ

(7.48)

Wi11 5 Wi 2 Vi11

(7.49)

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where i is the iteration index, V is the weight update matrix, γ 5 0:9 is the momentum, and η is the learning rate. Six state-of-the-art machine learningbased classifiers are adopted to compare with the developed CNNLSTM method, which are a kNN model, an SVM model with linear kernel, a GP model, an RF, a multilayer perceptron (MLP) classifier, and an adaptive boosting model (AdaBoost). We believe the selected benchmarks are general and representative, since they cover the nonparametric model (kNN), kernel-based model (SVM), feedforward NN (MLP), and ensemble learning models (RF and AdaBoost). The hyperparameters and parameters of these models are determined empirically and listed in Table 7.11, including the number of neighbors (n_neighbors) in kNN, the penalty weight (C) in SVM, the covariance function (kernel) of the GP, the number of trees (n_estimators), the maximum depth of the tree (max_depth), the number of features for the best split (max_features) in RF, the number of hidden layer neurons (neuron), activation function (activation), regularization penalty (alpha) in MLP, and the maximum number of estimators (n_estimators), and learning rate (learning_rate) in AdaBoost.

7.4.4 Results The case studies are implemented in Python version 3.6 with the Keras, and scikit-learn libraries. The experiments are repeated 10 times to improve the reproducibility and consistency. All the experiments are conducted on a workstation with an Intel Xeon(R) E5-2603 1.6 GHz CPU and an NVIDIA TITAN V GPU. It took 11 minutes to train a CNNLSTM model with the experiment setups described above and 3.7 ms to detect the occupancy at a time step. The computational time is applicable for real-time detection.

Tensorflow,

Table 7.11 Benchmark model hyperparameters and parameters. Model Hyperparameters/parameters kNN

SVM GP RF MLP AdaBoost

n_neighbors 5 3 C 5 0.025 kernel 5 1:0 3 RBF ð1:0 Þ n_estimators 5 10, max_depth 5 5, max_features 5 1 neuron 5 100, activation 5 ReLU, alpha 5 0.01 n_estimators 5 50, learning_rate 5 1.0

RF, random forest; GP, Gaussian process; kNN, k-nearest neighbor; SVM, support vector machine; MLP, multilayer perceptron classifier.

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Being a typical binary classification problem, the occupancy detection can be evaluated based on the binary confusion matrix, which consists of four elements (as shown in Fig. 7.13): (1) true positive (TP), denoting the count that a house is actually occupied and is detected occupied; (2) false positive (FP), denoting the count that a house is actually vacant but is detected occupied; (3) true negative (TN), denoting the count that a house is actually vacant and is detected vacant; and (4) false negative, denoting the count that a house is actually occupied but is detected vacant. Based on the confusion matrix, five metrics are used to quantify the overall performance of the classification models, which are accuracy (ACC), sensitivity (SNS, also known as TP rate or recall), specificity (SPC, also known as TN rate), precision (PRC), and F1 score (F1): TP 1 TN TP 1 TN 1 FP 1 FN TP SNS 5 TP 1 FN TN SPC 5 TN 1 FP TP PRC 5 TP 1 FP SNS 3 PRC F1 5 2 SNS 1 PRC

Detected occupancy condition

ACC 5

1

FP

TP

0

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FN

0 1 Actual occupancy condition Figure 7.13 A binary confusion matrix.

(7.50) (7.51) (7.52) (7.53) (7.54)

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where ACC indicates the overall accuracy of the detection. SNS measures the proportion of the actual occupied conditions that are correctly detected as such. SPC measures the proportion of the actual vacant conditions that are correctly detected as such. PRC is the success probability of detecting a correct occupied condition. F1 combines several metrics in the imbalanced classification problem. The occupancy detection accuracy is not only affected by the models, but also related to the threshold shown in Eq. (7.47). Therefore in addition to the five overall metrics, another set of metrics are used to assess the goodness of the classifiers over the entire operating range, which are receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The former one is a curve of TP rate (SNS) against FP rate (1 2 SPC) at various threshold settings, which can also be used to determine the best th. A larger deviation between the ROC curve and the diagonal line represents a better occupancy detection. The AUC value is a comprehensive measurement of the ROC curve, where a larger AUC value (maximum AUC 5 1) indicates a better result. The confusion matrix of one repeat is shown in Fig. 7.14. Results of different models are located in different columns and matrices in different rows indicate results of different cases. Even though the labels are highly imbalanced in the whole dataset (as listed in Table 7.9), the testing data labels could be balanced. For example, the vacant conditions and occupied conditions ratio is 115:106 in the testing data of C. In addition, the diversity of the four cases directly impacts the model performance. For instance, kNN detects more FP than TN in C1 but vice versa in C2 and C4. Different models perform distinctively in the same case. For example, SVM, GP, and MLP accurately detect more occupied conditions; however, AdaBoost is more powerful in detecting the vacant conditions in C2. We can conclude that both the dataset and the benchmarks are diverse and general for the comparisons. Based on confusion matrices, the evaluation metrics are calculated and listed in Tables 7.12 and 7.13. From Table 7.12, it is observed that every benchmark model has the chance to generate satisfactory occupancy detection, since five out of six benchmark models outperform others in some cases and based on some metrics. Nevertheless, none of the benchmark models can always beat others in all the four cases and all the metrics. In addition, some benchmark models make extremely assertive detections. For example, GP detects occupancy conditions with 100% SNS but 0% SPC in C1, C3, and C4. Some benchmarks are not robust,

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Figure 7.14 Confusion matrices of the occupancy detection results. The darker color indicates a higher frequency, and the gray color means a zero occurrence. Positive diagonal elements indicate right detections, and negative diagonal elements show the wrong detections.

which is revealed by the nonnegligible variance of the metrics over 10 experiment repeats, such as SPC of RF in C2 and SPC of MLP in C4. In contrast, the developed CNNLSTM model shows encouraging accuracy in both the occupied and vacant conditions. More importantly, the CNNLSTM model is more accurate than benchmark models in all the four cases, indicated by two overall evaluation metrics, ACC and F1. The average ACC and F1 over the four cases and 10 experiment repeats are 89.41% and 91.55%, respectively. The robustness of the CNNLSTM model is not only revealed in diverse cases but also shown in 10 sets of experiments, which is supported by the relatively small variances listed in Table 7.13. To assess the model performance independent of the choice of th, ROC and AUC are adopted. Specifically, a set of th values, ranging from 0 to 1, are used to determine y^ and calculate TPR and FPR, as shown in Fig. 7.15. The perfect classifier should have the ROC curve straight up the vertical axis then along the horizontal axis. The classifier that randomly generates occupancy detection results sits on the diagonal, and the classifier that detects complete reverse results has a curve in the bottom left part of the ROC space. Therefore the developed CNNLSTM model has better and more robust occupancy detection capability with

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Table 7.12 Mean of evaluation metrics (%) of the 10 occupancy detection experiment repeats. Case Metric Models

C1

C2

C3

C4

ACC SNS SPC PRC F1 ACC SNS SPC PRC F1 ACC SNS SPC PRC F1 ACC SNS SPC PRC F1

CNNLSTM

kNN

SVM

GP

RF

NN

AdaBoost

91.34 93.13 80.74 96.63 94.84 85.25 82.36 87.91 86.79 84.21 98.47 99.12 96.67 98.81 98.96 82.56 89.50 63.92 87.21 88.20

71.66 70.00 81.48 95.73 80.87 71.04 90.57 53.04 64.00 75.00 84.26 86.79 77.19 91.39 89.03 67.40 85.93 17.57 73.71 79.35

85.03 99.38 0.00 85.48 91.91 61.99 98.11 28.70 55.91 71.23 86.57 92.45 70.18 89.63 91.02 72.16 96.98 5.41 73.38 83.55

85.56 100.00 0.00 85.56 92.22 70.59 99.06 44.35 62.13 76.36 73.61 100.00 0.00 73.61 84.80 72.89 100.00 0.00 72.89 84.32

85.51 99.94 0.00 85.55 92.19 80.81 89.62 72.70 76.69 82.12 84.26 95.66 52.46 85.06 89.99 73.15 99.70 1.76 73.19 84.41

85.29 91.06 51.11 91.74 91.37 65.43 94.43 38.70 59.08 72.48 91.76 92.45 89.82 96.23 94.29 80.04 95.33 38.92 81.02 87.48

87.70 96.88 33.33 89.60 93.09 81.90 65.09 97.39 95.83 77.53 90.74 92.58 85.61 94.72 93.64 80.22 91.96 48.65 82.81 87.14

ACC, Accuracy; CNN, convolutional neural network; GP, Gaussian process; kNN, k-nearest neighbor; LSTM, long short-term memory; PRC, precision; RF, random forest; SNS, sensitivity; SPC, specificity; SVM, support vector machine.

different thresholds. The AUC values of all the experiments are shown in Fig. 7.16, where the developed CNNLSTM outperforms other models consistently. To dig into the reason of better occupancy detection performance of the developed CNNLSTM model, the detection probabilities and results of the developed model are compared to those of the three best benchmarks, that is, SVM, MLP, and AdaBoost. The outperformance of the CNNLSTM model is twofold. First, the developed model is more accurate, revealed by the smaller deviations between detections and the actual conditions. Actually, the CNNLSTM model only misclassifies the occupancy three times at three discrete hours, which is less harmful to the demand response decisions. In contrast, all the three best competing models generate more and longer period wrong detections. The second advantage is that the CNNLSTM model is more confident in the detection it

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Table 7.13 Standard deviation evaluation metrics (%) of the 10 occupancy detection experiment repeats. Case Metric CNNLSTM kNN SVM GP RF NN AdaBoost

C1

C2

C3

C4

ACC SNS SPC PRC F1 ACC SNS SPC PRC F1 ACC SNS SPC PRC F1 ACC SNS SPC PRC F1

0.49 0.59 3.40 0.56 0.30 1.09 6.55 6.18 4.40 1.51 0.38 0.44 1.29 0.45 0.26 1.58 3.95 11.82 3.66 1.07

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.17 0.20 0.00 0.02 0.10 6.80 4.15 15.85 9.92 4.67 4.04 1.98 15.24 3.98 2.32 0.49 0.35 2.30 0.42 0.23

0.85 2.52 9.85 1.34 0.66 4.50 4.41 12.50 4.11 1.81 0.57 0.89 3.86 1.33 0.35 2.56 3.03 16.68 3.86 1.15

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.95 1.59 2.96 1.11 1.35 0.00 0.00 0.00 0.00 0.00

ACC, Accuracy; CNN, convolutional neural network; GP, Gaussian process; kNN, k-nearest neighbor; LSTM, long short-term memory; PRC, precision; RF, random forest; SNS, sensitivity; SPC, specificity; SVM, support vector machine.

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FPR

Figure 7.15 ROC curves in randomly selected experiments. ROC, Receiver operating characteristic.

makes. This is illustrated by the darker color of the classification probabilities, compared to other models, such as AdaBoost. Therefore the outperformance of the developed model is well-explained (Fig. 7.17).

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0.75 0.50 0.25 0.00 C1

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Figure 7.16 The AUC statistics of the 10 experiment repeats. AUC, Area under ROC curve. Probability

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Figure 7.17 The occupancy labels, detection probability, and results of C3.

7.5 Conclusion AI has been witnessed to have significant impacts on power system operations. Large amounts of data are being collected by smart grid devices, such as the AMIs and the PMUs, which facilitate the wide applications of AI techniques to power systems. In this section, two emerging machine learning subfields were introduced: ensemble learning and deep learning. Three use cases were used to demonstrate their applications in power systems. Specifically, competitive and cooperative ensemble learning models were developed to provide short-term wind forecasts. Both methods included state-of-the-art machine learning models, for example, ANNs, SVR models, GBMs, RF models, and Q-learning models. In

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addition, a DLGRU was used to solve a network reconfiguration problem through a “learn to optimize” manner. In the last application case, a deep learning configuration with a CNN and a LSTM network was developed to detect real-time occupancy conditions in smart buildings. Three sets of case studies based on publicly available datasets showed that the developed ensemble learning methodologies and deep learning methods outperformed the corresponding benchmarks. The accurate forecasts and detection are valuable to renewable integration, power system reliability, and building integration into the smart grid.

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CHAPTER EIGHT

Power system operation with power electronic inverter dominated microgrids Yuhua Du1, Xiaonan Lu1 and Xiongfei Wang2 1

Temple University College of Engineering, Philadelphia, PA, United States Department of Energy Technology, Aalborg University, Aalborg, Denmark

2

Contents Nomenclature 8.1 Power system evolution toward modernization 8.2 Networked microgrids with parallel inverters 8.2.1 Advanced microgrid structures 8.2.2 Concept of dynamic microgrids 8.3 Parallel inverter operation in microgrids 8.3.1 Parallel inverter operation in the context of dynamic microgrids—steady-state operation 8.3.2 Parallel inverters operation in the context of dynamic microgrids—transient-state operation 8.4 Conclusion References

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Nomenclature DER DOE DG EPS GFM IEEE PCC POI PV SSW

distributed energy resource Department of Energy distributed generator electric power system grid-forming Institute of Electrical and Electronics Engineers point of common coupling point of interconnection photovoltaic smart switch

New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00008-0

© 2021 Elsevier Inc. All rights reserved.

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8.1 Power system evolution toward modernization Traditional power grids are implemented based on a centralized architecture. It provides limited automation for power system operation and lacks situational awareness. For example, only one-way power flow is allowed and the power generation is regulated following the load. Meanwhile, the whole system has little operational flexibility with respect to new producer participation, agile configuration of system topology, etc. Nowadays, the legacy power system architecture has been dramatically upgraded with the increasing penetration of renewable energy sources and dynamic and controllable load-side management. The concept of “smart grid” was proposed to identify the significant difference of modern power system compared to the conventional one. Different organizations have proposed the descriptions of smart grids from different aspects. US Department of Energy (DOE) proposed that [1] a smarter grid applied technologies, tools, and techniques available now to bring knowledge to power—knowledge capable of making the grid work far more efficiently. The Smart Grids European Technology Platform proposed that [2] a smart grid is an electricity network that can intelligently integrate the actions of all users connected to it (generators, consumers, and those that do both) in order to efficiently deliver sustainable, economic, and secure electricity supplies. Institute of Electrical and Electronics Engineers (IEEE) proposed that [3] the term “smart grid” represents a vision for a digital upgrade of distribution and transmission grids both to optimize current operations and to open up new markets for alternative energy production. It is worth mentioning that conventional power grids are dominated by rotational generating units in the generation and transmission systems, and the distribution systems, which are the backbone to connect upstream bulk power system and downstream end users, are mainly comprised of passive feeders. Along with electric grid modernization, versatile grid resources are integrated into the electric grids. Especially in distribution grids, conventional passive distribution networks are implemented with increasing penetration of power electronic devices, which are controllable devices that can actively participate in grid services (e.g., voltage

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regulation [4] and power quality enhancement [5]). Increasing participation of active sources and loads interfaced with power electronic inverters significantly enhances grid operational flexibility and enables additional functionalities that can help address the issues in today’s electric grids. However, it still poses challenges in terms of system stability, resiliency in face of power outages, coordinated operation with conventional grid resources, etc., which should be fully addressed and have been investigated in the past years.

8.2 Networked microgrids with parallel inverters 8.2.1 Advanced microgrid structures Conventional vision of microgrids generally focuses on a single group of loads and distributed generators (DGs) that form a single electric power system (EPS) with clearly defined electric boundaries and a single point of common coupling (PCC). In case, there are multiple microgrids coexisting in the distribution system; they are treated as independent entities individually and would not be physically interconnected to each other. However, as microgrids being created and integrated into the grid, a viable and advanced microgrid model that interconnects the microgrid with the utility and additional microgrids is proposed in Ref. [6]: advanced hardware, intelligent power electronic inverters, smart controllers, and compatible communications will be the enabling technology mix used to maximize a microgrid system’s economic and operational benefits. Advanced communication interfaces and smart controls will increase the value of the energy provided by these advanced microgrid systems. The reliability, resilience, and interoperable electrical service for conventional and advanced microgrid customers will be vastly improved over the results of today’s installed microgrids. The desire to link multiple microgrids gives rise to a new concept of microgrids, that is, nested microgrids. Nested microgrids, also called interconnected microgrids, microgrid clusters, or aggregated microgrids in different works, refer to the interconnection of multiple adjacent microgrids into one network. Compared to a conventional microgrid that operates independently and only interacts with the external system through a single

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Bulk supply connection (subtransmission)

Distributed substation

R

R Other feeders L

R

L L

L Single customer microgrid

L R

Partial feeder microgrid

Feeder Full feeder microgrid Full substation microgrid

Figure 8.1 Configuration of nested microgrids.

PCC, nested microgrid could have multiple PCCs. A nested microgrid structure envisioned by DOE is presented in Fig. 8.1 [7]. Cooperated operation among nested microgrids is enabled under both grid-connected and islanded mode, which could potentially improve system resiliency, operation efficiency, and economic benefit. The cooperation among microgrids enables the utilization of the most efficient generation methods and has the system run optimally. System resilience could be improved by having areas with deficient generation supported by other nested microgrids that are connected. However, the management of nested microgrids also brings several challenges: • By connecting multiple microgrids together, system operation and coordination become more complicated with the number of assets increases. • Managing multiple PCCs results in additional operation complexity (e.g., protection coordination), which has not been exclusively studied. • Interactions among nested microgrids become critical and require extra control efforts as the nested microgrid network becomes complex. Besides the constantly increasing interests from academia, there have been several real-world projects that put the concept of nested microgrids into practice. For example, the Bronzeville microgrid is initiated by

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Commonwealth Edison to install a microgrid in a location that would allow for the interconnection with a nearby campus microgrid, which has already been implemented by Illinois Institute of Technology. The two microgrids are connected to the grid through different substations; however, they have a tie line allowing them to be connected electrically. Another example is the Olney Town Center microgrid project funded by DOE. The microgrid is arranged into several zones based on the distribution of distributed energy resources (DERs) and critical loads and a microgrid controller is designed such that the system is able to meet the following power quality requirements: system average interruption duration index of less than 2 minutes, reduction in emissions of 20%, and an improvement in efficiency of over 20% [8].

8.2.2 Concept of dynamic microgrids In addition to the notion of nested microgrids, there is also another type of advanced microgrid structure that has been proposed, which is called dynamic microgrids. A dynamic microgrid can be defined as a microgrid with flexible boundaries that expand or shrink to keep the balance between generation and load. The boundaries of these dynamic microgrids would be the substation breakers and distribution automation reclosers and sectionalizers. Compared to conventional microgrids that have static electric boundaries, the structure of dynamic microgrids could be actively varied as per request to the system operator. For example, the topology of a dynamic microgrid could be varied due to a power balancing request for DER integration or participation of distributed system restoration during natural disasters. In the meantime, since dynamic microgrids could also be dominated by power electronic inverter dominated resources, flexible control functionalities could be implemented, such as power quality enhancement and advanced networked reconfiguration. The differences between conventional static microgrids and emerging dynamic microgrids have been briefly presented in Fig. 8.2. A typical multimicrogrid structure is shown in Fig. 8.2. The electric boundary of each static microgrid is predefined. Interconnections among static microgrids are achieved through static breakers at the static PCC. When the breaker is open, the whole microgrid operates as an independent entity with respect to the external system. On the other hand, structure of a

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Electric boundary

Open SSW

Open static breaker

Closed SSW Dynamic Microgrids

Static Microgrids

R R

R

R R

R R

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Figure 8.2 Networked microgrids operation in the context of static microgrids, and dynamic microgrids. Static microgrids share the same PCC while dynamic MGs are interconnected through varying POI.

dynamic microgrid is shown schematically in Fig. 8.2. Dynamic microgrids have varying electric boundaries. Interactions between neighboring dynamic microgrids are achieved using conventional or emerging breakers [e.g., smart switches (SSWs)]. As the topology of each dynamic microgrid varies, their points of interconnection (POIs) become dynamic accordingly.

8.3 Parallel inverter operation in microgrids 8.3.1 Parallel inverter operation in the context of dynamic microgrids—steady-state operation Compared to parallel inverter operation in single microgrids, inverter operation in the context of dynamic microgrids represents more operation challenges on system stability in both steady and transient states. Inverter operation in single microgrids is designed for microgrids with static electric boundaries. The inverter-based DGs would be properly sized and managed as a group to enable the steady-state operation of a microgrid under both grid-connected and islanded modes. It is

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noteworthy that DG’s “plug and play” operation would not change system electric boundaries and all the online DGs are still managed as a group. However, when dealing with variable system topology, a more vital goal is to find the optimal system operation topology that satisfies the designed objective with consideration of system operational constraints. This problem can be resolved with network reconfiguration techniques. Existing optimization objectives for distribution system reconfiguration are concluded as follows: • Transmission line loss minimization: The network reconfiguration aims to minimize the power loss on transmission lines, and loads are preferable to be supported by nearby generations. • System operation reliability maximization: Such objective is usually designed considering system abnormal operation and the network reconfiguration is used to maximize service restoration. • Maximization of renewable penetration (DG hosting capacity): High penetration of intermittent renewables [e.g., photovoltaics (PVs)] could result in undesired system voltage quality issues, which could be mitigated by online reconfiguration in active distribution networks. Besides the aforementioned objectives, system operation constraints are also important when deriving the optimization formulation of network reconfiguration. Existing system operational constraints that have been frequently considered are concluded as follows: • Operational constraints: System steady-state operation status (e.g., bus voltage, transmission line current, and voltage unbalance factor at the critical bus) under each topology should be within the acceptable range. Each DG should be operated under their rated capacity. • Topology constraints: Distribution system should always be kept in a radial structure, mainly because (1) limitation of steady-state power flow calculation techniques and (2) challenge on protection setup. • Physical constraints: System topology variation is achieved using controllable switches, whose quantity and position should be optimized to minimize the cost. Another related constraint is the operation frequency of switches. • Unbalance constraints: Inverter-based DGs are more sensitive toward system unbalance and could be forced to trip under extreme conditions. It is suggested that a three-phase inverter-interfaced DG may trip if its voltage unbalance factor is greater than 3% and the unbalanced loading current should be kept within 10% 20%.

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The aforementioned system topology optimization problem considers mostly system steady-state operation. Inverter-based DGs in these problems are treated as conventional PQ/PV buses. Renewable DGs are considered to cause voltage issues and they may be curtailed if needed. Storage is usually considered as a bidirectional power source with bounded power and energy capacity. On one hand, it can provide more flexible reconfiguration solutions; on the other hand, it would make the reconfiguration problem computationally complex. One special case is when an islanded microgrid is intentionally formed during network reconfiguration. Under such scenario, at least one DG needs to be operated as the slack bus under grid-forming (GFM) mode to stabilize the islanded system voltage and frequency, while the rest DGs could be operated under power control mode, that is, grid-following mode. It is feasible and favorable for parallel inverters to operate under GFM mode simultaneously in an islanded system; however, such scenario has not been extensively discussed in existing works. In addition, cooperative operation among connected DGs regarding system topology variation is usually not covered.

8.3.2 Parallel inverters operation in the context of dynamic microgrids—transient-state operation 8.3.2.1 Inverter dynamic stability during network reconfiguration Conventional network reconfiguration problems are usually derived under transmission level and consider only system steady-state operation stability while ignoring the transient responses on system frequency and voltage during system topology variation. Such an approach is legit mainly due to the fact that conventional transmission systems are energized using synchronous generators with significantly large inertia and are robust against system transient responses. However, this is not always applicable for distribution system reconfiguration in the context of dynamic microgrid operation. Distribution systems have small line impedances and are usually energized by inverter-based DGs with small inertia when islanded. Transient responses caused by system topology transitions are more significant in such type of system. Meanwhile, inverter-based DGs are also more sensitive toward operational voltage and frequency transients. As per IEEE Std. 1547, the DERs shall cease to energize the area EPS and trip within the respective clearing time. Detailed tripping requirements for different types of DERs (Categories I, II, and III) regarding abnormal frequency and voltage are listed next (Tables 8.1 8.4).

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Table 8.1 Distributed energy resource (DER) response to abnormal frequencies for DER of Categories I, II, and III. Shall trip Default settings Ranges of allowable settings function Frequency (Hz) Clearing time (s) Frequency (Hz) Clearing time (s)

OF2 OF1 UF1 UF2

62.0 61.2 58.5 56.5

0.16 300.0 300.0 0.16

61.8 61.0 50.0 50.0

66.0 66.0 59.0 57.0

0.16 1000.0 180.0 1000.0 180.0 1000.0 0.16 1000.0

Table 8.2 Distributed energy resource (DER) response (shall trip) to abnormal voltages for DER of Category I. Shall trip Default settings Ranges of allowable settings function Voltage (p.u.) Clearing time (s) Voltage (p.u.) Clearing time (s)

OV2 OV1 UV1 UV2

1.20 1.10 0.70 0.45

0.16 2.0 2.0 0.16

Fixed at 1.20 1.10 1.20 0.0 0.88 0.0 0.50

Fixed at 0.16 1.0 13.0 2.0 21.0 0.16 2.0

Table 8.3 Distributed energy resource (DER) response (shall trip) to abnormal voltages for DER of Category II. Shall trip Default settings Ranges of allowable settings function Voltage (p.u.) Clearing time (s) Voltage (p.u.) Clearing time (s)

OV2 OV1 UV1 UV2

1.20 1.10 0.70 0.45

0.16 2.0 10.0 0.16

Fixed at 1.20 1.10 1.20 0.0 0.88 0.0 0.50

Fixed at 0.16 1.0 13.0 2.0 21.0 0.16 2.0

Table 8.4 Distributed energy resource (DER) response (shall trip) to abnormal voltages for DER of Category III. Shall trip Default settings Ranges of allowable settings function Voltage (p.u.) Clearing time (s) Voltage (p.u.) Clearing time (s)

OV2 OV1 UV1 UV2

1.20 1.10 0.88 0.50

0.16 13.0 21.0 2.0

Fixed at 1.20 1.10 1.20 0.0 0.88 0.0 0.50

Fixed at 0.16 1.0 13.0 21.0 50.0 2.0 21.0

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Electric system topology variation is usually done by changing the on and off status of controllable switches (e.g., SSW). Without proper regulation, undesired transient responses will be introduced to the inverterdominated system: • Due to the current continuity in the line impedance, any nonzero current flow through the switch at the moment it opens will result in an undesired system transient response. For islanded systems, such type of operation variations can be modeled as step load current changes and the transient responses are mainly determined by the magnitude of through current, selection of droop control gains, and system admittance matrix. • Asynchronous voltage phasors on both sides of an opened switch will result in undesired system transient response at the moment it closes. The mismatched voltage phasor can be modeled as an additional voltage source that is connected in series in the system. The transient responses are mainly determined by the magnitude of mismatched voltage phasors on magnitude, phase and frequency, selection of droop control gains, and system admittance matrix. 8.3.2.2 Network reconfiguration with improved inverter operation performance Based on the aforementioned discussion, it can be concluded that without proper regulation, the transient response during system transits could result in inverter-based DGs ceasing to energization and eventually lead to blackout if the system is not coupled to a strong grid. Such issue is critical during system restoration in the context of dynamic microgrids, as the whole system could be completely islanded and network reconfiguration is supposed to provide grid service restoration. DG dynamic operation constraints should be considered when deriving the optimization problem of system network reconfiguration. Due to the mathematical difficulties, it is not feasible to observe system transient response using conventional power flow techniques, since it will significantly complicate the computation and makes the process of solving the optimization problem time-consuming. Authors in Ref. [9] proposed an alternative approach to incorporate the stability of microgrids and dynamic performance of DGs during network reconfiguration for system restoration. Dynamic simulations are performed in parallel using simulation software (MATLAB/Simulink) to observe system transient response and evaluate the feasibility of reconfiguration schemes. The search of the

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optimal shall not end until the system operation constraints are met from both power flow calculation and dynamic simulation. The proposed method has been applied to the Avista distribution system that serves Pullman, including Washington State University. By applying the proposed method, it is observed using dynamic simulation that improper restoration (restoring all the critical loads in one step) will result in out-bounded transient response on system frequency and voltage, while proper switching actions can show an acceptable system dynamic response. Such approach can effectively avoid generating out-bounded transients during network reconfiguration, which improves system operational resilience. However, it has the following limitations: • On the one hand, accurate dynamic simulation requires sufficiently detailed system models, and on the other hand, depending on the complexity of system modeling, dynamic simulation could be time-consuming. There is a trade-off between the accuracy of dynamic simulation and the computation time it takes to solve the optimization problem. • Though such approach can circumvent reconfiguration schemes that result in out-bounded transients, it does not provide any active solutions that help eliminate the undesired transients. Instead, it would further limit the possible reconfiguration options, which could even result in a nonsolvable optimization searching in extreme cases. 8.3.2.3 Seamless network reconfiguration using advanced inverter control The system operation status can be actively regulated by inverter-based DGs to achieve seamless system topology variations, which can, in turn, ensure the operation stability and resilience of inverter-based DGs. In addition, seamless system topology variations can also enable more reconfiguration options. As previously discussed, to seamlessly open a closed switch, the through current or power needs to be minimized before the corresponding switch changes its status, while to seamlessly reclose an opened switch, the voltage phasors on both sides of the switch need to be synchronized before the switch changes states. Such control objectives can be integrated into the secondary control level. However, conventional secondary control approaches are mainly designed for single microgrids with static topologies and are not applicable for dynamic microgrids operation due to the following reasons: • Conventional secondary control approaches do not coordinate the interconnections of dynamic microgrids during network reconfiguration. System frequency and voltage regulations can be constantly

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enabled using inverter-based DGs; however, extra control efforts are required to achieve seamless system transit. • DGs are managed in a static group in a conventional secondary control level, which could fail when system topology varies. To be more specific, DGs that are connected to the same feeder should regulate system operation states coordinately, while interactions among DGs that belong to isolated microgrids using conventional secondary control could destabilize the entire system. • Distribution systems are usually unbalanced and conventional secondary control considers system voltage unbalance mitigation at a single critical load bus, which could fail when multiple critical load buses are connected in the context of network reconfiguration with dynamic microgrids. Extra control efforts are required to coordinate voltage unbalance mitigation over multiple critical load buses, which have not been explicitly considered in conventional secondary control diagrams. Generally speaking, topology variation of dynamic microgrids starts with the reconfiguration request sent from the system operator to the target switch that needs to change status. DGs shall coordinate to minimize the through power at the target switch or minimize the voltage phasor mismatch across the target switch. The target switch shall change status once the system seamless transition criteria are met and the system can transit to a new topology without undesired transients. The time it takes to fulfill seamless reconfiguration is dependent system by system and could be in the range of milliseconds to seconds, as specified in IEEE Std. 2030.7. Proper reconfiguration of dynamic microgrids requires coordinated control among all the DGs and controllable switches (e.g., SSW). A distributed control framework designed for dynamic microgrids operation is introduced [10,11]. As shown in Fig. 8.3, SSWs are used to identify the electric boundaries of dynamic microgrids. Conventional switches only provide fixed a breaking point in the electric system following local protection scheme, while SSW could be equipped with advanced functionalities, such as remote controllability and communication capability. A number of SSWs have been commercialized, and used in the field, such as the IntelliRupter from S&C Electric Company. The concept of minimum microgrid (minmicrogrid) is proposed as the building block that constructs dynamic microgrids. A min-microgrid is defined as the smallest set of DGs and SSWs that are able to support local critical loads by forming a microgrid, whose installed generation capacity exceeds the critical load therein (as

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R

Local energy resource R

SSW

S

R S

S S

R

R

R

R

S

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Figure 8.3 Configuration of min-microgrids.distributed feeders divided into minimum-MGs (each block represents a minimum-MG)

per request in IEEE Std. 2030.7). The distribution system is assumed to be divided into several min-microgrids and each microgrid in the dynamic framework is comprised of one or more neighboring min-microgrids. The electric boundary of a min-microgrid is defined by the positions of SSWs and the connectivity among neighboring min-microgrids is determined by the on off status of SSWs. Following rules apply when locating the SSWs: • Each min-microgrid has at least one POI that interconnects to the rest of the grid via SSW. • Two adjacent min-microgrids share only one SSW (this can be easily achieved in a radial distribution system). In Ref. [11] a modified IEEE 34-bus test feeder is divided into four min-microgrids using three SSWs. Five identical inverter-based DGs are connected to the system and operate under GFM mode. The test feeder is islanded and operates in the context of dynamic microgrids. As shown in Fig. 8.4, the system initially operates under Topology 1 with SSW12 and SSW23 closed and SSW34 open. The system is initially operated with only primary control and secondary control is initiated at t1 to regulate system frequency and voltage as rated. The system is then requested to transit from Topology 1 to 2 at t2, and then to Topology 3 at t4. Without proper regulation, the reconfiguration is done by directly closing SSW34 at t3, and opening SSW23 at t5. As shown in Fig. 8.5, it could be observed that significant transients will be generated each time the system transits (Δfmax 5 1.6 Hz and ΔVmax 5 0.2 p.u.). To achieve seamless system reconfiguration, the aforementioned seamless transit criteria are met before the target SSW changes status. It is observed from Fig. 8.5 that with proper regulation, no significant transients are introduced during system

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Figure 8.4 Dynamic microgrid reconfiguration operation.

Figure 8.5 Recorded operation states under varying system topology.

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Figure 8.6 Zoomed-in system topology.operation states. (A) SSW34 closes and (B) SSW23 opens.

reconfiguration. The system transient response during system topology variationreconfiguration are zoomed-in and presented in Fig. 8.6. It can be observed that without proper regulation, undesired transients will be introduced to the system at the instance SSW changes status. Additionally, DGs are grouped and managed dynamically, as the connected DGs share the power consumption proportionally according to the real-time system topology [11].

8.4 Conclusion Increasing penetration of inverter-interfaced resources has vastly enhanced the controllability and operational flexibility of modern electric grids. Especially in distribution systems, inverter-based DGs can actively participate in grid services and provide additional functionalities to improve grid operation resiliency. However, it is also noteworthy that increasing power-electronics-based devices involve additional challenges. Since conventional rotational generation units are gradually replaced with inverter-based static sources, system inertia could be significantly reduced, which could jeopardize the operational stability and thereby obstruct the

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deployment of inverter-based resources. Microgrids as local energy systems can effectively organize and coordinate inverter-interfaced devices in a local area. Therefore there are increasing interests in integrating inverterdominated microgrids in distribution systems. Furthermore, multiple variants of conventional static microgrids, such as nested microgrids and dynamic microgrids, can further enhance the flexibility of microgrids and address the operational issues, for example, active stabilization in both the steady and transient states and voltage unbalance mitigation.

References [1] U.S. Department of Energy, The smart grid an introduction, in: Technical Report U.S. Department of Energy, 2008. [2] The SmartGrids European Technology Platform. [Online] ,http://www.smartgrids. eu/ETPSmartGrids.. [3] M. Olken, Beyond the gridlock, IEEE Power Energ. Mag. 7 (2) (2009) 4 6. [4] A. Vaccaro, M. Popov, D. Villacci, V. Terzija, An integrated framework for smart microgrids modeling, monitoring, control, communication, and verification, Proc. IEEE 99 (1) (2011) 119 132. [5] X. Wang, F. Blaabjerg, Harmonic stability in power electronic-based power systems: concept, modeling, and analysis, IEEE Trans. Smart Grid 10 (3) (2019) 2858 2870. [6] W.I. Bower, D.T. Ton, R. Guttromson, et al., The Advanced Microgrid: Integration and Interoperability, Technical Report Sandia National Laboratory, Albuquerque, NM, 2014. [7] S. Bossart, DOE perspective on microgrids, in: Workshop on Advanced Microgrid Concepts and Technologies, 2012. [8] M. Burr, J. Camilleri, D. Lubkeman, Q. Long, Y. Du, Microgrid Optimized Resource Dispatch for Public-Purpose Resiliency and Sustainability, United States, 2017, ,https://www.osti.gov/biblio/1415998.. [9] Y. Xu, C. Liu, K.P. Schneider, F.K. Tuffner, D.T. Ton, Microgrids for service restoration to critical load in a resilient distribution system, IEEE Trans. Smart Grid 9 (1) (2018) 426 437. [10] Y. Du, X. Lu, J. Wang, S. Lukic, Distributed secondary control strategy for microgrid operation with dynamic boundaries, IEEE Trans. Smart Grid 10 (5) (2019) 5269 5282. [11] Y. Du, X. Lu, H. Tu, J. Wang, S. Lukic, Dynamic microgrids with self-organized grid-forming inverters in unbalanced distribution feeders, IEEE J. Emerg. Sel. Top. Power Electron., accepted and in press.

CHAPTER NINE

Automated optimal control in energy systems: the reinforcement learning approach Xiangyu Zhang and Huaiguang Jiang National Renewable Energy Laboratory, Golden, CO, United States

Contents 9.1 Introduction 9.1.1 Background 9.1.2 Markov decision process and Bellman equations 9.1.3 Solving Markov decision process problems 9.1.4 Value-based reinforcement learning 9.1.5 Policy-based reinforcement learning 9.1.6 Actorcritic reinforcement learning 9.1.7 Summary 9.2 Deep reinforcement learning 9.2.1 What is deep reinforcement learning 9.2.2 Introduction to three deep reinforcement learning algorithms 9.2.3 Scalable reinforcement learning frameworks 9.2.4 Curriculum learning 9.2.5 Meta learning 9.2.6 Multiagent system 9.2.7 Summary 9.3 Reinforcement learning in energy systems 9.3.1 Advantages of applying reinforcement learning in engineering problems 9.3.2 Training a reinforcement learning controller 9.3.3 Reinforcement learning applications in energy systems 9.3.4 Some interesting research topics 9.3.5 Limitations and challenges 9.3.6 Summary References

New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00015-8

© 2021 Elsevier Inc. All rights reserved.

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9.1 Introduction In this section the basics of reinforcement learning (RL) will be summarized and reviewed, with a goal to give readers a concise introduction or a refresh of memory on what RL does, what RL components are, and how typical RL problems are usually solved. It is worth noting that this section is not intended to be a thorough explanation of RL preliminary. For detail and more systematic learning of RL, interested readers should refer to Ref. [1] for more thorough explanation.

9.1.1 Background RL, as one of the three key machine-learning methods (the other two are supervised learning and unsupervised learning), enables computers to solve sequential decision-making optimal control problems in a novel manner without formulating an optimization problem. The computer, also known as an RL agent or decision maker, learns an optimal control strategy from the experience collected by interacting with an environment repeatedly. Typically, problems that can be solved by RL are sequential optimal control problems, namely, they involve multistep decision-making. At each control step, as shown in Fig. 9.1, the RL agent makes a decision on what action to take based on the current state/observation st. The chosen action at is sent to the environment, which then evolves given the current system state, action, and other factors. In addition, the action will be evaluated by the environment, rendering a scalar reward rt. By repeatedly making decisions and receiving the rt over the control horizon, the RL agent obtains feedback on how it performs and uses such experience to adjust its future decision-making process. However, due to the strong temporal dependency between steps, it is worth noting that the feedback rt might be delayed, which means a high/low

Figure 9.1 RL agent and environment. D represents the system dynamics of the environment, R is the reward function and asterisks in D, and R represent other internal factors of the environment. RL, Reinforcement learning.

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rt might not be directly attributed to the immediate action at but because of an earlier action at2k or a combination of actions of many previous steps. That being said, the optimal solution for an RL problem might be far from being greedy at each step; sometimes an action that yields low reward at current step might ultimately lead to a global optima. Therefore the goal of RL is to learn an optimal policy π that helps the RL agent achieve the maximum future PT 2t rt1k11 . To obtain such an optimal policy, cumulative reward Gt 5 k50 many different RL algorithms and techniques are devised to learn from a large amount of experience collected by repeated interaction with the environment. The repetition of experience collection and learning will “reinforce” RL agent’s understanding of the environment’s behavior and gradually help it acquire a better control policy. Examples of RL problems In general, problems that are suitable to be solved using RL should meet the following two important features: (1) they can be formulated as an sequential optimal control problem with an objective to be minimized/maximized and (2) there is strong temporal dependency among the control steps, namely, actions at earlier steps have impact on future states and actions. For a better understanding, refer to the following links for some interesting classic control problems that can be properly formulated as RL problems: https://gym.openai. com/envs/#classic_control. These examples are from OpenAI, an artificial intelligence (AI) research laboratory, who designed a Python implementation of RL problems called gym environment, which we will discuss in more detail in Section 9.3.2.

9.1.2 Markov decision process and Bellman equations Based on the previous conceptual understanding, now let us look at a more strict mathematical formulation of RL problems. Markov decision process (MDP) is used to formally formulate the environment in an RL problem. In literature, MDP is usually represented using a quintuple (S; A; P; R; γ), representing five key components of MDP. 1. State set S is a set of states, which describes the system status, characterize system history, and can be used to infer the future of the system. The state at step t is represented as stAS, and the transition between states has Markov property (i.e., P(st11|st) 5 P(st11|s1, s2,. . ., st)). 2. Action set A is a set of all possible actions that can be taken by the RL agent. The action taken by the decision maker at step t is atAA.

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3. State transition probability set P is a set of probability describing the system dynamics. Probability P(st11|st, at)AP represents the chance for the system state that moves from st to st11 considering the action at taken. Similarly, in classic control theory, the state-space representation (i.e., X(k 1 1) 5 AX(t) 1 BU(t)) also describes such state transition relationship, but in a deterministic way. 4. Reward function R is a function describing the environment’s immediate response toward the action at11 taken at state st. The environment can be stochastic, meaning the response for a given (st, at) might be random. 5. Discount factor γA[0,1] is used to discount future reward. Reasons to use a discount factor include avoiding infinite Gt due to infinite horizon or cyclic Markov process. If T6¼N, γ 5 1 can be used. The goal to solve a MDP problem is to find a behavior guideline to help the agent/decision maker to achieve maximum expected future the T 2t k discounted cumulative reward EðGt Þ 5 E Σk50 γ Urt1k11 , where ’tAT . 9.1.2.1 Policy The abovementioned behavior guideline for the RL agent interacting with an MDP problem is called a policy. A policy is a conditional probability distribution, showing the probability of an action being selected under a specific state: πðat jst Þ 5 P ½a 5 at js 5 st In general, a policy can be either deterministic or stochastic. Depending on the specific MDP, one might be better than the other. 9.1.2.2 Value function Value functions are mapping relationships that project current state st or state-action pair {st, at} to the expected sum of future cumulative discounted reward under a specific policy π. Mathematically, the state-value function and action-value function can be represented as follows: Vπ ðst Þ 5 Eπ ½Gt js 5 st Qπ ðst ; at Þ 5 Eπ ½Gt js 5 st ; a 5 at Different policies define different agent behavior, and thus even with the same state st, values for it under different policies might be different (i.e., Vπ1 ðst Þ 6¼ Vπ2 ðst Þ).

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9.1.2.3 Bellman equations From the perspective of dynamic programming, Bellman equations show the relationship between the value functions of two adjacent steps: Vπ ðst Þ 5 Eπ ½Gt js 5 st 5 Eπ ½rt11 1 γGt11 js 5 st 5 Eπ ½rt11 1 γVπ ðst11 Þ js 5 st Qπ ðst ; at Þ 5 Eπ ½Gt js 5 st ; a 5 at 5 Eπ ½rt11 1 γGt11 js 5 st ; a 5 at 5 Eπ ½rt11 1 γVπ ðst11 Þ js 5 st ; a 5 at 5 Eπ ½rt11 1 γEa ½Qπ ðst11 ; at11 Þ js 5 st ; a 5 at The Bellman equations show a very important dynamic programming concept called bootstrapping, namely, use a prediction (e.g., Vπ(st11)) to update another prediction (e.g., Vπ(st)). This concept lays crucial groundwork for the development of RL algorithms later. 9.1.2.4 Bellman expectation equations The following two equations represent the relationship between statevalue functions and action-value functions: Vπ ðst Þ 5

X

πða j st ÞUQπ ðst ; aÞ X Qπ ðst ; at Þ 5 rt11 1 γU Pðs j st ; at ÞUVπ ðsÞ aAA

sAS

Merging these two equations, there will be: ! X X Vπ ðst Þ 5 πðajst ÞU rt11;a 1 γU Pðs j st ; at ÞUVπ ðsÞ sAS aAA ! X X Qπ ðst ; at Þ 5 rt11 1 γU P ðsjst ; at ÞU πðajsÞUQπ ðs; aÞ sAS

aAA

9.1.2.5 Bellman optimal equations Note that in the Bellman expectation equations, value functions are evaluated for a given policy π. Imaging the following policy that always provides the maximum possible values: ( 1 if a 5 arg maxQπ ðs; aÞ aAA;πAΠ π ðajsÞ 5 0 else

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Π above is an infinite set of all possible policies. Such a π is called an optimal policy. With π , there will be V (s) 5 maxπVπ(s) and Q (s, a) 5 maxπQπ(s, a). When considering π , Bellman expectation equations become Bellman optimality equations: V ðst Þ 5 maxQ ðst ; aÞ aAA X Q ðst ; at Þ 5 rt11 1 γU P ðsjst ; at ÞUV ðsÞ

sAS

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X sAS

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9.1.3 Solving Markov decision process problems For a known MDP problem (all elements in the quintuple (S; A; P; R; γ) are known), by solving the Bellman optimality equations above, we can obtain the optimal policy π . These functions are nonlinear and usually require iterative algorithms to solve (i.e., dynamic programming). Interested readers refer to Sections 4.3 and 4.4 of Ref. [1] for details about these iterative methods such as policy iteration and value iteration. However, one practical issue is that when MDP is not perfectly known, which is often the case for many real-world MDP problems, we will not be able to directly use P(s|st, at) and R to calculate the value functions and solve the Bellman optimality equations. Though state transition and reward information are not directly available, if the agent can learn them or even the optimal policy from interacting experience, this will lead to many different RL algorithms.

9.1.4 Value-based reinforcement learning In this section the first type of RL methods, known as value-based methods, are explained. Similar to solving MDP using dynamic programming above, value-based methods learns an optimal policy indirectly by learning the value functions. When the optimal value functions are learned, a greedy policy can be implemented by always choosing the action that gives higher value (i.e., ðat 5 argmaxaAA Q ðst ; aÞÞ).

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Depending on how value functions are estimated, there are two approaches called Monte Carlo (MC) and temporal difference (TD) learning, which will be introduced next. 9.1.4.1 Monte Carlo reinforcement learning MC RL learns the value functions from experience, the idea is straightforward: since the value function reflects the expected return given a state or a state-action pair, we can obtain the expected return by sampling and use the empirical mean to approximate the expected value, given enough experience (reason why this is called MC). The Monte Carlo learning algorithm for control follows the following steps (Algorithm 9.1): To guarantee continual exploration, ε-greedy method is used for policy update. Instead of being purely greedy, ε-greedy policy chooses the greedy action ða 5 arg maxaAA Qπ ðs; aÞÞ with probability 1 2 ε (where εA(0,1)) and evenly samples all possible actions with probability ε. In Algorithm 9.1, ε is set to be the reciprocal of the iteration number k. By doing this, a larger value of ε encourages exploration at the early learning phases and eventually when the agent has collected enough experience, ε becomes smaller and this gradually reduce the probability of exploring new actions. One apparent drawback is that MC control requires a complete trajectory to update the Q-value function using Gt, this means policy update is relatively infrequent (can only update till the sequence is completed) and since Gt from a specific sequence is used, the update has higher variance. Also, due to the

Algorithm 9.1 Monte Carlo control [1,2]

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requirement for a complete trajectory, MC can only be used for finite step problem (T ¼ 6 N). 9.1.4.2 Temporal difference reinforcement learning Another type of learning, called TD learning, is introduced to address the issue of MC learning mentioned before. Interestingly, TD learning not only possesses the merit of MC learning (ability to learn from an unknown MDP through experience) but also resembles dynamic programming that updates an estimate using another estimate (i.e., bootstrapping mentioned earlier) and thus able to learn from incomplete sequence. In order to learn from incomplete sequence, instead of using the actual episodic return Gt, TD learning estimates the value of Gt. Depending on how the estimation is made, there comes two different implementations: SARSA learning and Q-learning. Before delving into the difference between them, let us first understand the following two concepts: 1. On-policy learning approaches learn to improve the policy π from experience collected using π. 2. Off-policy learning approaches learn to improve the policy π from experience collected using another policy π0 . SARSA is an on-policy TD learning algorithm, which follows the steps shown in Algorithm 9.2. According to the algorithm, one difference between SARSA and MC learning is that SARSA updates Q-value function every time step. Some literature also call this online learning or Algorithm 9.2 SARSA learning [1,2]

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incremental learning, in which learning happens within control steps, while MC learning is in an offline manner, since learning starts only after all experiences are collected. In addition, an even larger difference between these two learning method is the way they update the value function: in SARSA, by bootstrapping, rt 1 γQk ðst11 ; at11 Þ is used to estimate Gt. Since the next step action based on current policy is leveraged (i.e., at11 5 πk ðst11 Þ), and the algorithm uses the experience it collected using πk to update πk related Qk, it is why SARSA is an on-policy learning method. Q-learning [3], on the other hand, is an off-policy learning method. Instead of estimating Gt using rt 1 γQk ðst11 ; at11 Þ, it takes a greedy approach and uses Gt 5 rt 1 γmaxaAA Qk ðst1t ; aÞ in Qk update. This greedy update let Qk directly approximate the optimal value and is able to lead to an early convergence. However, it is worth noting that even though Qk is updated greedily, during agent’s rollout of the control horizon, the action it takes still follows the ε-greedy manner (i.e., ak 5 πk(sk)). This is to guarantee the exploration of more actions and states, which leads to a successful convergence of Q. In summary the value-based RL algorithm above learns the optimal value function values from experience. With optimal value functions the action at each control step is determined by a 5 argmaxaAA Qπ ðs; aÞ during control. 9.1.4.3 Value function approximation If the number of RL state and action is limited, a table with dimension of jS j 3 jAj can be used to store the optimal values. This table is called Qtable and combining Q-table with Q-learning is the learning approach often called tabular Q-learning. However, one issue with Q-table is that it does not work if the number of state is large or even infinite (for continuous states). In this case a parameterized function is used to approximate the relationship between state/action values and state/state-action (i.e., Vw(s) for state-value function and Qθ(s, a) to action-value function, and w and θ are parameters to be identified).

9.1.5 Policy-based reinforcement learning As introduced in previous section, value-based RL algorithms first learn values of actions and then choose actions with highest values during control. A policy is implicitly established based on all learned action values. In this section the policy-based RL, which explicitly learns a parameterized policy, is discussed. Notation πθ ðat j st Þ is used to represent the policy,

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and θAℝm is the parameters vector that determines the policy (m is an arbitrary number representing parameter dimension). Essentially, πθ ðat j st Þ represents a probability of action selection, as shown next: πθ ðat j st Þ 5 Pða 5 at js 5 st ; θÞ Since the controller’s performance measure depends on the control policy, which is parameterized by θ, the performance measure can thus be expressed as a function of θ, as in J(θ). To train an optimal controller, one can adjust θ to the direction where the performance measure J(θ) is maximized. Mathematically, controller parameters can be updated in the following gradient ascent manner: θk11 5 θk 1 αrJðθk Þ For episodic learning the performance measure can be represented in the form of the value function: JðθÞ 5 Vπθ ðs0 Þ, where s0 is the initial state. Using Bellmen equations, the gradient of J(θ) can be estimated as follows: rθ JðθÞ 5 Eπθ Qπθ ðs; aÞrθ logπθ ðajsÞ This is called policy gradient theorem (deduction process is omitted here; refer to Section 13.2 in Ref. [1] for details). RL algorithms that are based on this theorem are called policy gradient methods. Specifically, an MC version of policy gradient algorithm, also named REINFORCE [4], is

Algorithm 9.3 Monte Carlo policy gradient algorithm [4]

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introduced in Algorithm 9.3. Due to Eπθ ½Qπθ ðs; aÞ 5 Eπθ ½Gt js; a, the gradient of J(θ) is estimated using Eπθ ½Gt rθ log πθ ðajsÞ in REINFORCE. In many other policy gradient algorithms the term Qπθ ðs; aÞ is replaced with other terms in order to reduce variance level. Interested reader can refer to Section 2 of Ref. [5] for a summary of different Qπθ ðs; aÞ variants.

9.1.6 Actorcritic reinforcement learning To reduce variance and accelerate learning the idea of TD learning introduced earlier in value-based learning is again applied in the policy gradient algorithms. In actorcritic methods, not only the policy πθ but also a parameterized value function Vw(st) are learned. The policy learning part is called actor since the policy guides the RL agent to act and the value function learning part is called critic, which evaluates the value of each state or state-action pair. As a result, when updating the policy, instead of using Gt as in REINFORCE, bootstrapping is leveraged and thus using rt11 1 γVw ðst11 Þ to represent the reward-to-go. The updating rule is shown next, and the part with underline is called baseline, which is another technique that helps reduce variance during learning. θk11 ’θk 1 αθ ðrt11 1 γVw ðst11 Þ 2 Vw ðst ÞÞrθ log πθ ðat jst Þ Critic update for the value function parameters is shown as follows: wk11 ’wk 1 αw ðrt11 1 γVw ðst11 Þ 2 Vw ðst ÞÞrw Vw ðst Þ

9.1.7 Summary In this section, basic concepts regarding RL are first introduced and the nature of RL problems is described (i.e., sequential optimal decision-making). Then, a mathematical formulation of typical RL problems, the MDP, is presented and Bellman equations are introduced to solve MDP. Next, the need for RL algorithms is introduced in the case when MDP is not fully known and three types of RL algorithms (value-based, policy-based, and actorcritic methods) are discussed. By reading this section, we hope readers can have a preliminary understanding about RL and the logic behind its development. Again, for more indepth and systematic study of RL, we suggest interested readers to refer to Ref. [1] and many other learning resources.

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9.2 Deep reinforcement learning 9.2.1 What is deep reinforcement learning Deep RL (DRL) is the combination of deep learning and RL; specifically, deep neural network (NN) is used to approximate the RL value function or the control policy. In the 2010s the successful story from DeepMind for using DRL to train a controller to play Atari game marks a significant milestone in RL’s development [6]. Their experiment for using a convolutional NN (CNN) for value function approximation and with only raw game video pixels as inputs demonstrates the possibility of having end-to-end RL, which avoids labor-intensive work for crafting features for function approximation and truly shows the potential of artificial intelligence (AI). Moreover, NN can represent more complex inputoutput relationship than linear functions and it is expected to help solve more complicated decision-making problems.

9.2.2 Introduction to three deep reinforcement learning algorithms In this part, three DRL algorithms of different types are introduced: a value-based method, a policy gradient/actorcritic method, and evolutionary strategiesbased method. These three algorithms are three representatives to a series of RL algorithm categories; as a result, after introducing each of them, other algorithms of the same category are briefly mentioned and interested readers are suggested to read the corresponding papers for more details. 9.2.2.1 Deep Q-network: a value-based approach Recall the Q-learning discussed in the previous section—it is a value-based algorithm, which learns the action values for different state-action pairs Q (s, a) and then establish an implicit control policy based on the learned Q-values. Based on Q-learning, a deep Q-network (DQN) uses a deep NN for actionvalue function approximation in contrast to earlier tabular Q-learning or approaches using linear function approximation. Specifically, an NN with state as input and action values as outputs will be learned. There are several reasons for using NN for value function approximation: (1) NN can properly take in high-dimensional input data and map it to the Q-value; (2) unlike linear function approximation, NN does not require handcrafted features as inputs; and

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Figure 9.2 Example Q-network for a simple hypothetical RL problem with st Aℝ4 as the state and three actions from the legal action set. As shown in the figure, st is the network input and outputs are action values of three actions Q (st, at), i A {1, 2, 3}.

(3) NN is differentiable by back-propagation [7], which can be used in gradient-based method. Fig. 9.2 shows an example of a DQN (Q(s, a, w), in which w represents the NN parameters): typically the input is st and output dimension equals to the number of discrete actions (yes, DQN only works with RL problems with finite actions). It is worth noting that although a fully connected NN is shown in Fig. 9.2 as an example, DQN can be in any NN form such as CNN (as in the original DQN paper, st is the raw pixels of game video image) or a recurrent NN (RNN) (if the input contains some time series information such as renewable generation forecasts in the energy-related control problem) or with mixed type of inputs. In the same vein as training a NN in supervised learning, training a DQN can be done by minimizing the loss between DQN outputs and the ground truth values. However, in RL, there are no ground truth values, but only experience. As a result, one idea is to use bootstrapping to generate the “ground truth” values and thus the loss function can be defined as: 2 0 0 LðwÞ 5 E ðr 1γ maxQðs ; a ; w Þ2Qðs; a; w ÞÞ aAA

However, when directly using Q-learning with NN function approximation and bootstrapping, there will be algorithm stability issue and might even cause divergence. As a result, two techniques are proposed in the original DQN paper [6,8] and they are proven to be effective in improving the training stability: first, introducing a target Q-network, whose parameters (w2) are copied from the learning Q-network (w) periodically

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and stay fixed for many steps, to generate the “ground truth” values. Therefore the loss function becomes: 2 0 0 2 LðwÞ 5 E ðr 1γ maxQðs ; a ; w Þ2Qðs; a; w ÞÞ aAA

The second mechanism used for stabilizing training is the use of experience pool called experience replay. Specifically, the agent’s experience at each time step are saved in a replay memory, and during Q-learning update, sample of experience will be drawn at random from the pool. The benefit that this experience replay brings include (1) improving data efficiency as each step of experience might be used in multiple updates and (2) randomly sample experience from the pool can effectively break correlations between samples, which helps reduce variance. In addition to the original DQN paper, other studies propose some improvement based on the plain DQN. For example, double Q-learning [9,10] is used to overcome the issue that Q-learning sometimes suffer from action-value overestimation and a prioritized replay [11] is introduced to prioritize samples in the experience replay and to learn from the most important experience. Wang et al. introduces a dueling network [12] to improve the learning performance and Hessel et al. combine six different techniques proposed in literature for improving DQN into a RAINBOW DQN [13] and show that it can achieve a better performance. 9.2.2.2 Asynchronous advantage actorcritic: an actorcritic approach Asynchronous advantage actorcritic (A3C), as its name indicates, is an actorcritic algorithm that focuses on parallel learning by having workers update the learning policy in an asynchronous manner. With A3C, each worker has its own gym environment instance as an action test bed, shown as ➀ and ➁ in Fig. 9.3. With the collected experience, each worker calculates gradients regarding to all samples and accumulates them together. Once all samples are processed, the accumulated gradient will be used to update a global shared parameter vector (➂). The global parameters, being updated by all workers asynchronously, are then fetched to each worker (➃), who will use the updated parameters to start a new iteration of experience collection. Pseudocode for each A3C worker is shown as in Algorithm 9.4. From the gradient update part, it can be seen that this follows exactly the same rule as we describe in the last section about the actorcritic algorithm.

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Figure 9.3 A3C illustration. Parallel works collect experiences independently and update a set of global parameter asynchronously. A3C, Asynchronous advantage actorcritic.

But by relying on parallel workers and update policy periodically shows an outstanding stabilizing effect on training a deep NN with online RL algorithm. Similar to DQN, which uses an experience replay to decorrelate the sample used for policy update, A3C uses experience collected from parallel workers, which are collected from different environments and thus break the correlation. This parallel learning, in addition, has demonstrated even better learning performance using single machine with multicore processor when compared with the GPU-based algorithm. A variant of A3C is A2C (advantage actorcritic, removing the A from asynchronous in A3C), which is a synchronous version of A3C. Instead of updating policy asynchronously, A2C waits for every parallel worker and updates the policy synchronously, and then each worker will fetch the same updated global policy for the next iteration. According to the experiment from OpenAI, A2C implementation is “more costeffective than A3C when using single-GPU machines, and is faster than a CPU-only A3C implementation when using larger policies” [15]. Besides A2C and A3C, there are many other actorcritic algorithms. They all hold the core concept of policy gradient/actorcritic method, but they proposed different mechanisms to stabilize the learning process or accelerate the policy convergence. Some examples are as follows: deep deterministic policy gradient (DDPG) [16] is proposed to learn a deterministic policy for problems with continuous action spaces; trust region policy optimization (TRPO) [17] establishes a trust region using KL divergence when updating policy in order to provide a monotonic performance improvement; similarly, a proximal policy optimization (PPO) algorithm [18] is proposed to simplify the implementation of TRPO but

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Algorithm 9.4 Asynchronous advantage actorcritic pseudocode for each parallel worker [14]

remain the learning performance; actorcritic using Kronecker-factored trust region (ACTKR) [19] is proposed that uses Kronecker-factored approximation curvature to update gradient; and more [20,21]. 9.2.2.3 Evolution strategiesbased reinforcement learning In the abovementioned algorithms, gradient back-propagation is needed for NN training at each training step. In contrast to this, there have been some efforts to use even less computation for NN training. As a result, evolution strategies (ES), a type of gradient-free black-box optimization, has been utilized for network training in RL (Algorithm 9.5) [22].

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Algorithm 9.5 Evolution strategiesbased reinforcement learning [22]

ES algorithm mimics the evolution process in the nature: starting from a generation with an initial set of parameters (known as genotypes), the ES perturbs such genotypes in various ways and created new genomes (known as mutation), and the fitness of the new genomes will be evaluated by the “nature” and the best one will be recombined as base gene for the next generation. By repeating this mutation and selection iteration a set of optimal parameters are expected to be obtained generation after generation. Similarly, the same algorithm can be used in optimizing RL policy, which is assumed to be parameterized by a parameter vector θ. Assuming by following the policy πθ, the RL received is F(θ) and the goal is to find θ that maximize a stochastic objective of EEBNð0;IÞ ½Fðθ 1 σEÞ (σ as the noise standard deviation is given). This maximization problem can be solved by stochastic gradient ascent and the gradient can be directly approximated by samples [22]: rθ EEBNð0;IÞ ½Fðθ 1 σEÞ 5 σ1 EEBNð0;IÞ ½Fðθ 1 σEÞE According to Ref. [22], because ES does not need back-propagation, it can reduce the computation load by two-thirds when compared with other RL algorithms. Besides, ES-RL can also be scaled conveniently without tremendous communication overhead increase: consider scaling the PPO [18], an actorcritic algorithm, at each iteration, all parallel workers need to share with each other the entire gradient information, thus requiring higher communication bandwidth. In contrast, ES only requires sharing the scalar reward Fi and Ei . Moreover, sharing Ei can be avoided by synchronizing random seeds among workers before optimization, as a result, Ei from other workers can be generated locally using the known seed. The information sharing and policy

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parameter update part only accounts for a small fraction of the total learning time, according to Ref. [22], even when scaled up to 1440 workers. Another advantage identified is that ES demonstrates better exploration behavior than other policy gradientbased methods, as ES is able to perform actions that are not possible to be learned by other algorithms. In summary, ES-RL provides an additional perspective on how RL can be trained. The approach of solving RL problems by leveraging black-box optimization is called direct policy search. Another example of this type of algorithm, augmented random search, can be found in Ref. [23]. The authors show that the sample efficiency of direct policy search is not as bad as believed.

9.2.3 Scalable reinforcement learning frameworks Recall that in RL, the agent is generally repeating two steps iteration after iteration: experience collection and policy update. This pattern applies to many different RL algorithms, value-based or actorcritic, and up until a point, the two steps are conducted sequentially: in DQN the single RL agent collects experience from environment and puts them in experience replay and when a certain amount of experience has been collected, it starts to update the Q-network; in PPO, though multiple workers can be used, they first collect experience together, and until everyone is done, the gradient information are shared among all workers and they update the policy together using the averaged gradient and considering the average KL divergence. In A3C, although each work updates the policy asynchronously, but for a single worker, the two steps are still sequential. Later, researchers start to think: can these two steps be conducted in a parallel manner, namely, to decouple the “acting” and “learning” parts. As a result, a distributed architecture for scalable learning is proposed in Ref. [24]. In this architecture, as shown in Fig. 9.4, there are distributed workers that are specialized in experience collection in a parallel manner with their own environments (shown as ➀), and the experience are then pushed into an experience replay pool shared by all actors (shown as ➁). The actors are also responsible to compute initial priorities for the data. In parallel with experience collection, there is a learner that samples from the prioritized experience replay and use them for policy update (as shown in ➂ and ➃). The learner is usually powered by GPU to enable fast gradient computation and high data throughput. The priorities of experience are

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Figure 9.4 Ape-X illustration.

also updated by the learner (shown as ➄) to enable a biased sampling that helps the learner focus on learning using the most valuable experience. Depending on the actual control problems (whether continuous action space or discrete), the learner can use DQN or DPG (or other off-policy algorithms) for value/policy update. Finally, as shown in ➅, the updated policy will be periodically fetched by actors and use it to collect more experience. In experiments, this distributed architecture can achieve state-of-theart performance on both discrete and continuous tasks, when compared with other algorithms from the perspectives of performance and learning wall time. However, it is worth noting that the highly distributed learning architecture is based on one assumption: large amount of learning experience can be generated in parallel easily. Otherwise, if the data take long time to simulate or the simulator itself cannot be properly run in parallel, Ape-X might not be the optimal option. Another architecture in a similar vein is the Importance Weighted ActorLearner Architecture (IMPALA) [25].

9.2.4 Curriculum learning In the previous section and the one before the efforts of enabling DRL and scaling it for accelerated learning are introduced. In this section, work from another perspective for efficient learning is introduced, and that is curriculum learning.

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Human learners benefit from learning using a curriculum: the knowledge are presented in a meaningful order and the student’s understanding to more complex concepts and more intricate problems gradually deepens. The same idea intrigues researchers from cognitive science domain, and they start to think: will machine learning also benefit from a similar learning setting? Bengio et al. [26] conduct some experiments and lead to some results that support this hypothesis, and there are some other works [27,28] that find success when using curriculum learning to solve difficult tasks. Though there are some signs that curriculum learning is effective, one question arises: how to generate a reasonable curriculum and especially generate in automated manner, instead of being handcrafted. To this end, Matiisen et al. [29] propose a teacherstudent curriculum learning framework, in which the teacher will automatically generate learning tasks for the student based on its learning performance. An experiment on solving a Minecraft maze problem is conducted, and it shows that the automatic curriculum learning can lead the student to successfully solve the problem by breaking the complicated problem into several subtasks and master them progressive. In contrast, direct RL on the problem cannot lead to a solver.

9.2.5 Meta learning Meta learning is subarea in machine learning, which widely employed on metadata with its automatic learning algorithms. This algorithm aims to solve the scalability problem, which is defined as the capability of an algorithm to handle an increasing number of works in different applications or conditions. In general machine learning the scalability is a tricky problem and difficult to tune, for example, a designed algorithm performs very well in one domain, but not in other domains or new domains. The reason is a well-trained algorithm fitting its own data domain very well, and this blocks the algorithm to adapt the problem in new domain. In other words, it is difficult for the traditional algorithms to learn the inner connection between the data domains. In addition, the metalearning concept is widely broaden to few-shot learning and one-shot learning. In general, there are three major types of meta learning to solve this problem: RNNbased (or model-based), metric-based, and optimizationbased. RNN-based: This kind of metalearning contains an RNN [such as long short-term memory (LSTM) and gated recurrent units (GRU)] as a

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memory to keep the parameters (to be tuned). In the training step the parameters are updated recurrently as both input and output of the RNN. Considering the large computation load, multiple GPU (hundreds or even thousands of level) are employed for training the model concurrently. In the objective designed the performance of the algorithm such as accuracy and residual errors are treated as rewards to tune the policy of the RNN controller. Neural architecture search (NAS) is a famous example in this kind of algorithm [30], which is a critical algorithm in automated machine learning (AutoML). Metric-based: The nearest neighbor idea is the core for the metric-based metalearning algorithm, which aims to find and measure the distance generated in high-dimensional space by the kernel tricks. Therefore the objective function is based on the relative distance in high-dimensional space. Different NNs and kernel tricks can be seemed as embedding functions to map the original data into high-dimensional functions. In this area, face verification is a typical application, and convolutional Siamese neural network is a famous algorithm [31], which can be widely employed in energy system security. Optimization-based: The optimization-based algorithm is similar as calculus of variations, which employs a bunch of subfunctions for different subtasks. The metalearning function learns the inner connections between different subtasks and generate new functions to adapt new data domains. Specifically, this process can be explained to learn the prior distribution of the parameters in machine learning. According to the Bayesian law, the posterior distribution is computed with prior distribution and maximum likelihood, which can be received by traditional machine-learning technologies. In this area, two typical algorithms are MAML [32] and Reptile [33], which can be used as basic algorithms for few data state estimation and forecasting algorithms in power system applications.

9.2.6 Multiagent system The multiagent system is defined as a group of organized and multiinteracting intelligent agents, which provide a more accurate model for self-organized systems such as power system with a lot of distributed energy resources (DERs) and transportation systems [34]. Considering the behaviors and cooperation manner of the agents, the multiagent system can be divided into three types of algorithms: cooperative, competitive, and mixed setting [35]. Cooperative multiagent system: In the cooperative multiagent system the Nash equilibrium can be seemed as global optimum [35], and all the agents

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contains similar goal and share full or partial information. For example, in Ref. [34], the distributed bidirectional charging and discharging stations and electrical vehicles in a urban area are modeled as multiple agents; they cooperate and share full information to achieve the same global objective in large scale, while also keeping its own objective in small scale. In Refs. [36,37] a distributed algorithm are employed to model the multiagent system in both discrete and continuous domain. In very large scale the mean-field algorithm is used to model a large volume of agents in Ref. [38], which formulates the agents into two equivalent agents. Competitive multiagent system: According to Game theory, the competitive multiagent systems are usually modeled as zero-sum system, which also indicates this kind of system are usually modeled or formulated into two equivalent players. In this area the algorithms can be divided into value-based algorithms and policy-based algorithms [35]. In value-based algorithms a linear program method is presented in Refs. [35,39]. In policy-based algorithms a global convergence of nested policy gradient is discussed in Ref. [40]. Mixed multiagent system: Compared with the cooperative multiagent system and the competitive multiagent system, the mixed multiagent system is more challenging and less well understood [35]. In this area the major challenging is how to find a Nash equilibrium in different scenarios. Similar as before, this area can also be divided into two major areas—policy-based and value-based, and the detailed information can accessed in Refs. [35,41,42]. In power system areas, considering different applications, these three major models can be combined flexibly and to handle the problems in different scales.

9.2.7 Summary In this section, DRL is introduced and by presenting three representative DRL algorithms, how to integrate deep NN for RL training is demonstrated. Techniques such as scalable learning platform and curriculum learning are shown as some efforts for achieving more efficient learning. Other topics such as metalearning and multiagent learning are briefly covered. It is worth noting that at this moment, this is a very active and productive research community, and domain scientist from energy engineering domains can closely follow the development in DRL and to see how the state-of-the-art algorithms be leveraged to solve real-life engineering problems, which will be discussed in more detail next.

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9.3 Reinforcement learning in energy systems In this section, how RL is and can be used for solving control problems in energy systems is discussed. This section will be divided into four parts. First, advantages of RL that make it a promising candidate for solving some engineering-related optimal control problems are presented. Second, the tasks and workflow to design an RL learning environment for specific engineering problems are demonstrated. Third, RL applications on several energy-related research topics are reviewed. Finally, limitations and challenges for using RL in real-life applications are discussed.

9.3.1 Advantages of applying reinforcement learning in engineering problems The need for advance optimal control under sophisticated environments in modern engineering and the rapid development in AI have inspired many emerging research on applying RL in solving engineering problems. Practically, following are the several potential advantages of RL that make it competitive to the state-of-the-art optimal controllers. 1. Real-time readiness Existing approaches for conducting sequential optimal control are mostly based on optimization approaches, which might need to solve optimization problems at each control interval repeatedly (e.g., model predictive control). Two issues arise in some cases: (1) it does not work for some complicated control problems that involve optimization problems take long time to be solved; (2) it requires powerful computers for the on-demand computation. In contrast, an RL controller can be trained/optimized before deployment in an offline manner and during control, the decision-making only involves a situation evaluation process (i.e., evaluating value or policy network, which requires very light computation). Therefore RL controllers are suitable for problems that prefer fast responses and would like to avoid heavy computation load on-the-fly. 2. Model adaptability Due to the nature of optimization-based approaches, systems being controlled need to be properly modeled into mathematical representations to be put in optimization problems. However, formulating accurate models in some domains might be labor-intensive: for instance, to model hundreds of houses in order to implement smart home control or to model all intersections in the city for intelligent traffic light

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control. In contrast, RL does not place constraints on system models and a controller can be trained on models that do not even have explicit mathematical expressions. As a result, many data-driven models in the form of various supervised learning models can be used for RL training. These models are much cheaper to obtain and the acquisition can be fully automated, especially considering massive deployment of optimal controllers. 3. Handling nonlinearity Besides using a powerful computer to increase the efficiency of solving optimization problems, an alternative that is frequently used in real-life applications is model simplification. Though transforming the original problem to a similar, simpler problem (e.g., linear programming) can reduce the computing time, the simplification might lead to a suboptimal control performance on a real system. In contrast, RL can learn nonlinear control problem directly (nonlinearity from either controlled system or the reward structure). 4. Modeling uncertainty Many control problems involves uncertainty, on one hand, disregarding it and use deterministic optimization will inevitably cause suboptimal control, while on the other hand, leveraging stochastic programming requires explicit effort for estimating probability distribution. In contrast, RL can directly learn from data about the uncertainty in an implicit way, making considering uncertainty in control a much easier effort (which might be valuable in some applications). Of course, whether RL can achieve or even exceed the performance of a stochastic programmingbased controller regarding the control performance needs further investigation.

9.3.2 Training a reinforcement learning controller 9.3.2.1 Typical workflow Though training an RL controller on an actual system is possible, it is very unlikely to do so in reality in many cases for two reasons: first, at the beginning of the learning phase, an RL controller needs to adequately explore a variety of actions and some of them might be dangerous for a real system (e.g., testing different and random control signals in a normally operating power grid); second, sample efficiency in RL learning is limited to real-time while experience collection with software simulations plus parallel learning is much faster (e.g., it might take 3 years in real life to collect enough experience for an RL controller to out-compete a rule-based controller [43]). As a result, in most cases,

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RL controllers are trained in a simulated environment first until there is a welltrained policy, and only then will it be deployed on a real system for continual learning and controlling. Admittedly, in areas other than engineering, where simulators are not as common and easily accessible or simply because wrong decisions during exploration are not as harmful, directly learning on the actual system might not be as undesired (design an RL-based recommender system [44]). Fig. 9.5 shows the typical workflow for training an RL controller for an actual physical control problem. First, starting from upper left corner, the control subject is modeled in a software simulation. Then, the system simulator is wrapped by the OpenAI Gym framework [45] (introduced in detail in the next part). Next, by utilizing a proper RL algorithm, an RL controller is trained based on the simulator wrapped in a gym environment. Finally, after adequate examination and testing, the trained controller is deployed to the actual system for control and continual learning. 9.3.2.2 Building a reinforcement learning environment In this section, how to design an RL learning environment for specific control problems is explained. By reading this part, readers should be able to tell the basic components of an RL learning environment and understand how to design and build one. For the purpose of efficient benchmarking, increasing code reusability, and facilitating domain researches, the RL community has standardized an interfacing framework, the OpenAI Gym [45], for RL algorithms to collect control experiences from a simulator and learn to have a better performance. Such an interface, which defines several key function calls and

Figure 9.5 Typical workflow for using RL for a real-world control problem. RL, Reinforcement learning.

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corresponding data structures, largely accelerates the development needed by both algorithm developers and domain scientists who use the algorithm to solve a problem of interest. Fig. 9.6 shows the interaction between an RL agent and a specific domain problem written in the OpenAI Gym format (referred to as a gym environment hereinafter). Below are some discussions based on three aspects: (1) controlled subject simulator, (2) environment key attributes, and (3) interfacing functions. 1. Simulator: A gym environment wraps a simulator of the controlled subject, and the simulator determines the system dynamics and can realistically reflect the system next state given current state and RL agent’s action. Typically, the following three types of simulators are commonly used in the gym environment (i.e., the “core simulator” shown in Fig. 9.6). a. Simplified physics models: Some simple mathematical models that describe the system dynamics such as state-space model (which are commonly used in classic control), power flow equations for power system control, and building resistancecapacitance (RC) thermal models [46] for building engineering. Such simulators have fast computation and can

Figure 9.6 Interaction between an RL agent and a gym environment: (1) Agent calls the reset function to start a new control episode. (2) Episode initialized with exogenous data (e.g., for wind turbine yaw control problem, wind data of speed and direction for a period of time are sampled and loaded). (3) and (4) Control episode initialized and return the initial state so to the RL agent. (5) Agent implements control at each interval. (6) and (7) The controlled system updated, reward rt is evaluated and returned with the new state st11. Steps (5)(7) are repeated until this control episode is terminated and the agent will call the reset function again to start learning in a new episode. RL, Reinforcement learning.

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be easily scaled but are also comparatively less accurate and require domain expertise for the system identification. b. Domain-specific simulators: Unlike traditional optimization-based approaches, RL does not place any limitation on system model, which can be nonlinear, nonconvex, stochastic, and even timevariant. As a result, many use domain-specific, first principle simulators to gain high modeling accuracy (e.g., OpenDSS [47] for power system engineering and EnergyPlus [48] for building control). Of course, domain expertise is required to build such model case by case, and despite the capability for precise modeling, the underlying computation for some simulators is heavier, which affect learning efficiency directly or indirectly. In addition, learning scalability largely depend on the scalability of the simulator (e.g., unable to use scalable RL algorithms for learning acceleration if the simulator itself cannot/is hard to run in parallel). c. Data-driven models: Recently, with the development of machinelearning/deep-learning techniques and advanced sensing technologies, many data-driven models are proposed, aiming at combining fast computation, preserving modeling accuracy, and reducing modeling cost. The biggest advantage of this type of simulators based on data-driven models is their low modeling cost (since data-driven models can be learned in a fully automated manner, and thus this avoids labor modeling cost), which is of great importance in some applications that requires case by case modeling extensively. Another merit of the data-driven model is that it can adapt to model drift by periodically retrain the model. In summary, for a specific control problem, researchers need to identify the best simulator for their problems, considering the model accuracy, readiness, cost, and scalability. 2. Environment key attributes: There are four key attributes for a gym environment: action space, observation, reward structure, and termination condition. Proper design of these attributes is of great importance and will determine if an effective controller can be successfully trained. a. Action space defines the number and range of the control variable(s). Gym environment allows action space for both discrete and continuous actions. Action space needs to be declared in the gym class’s constructor function (i.e., the “_ _ init _ _” function in the gym class). This information is indispensable since RL algorithms require it to construct the outputs of the policy network/value network.

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Discussion A: Choosing an appropriate action space Imaging a single battery optimal control problem in which the power is being controlled. When formulating this problem into a gym environment, there are many choices for the action space: (1) discrete: a A {charging, discharging}, (2) continuous w/o constraints: a A [ 2 Pch, Pdis], and (3) continuous w/ ramping constraints: a A [ 2 ΔP, ΔP]. All three action spaces can be used to train a battery controller, but it is up to the researchers to determine which one suits the best for their specific control problem.

b. Observation/state space defines the number and range of the observable system state. In implementation the observation can either be a vector or a tensor (e.g., an image) or a combination of both. Unlike action space design, choosing an appropriate observation space is not as straightforward: researchers need to decide which parameters to be included, and the decision, in many cases, will impact the training results. Similarly, the observation space is defined in the gym class’s constructor function as well and will be used by RL algorithms when building the input of the policy network/value network. Discussion B: Making observation space inclusive Considering the battery control example in Discussion A, if we choose to use action space (3), it is of great importance to include current power output P into the observation space since the optimal action ΔP will P the RL controller needs to be aware of. In contrast, if action space (2) is used, P can be excluded. In summary, when designing the observation space, researchers need to include all possible information that may influence the optimal action and meanwhile try to avoid variables that cannot be observed or predicted, though their existence might help improve the controller performance.

c. Reward structure defines how the RL agent will be rewarded or penalized at each step given its action taken. Defining an appropriate reward is the key for solving a problem using RL: in general, the researcher needs to design a reward structure that perfectly aligned with the control objective. Reward can be dependent on exogenous data (e.g., energy cost is dependent on electricity price), current state (e.g., penalty if voltage being controlled breaches boundaries) or states of past few steps (e.g., penalizing improper use of a device: turning a device off before its minimum operating time).

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d. Termination condition defines when the control episode will be terminated. Typically, there are two choices: first, all control episodes terminate at maximum control step (i.e., step count is the only terminating criteria). Second, control episode terminates with some specific conditions. For instance, when training a controller to bring a faulty system back to normal (e.g., power system black start), the episode can terminate when normalcy is obtained, even before the maximum step allowed. 3. Two key interfacing functions: Two key functions are repeatedly called by the RL algorithms, “reset” and “step.” As mentioned in Section 9.1, the objective of the sequential optimal control is to maximize expected reward over a specific control horizon, called an episode. The “reset” function is called to start a new episode of simulation by reinitializing the environment, when an episode is terminated. The “step” function is called by an RL agent to implement a one-step control. It triggers the simulator inside the gym environment to simulate one step and return the reward and observation at the next step. reset( ) and step(a)

•

•

reset( ) does not take in any argument and it returns the initial observation of the system. For instance, in a gym environment related to battery optimal control problem. There will be a code snippet in the “reset( )” to reinitialize the battery SOC. step(a) contains the following four steps. First, preprocessing the action if it is normalized, for example, a 5 0.5 is given for the battery control problem, then the actual power output from battery is p 5 a 3 Pch . Second, pass the processed action into the simulator to simulate the dynamics of this control interval and receive the next state from the simulator and provide the action evaluation signal, namely, a scalar reward. Then, tick the step by plus one ðt’t 1 1Þ and finally determine if the episode is terminated by examining some termination conditions. The step function will return a tuple of (state, reward, if_terminated, info), in which “info” is a Python dictionary that either is empty (i.e., info 5 {}) or contains additional information.

9.3.2.3 Selecting the right algorithm With the gym environment for the specific control problem properly formulated, the next question to think about is which RL algorithm should be used given so many available options out there? Below are three perspectives for considerations:

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First, some algorithms have limitations: DQN and its variants can only be used for problems with discrete actions and DDPG-related algorithms only work for continuous control problems. As a result, algorithm should be chosen according to the nature of the specific problem. Second, some algorithms have been tested better than others in RL literature using some benchmark test cases; thus, consider them first is generally a good option. For example, as mentioned in Section 9.2.2, TRPO considers a KL divergence constraint during policy update and its performance is better than vanilla policy gradient (VPG) algorithm. Again, PPO overcomes the TRPO’s complicated implementation issue and achieves similar performance by using a clipped surrogate objective. Therefore training an RL controller using PPO might lead to more reliable and faster learning than using VPG or TRPO. Third, factors such as hardware availability or learning scalability should be taken into consideration as well: If GPU is not available for training, A3C can be considered since it can be parallelized on single machine with multicore CPU with performance similar or better than GPU-based algorithms in some cases [14]. In the case where scalability is important and the simulator run in parallel, evolutionary strategies RL (ES-RL) is preferred than PPO, since parallel workers in ES-RL only need to share a scalar [22], while workers in PPO need to share all gradient information, and this makes ES-RL learn faster than PPO from wall-time perspective.

9.3.3 Reinforcement learning applications in energy systems In this section, RL applications on several energy system topics are discussed, and some representative studies are presented. 9.3.3.1 Smart buildings According to data from the U.S. Energy Information Administration (EIA), in 2018, around 40% of total energy consumption in the United States are from buildings, both residential and commercial [49]. Proper building control is expected to maximize occupancy comfort, optimize building energy usage, and provide potential grid services to power system. Currently, the state-of-the-art intelligent building control are mostly based on model predictive control (MPC) [50], which optimize a specific control objective over a period of control horizon, while considering several operation constraints. Of course, successful stories of MPC in many domains have proven it to be an outstanding controller. However, when applying MPC for smart building/smart home control, there will be

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several practical issues [51,52] as discussed earlier in Section 9.3.1: the requirement for on-demand computation and modeling effort required to obtain an accurate but simple mathematical model for the controlled subject. In contrast, RL, as an alternative approach for sequential optimal control, does not have these issues and can be a potential powerful candidate for building control, especially considering massive deployment in many buildings. First, as introduced in Section 9.3.2, RL controller can be trained offline before it is deployed in a real building, and only limited computation is required for the policy evaluation during actual control. Thus an RL controller can be implemented using cost-effective embedded system to reduce implementation cost. Second, RL controllers do not place any constraint on the format of building models, and these models do not necessarily need to have an explicit mathematical formulation. As a result, RL can adapt to more model types, including some data-driven building models, which are accurate but much cheaper to obtain. In all, RL controllers seems to be very promising to solve smart building control problems in a low-cost but effective way and thus inspired many research. Before 2015, most building RL research uses value-based RL (mostly Q-learning and some other TD learning). For instance, in as early as 1997, Anderson et al. [53] started using Q-learning for a heat coil temperature set point tracking task. Henze and Schoenmann [54] investigated using tabular Q-learning for building thermal energy storage system control under the influence of time-variant electricity price (i.e., time-of-use price and real-time price) in a simulated environment. Dalamagkidis et al. [55] uses TD(λ) with linear function approximation to solve a building optimal control problem that minimizes the energy consumption for achieving indoor comfort. In these studies, experiments show that RL controller can achieve good performance if the control task is relatively simple (e.g., in the set point tracking task [53]); however, if control problems are not as straightforward (e.g., when multiple control objectives are considered), RL controllers’ performance is not as good. For instance, it takes 4 simulated years for RL controller to approximate the performance of a Fuzzy-PD controller in Ref. [55] and the RL controller cannot match a predictive optimal controller in Ref. [54]. Admittedly, building control is a complicated problem and it might not be easy for tabular Q-learning or linear function approximated RL to learn. However, as of today, will the DRL be a game changer? Many

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researchers have started their investigation on how DRL can be used for modern building control. For example, Mocanu et al. [56] leverage deep Q-learning and deep policy gradient to perform online scheduling for building energy management system and showed that they can outperform a tabular Q-learning baseline (an expected result). In order to achieve an overall optimum and encourage device coordination, Ding et al. [57] tested a DRL-based holistic building controller in simulation, with a goal to optimize the trade-off between four different building subsystems. Besides these two examples, there are many other studies which focus on this emerging study, and the interested readers can refer to Ref. [58] for a more comprehensive and up-to-date literature review. According to Ref. [58], although as of today, the most used DRL algorithm in building control research is DQN, and more studies are expected to test and compare other algorithms including actorcritic methods, as researchers gradually move from discrete control to more accurate continuous control. Future studies should consider two questions before an RL controller can be widely used for building control in real-life applications: 1. What simulator should be used for building RL controller training? As discussed earlier, directly training building RL controller in a real building environment suffers from low sample efficiency (i.e., real-time) and might cause building operation issue. In this case, what simulator should be used is a good question. To fully take advantage of RL’s capability of handling nonlinear system, Wei et al. [59] utilized the high fidelity EnergyPlus model to train a control policy for the building air-conditioning system. However, considering that such a model might be costly to obtain, using a crude EnergyPlus model composed during the building design phase to pretrain an RL agent and then deploy it in a real building for continuing learning is proposed in Ref. [60]. But this still does not work for existing buildings, which accounts for the majority, and not all newly constructed buildings have an EnergyPlus model during planning. As a result, Zhang et al. [61] point out by deploying low-cost Internet of Things (IoT) devices and leveraging machine-learning methods, data-driven building models can be automatically learned from building operation data, and thus accurate building specific model can be obtained with much lower cost. With more studies and field tests on data-driven building models, using these models for building RL controller training is a practical and promising solution for future real-life applications.

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2. How to widely deploy RL controllers in buildings with low cost Intelligent building control is not only beneficial to both building owners but also favorable to the power system as it provides responsive loads to the grid. The advantage grows with the number of smart buildings. Reducing the cost and increasing the return-on-investment is important to encourage more building owners to commission such a control system. As discussed earlier, RL controllers do not require an on-site powerful computer to solve optimization problems (reduces both hardware and software cost) and can also use a data-driven building model for training (reduces the modeling cost). Another approach worth future research study is transfer learning, which transfer the knowledge from an already trained controller to a new controller for another but similar building. Typically, the transfer is done by warmstart the NN training by loading parameters from another controller’s NN. By doing this the training time needed for the new controller is decreased (comparing with training the controller from scratch) and thus the training cost is further reduced. But it is worth noting that transfer learning only works with similar buildings (at least with the same state and action space); to this end, there should be some studies that investigate how to quantitatively evaluate the similarity between buildings and how the similarity is related to the decrease of training time when transfer learning is applied. 9.3.3.2 Demand response Demand response (DR) is to encourage electricity customers to change their power consumption in certain periods in response to a signal received from utility company and in exchange of financial benefits [62]. With proper implementation and adequate customer adoption rate, DR can have a vital role in improving power system reliability and reducing the risk for having major blackouts. As a result, DR is considered to be a significant component of future smart grid. Usually, before and during DR events, DR program organizers and participants need to make sequential optimal decisions of their own. For instance, on one hand, utility companies, as decision makers, need to dynamically determine the real-time price over a period of time, considering the wholesale price, weather, and customer’s behavior, in order to properly guide the end users for load reduction, while satisfying other objectives and constraints. On the other hand, customers are also decision makers and need to optimally operate their houses, businesses, or

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industrial plants during the DR events to maximize benefit, while minimizing the cost from discomfort or production discontinuance. And in many cases, uncertainty is involved in such decision-making processes (e.g., price uncertainty for customers and the uncertainty of customer behavior for utilities). Therefore many studies investigate using RL for these decision-making processes. We will look into some examples using a bottom-to-top sequence, namely, DR problems at the device level, the customer level, the aggregator level, and finally the utility level. First, device level DR control problems are the most well-studied ones among the four-level problems mentioned above, and they account for the most number of publications as well. In the literature, according to this review paper [63], devices being control during DR events are typically of two types: (1) thermostatically controlled devices (TCL, i.e., airconditioning systems and water heaters) and (2) storage-based devices (e.g., batteries and electric vehicles). These two types of devices introduce strong temporal dependency to those control problems and thus made them suitable for RL to solve. Typical control problem solved in these studies is to minimize electricity cost while satisfying some operation constraints, considering a deterministic or stochastic time-variant price signal (e.g., real-time price in price-based DR programs). To add more complexity, uncertainty from user behavior or local renewable generation can be considered. For instance, an RL controller based on fitted Q-iteration is proposed in Ref. [64] to help water heaters to participate in DR programs. Two highlights are presented in this paper: (1) an auto-encoder network is used for feature dimension reduction which helps RL for faster convergence and (2) a lab experiment is conducted using an actual water heater and validated the proposed method. For a similar problem the authors of Ref. [65] solve it using a model-based RL approach. Second, the difference between problems at the customer level and those at the device level is the involvement of more devices and potentially other customers. Thus there is a higher requirement for those controllers aiming at solving customer level problems since they need to control multiple devices of a single or multiple customer(s) in a coordinated manner (increased complexity when compared with single device control). Bahrami et al. [66] propose an online appliance scheduler based on an actorcritic RL method to reduce both cost and peak demand for multiple residential customers without knowing the electricity price ahead of time. Another example of customer level RL controller for DR programs can be found in Ref. [67], where a fully automated energy management system is proposed.

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At the third level, some studies investigate how to use RL to properly control customers’ devices at the aggregator level (i.e., optimal control for device clusters). Nowadays, to facilitate customers to participate in DR programs, many aggregators emerge, thanks to the development in the information and communication technologies (especially the IoT). Some aggregators are device manufacturer themselves (e.g., smart thermostats from Google Nest and Honeywell) while some others just support BYOD (Bring Your Own Devices) aggregation. During a DR event the aggregator needs to optimally control a large amount of heterogeneous devices to achieve the goal of load reduction, while meeting the operating constraints of each enrolled customer. One major difficulty is that the system dynamics and operating constraints are not known exactly. In Ref. [68] the authors propose a control scheme for managing a water heater cluster using a model free batch RL algorithm. A simulation on a water heater cluster of 100 devices shows that after 4045 days, the controller is able to learn to reduce the daily electricity usage. Finally, at the utility level, a dynamic pricing mechanism should be properly devised in order to guide price-responsive loads to adjust power consumption during grid peak hours. Problems at this level are in general not as well studied when compared with problems from previous three levels. The main reason is the modeling complexity: in previous problems on customer loads control at three levels, hypothetical price signals can be used to model the DR market, and they can be generated to train the corresponding controller; in contrast, when training the dynamic pricing scheme, customer models, and responding dynamics needs to be modeled, which is harder than price generation. Although there are studies [6971] that propose RL-based decision-making framework that do not assume the availability of users’ response functions, these approaches of directly learning customer’s response by trial-and-error on the actual system might not be practical to be used in real-life applications. 9.3.3.3 Grid operation According to Ref. [72], power system condition can be described by five operating states: normal, alert, emergency, in extremis, and restorative state. In this section, some examples using RL to facilitate the decisionmaking process in some of these states or during states transition are discussed. First, during system normal operation, due to the increasing penetration of renewable generation and electric vehicles at the user end, grid

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control is challenged in many ways. For better voltage stability, Duan et al. [73] propose using RL to train a controller for autonomous voltage control: by using real-time measurements from the supervisory control and data acquisition system or wide area measurement system, the trained RL controller can support grid operators with effective and timely control actions. In Ref. [74] a two-timescale voltage regulation scheme based on RL is proposed to coordinate smart inverters on a faster timescale and shunt capacitors on a slower timescale. Similarly, Xu et al. [75] use RL to set the optimal tap position for voltage regulation transformers. In addition to the voltage regulation, there are also works on the frequency regulation: as early as 2002, Imthias Ahamed et al. [76] use RL to design automatic generation control (AGC) systems, which limits area control error excursions or restore the balance between generation and load by monitoring the deviation in system frequency and tie line power flows. Later in Ref. [77], RL and artificial emotion is combined to solve the AGC control problem. Second, when the system is under the alert state (e.g., under N 2 1 or N 2 2 contingencies), grid operators might need to take adequate actions to keep the system operating in the stable region. For instance, the 2003 north American blackout was initiated by a series of line outages due to overloading [78]; to prevent similar cascading failure events, Zarrabian et al. [79] proposed using Q-learning to implement a real-time optimal control (adjusting generators’ output power) to relieve congestion of transmission lines and thus prevent cascading line outages and blackouts. Another important aspect of blackout prevention is to conduct vulnerability analysis; a Q-learningbased scheme is designed in Ref. [80] to identify the most devastating sequential topological attacks. Similarly, Zhang et al. [81] propose an online search method to precisely identify “representative risky fault chains” and use such information for further fault blocking control. Third, emergency control is of great importance as a successful control can stop system slide to the direction of system failure/in extremis state. At the emergency state, grid operators/automated controllers are required to take a sequence of fast, correct, and coordinated actions to mitigate/ minimize the damage. As a result, an RL controller, due to its real-time action readiness, is a great candidate for such type of control. In Ref. [82], RL-based control schemes for generator dynamic braking and undervoltage load shedding are investigated. Rapid system restoration after failure is also of great importance to improve system resiliency. Wu et al. [83] investigate an RL-based

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framework to identify the optimal restoration sequence after a cascading failure. RL should also be suitable to solve power system black-start problems, especially if considering using renewable energy sources as blackstart units. However, no RL-based black-start sequence designing tool has been investigated in the literature at the time of writing. 9.3.3.4 Renewable generation and battery control Renewable generation, due to the nature of the intermittent primary energy, also involves stochastic optimal control. One example is the wind turbine yaw control: to maximize power output, wind turbine yaw system needs to track the wind direction and adjust the turbine orientation accordingly. However, in some cases, greedily chasing the volatile wind direction might not be an optimal strategy because yawing is not instantaneous due to the mechanical process involved: by the time turbine turns to a desired direction, the optimal direction has already changed. As a result, some studies use RL to learn from wind historical data about the optimal yaw control strategy that yields the maximum power output over a period of time. Graf et al. [84] combined RL with alternating direction method of multipliers (ADMA!) and proposed an ADMMRL algorithm that can be used for wind turbine yaw control. In Ref. [85] a knowledge-assisted DDPG algorithm is proposed for cooperative wind farm control; the proposed algorithm uses prior engineering knowledge to ensure safety during learning as well as accelerating the learning process. An RL-based maximum power point tracking (MPPT) control is proposed in Ref. [86] to determine the optimal rotor speed. Grid-connected battery control is often formulated as an RL control problem. This is because battery introduces strong temporal dependency in the controlled system and as mentioned earlier the temporal dependency is indispensable for an RL problem. Typical battery-related RL problems also include a source of uncertainty: for example, how to optimally charging and discharging the battery to minimize the grid electricity usage under stochastic PV generation or random real-time price. In Refs. [8789], battery system is leverage to participate in energy arbitrage market in order to maximize financial gain by buying and selling energy. Similarly, optimal decisions are made for EV charging in order to minimize energy cost [90].

9.3.4 Some interesting research topics Although we have seen that engineers started using RL to solve some real-life engineering problems, there are still some pending and interesting

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research topics that need to be addressed. Several examples of these topics are discussed in the following: Proper RL/MDP formulation: To successfully leverage RL to solve realworld engineering problems, properly formulating the MDP is of great importance. But for some problems, the definition of MDP state space is not as straightforward: for instance, to design an RL controller for grid control, the best way to define the state space that can accurately reflect the network topology, power flow distribution and device status is not clear. Similarly, some research efforts are needed to study and summarize the best reward structures for some typical engineering problems (e.g., signal tracking/set point following and cost minimization) that helps them for a more effective learning and faster convergence. Controller training and evaluation: An RL policy helps achieve the maximum expected reward under certain distribution. For example, that means an RL wind turbine yaw controller trained with winter wind data might not be optimal for control in summer due to the different distributions of wind speed and direction in these two seasons. Therefore there should be a quantitative evaluation on how to design the training environment that shares the same distribution with the actual environment this controller will be used in. Following the same example, to train an RL-based turbine yaw controller to be used in August, an automated and quantitative analysis should help the researcher to choose which period of wind data should be used for training. Combining prior knowledge with RL: An RL agent typically learns from scratch by trial-and-error. However, engineers and scientists usually have deep understanding (i.e., prior knowledge) about the system to be controlled. How can the prior knowledge to be leveraged to help improve the learning efficiency, guarantee a safer exploration, and increase RL controller’s interpretability is an interesting research topic. Transfer learning: Instead of learning from scratch, an RL agent can take advantage of a previously trained RL controller so that the training time can be largely reduced. This is very helpful in applications such as wind farms turbine control and building optimal control, in which there are many different but similar instances that require RL controllers for the same purpose. The transfer learning can help an efficient and scalable deployment of RL controllers in a great number of instances. But research is necessary to quantify the similarity between two control scenarios and determine if knowledge is transferable between them.

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Multiagent RL: Oftentimes, there are multiple decision makers involved in a single engineering problem, and they worked either in a cooperative way or a competitive manner. Multiagent RL is suitable for such scenario; however, the training is much harder due to the nonstationary environment (i.e., for one agent, the other agents are part of the environment, and due to the learning process of other agents, the environment to this agent is constantly changing). Research is needed to investigate the effectiveness and stability of such learning framework.

9.3.5 Limitations and challenges Admittedly, in addition to the potential advantages that RL might bring in engineering, using RL might also suffer from some limitations and challenges, which are listed below to help the readers to gain a more comprehensive understanding: 1. In RL formulation, forming strict constraints (equality or inequality) is not as convenient as the traditional optimization approach. The workaround is to add large penalty if a constraint is violated. However, if the number of constraints is large, this workaround is not a good solution. 2. The behavior of an RL agent cannot be quantitatively described upon the completion of RL training. To be specific, when using chance constraint stochastic optimization to solve a problem, some risks can be quantitatively limited, but for RL, such information is absent. 3. A trained RL controller only provides optimal control performance if the actual environment is used for stays the same as the one it is trained on. That is to say, an RL controller needs to be retrained once the environment has changed. 4. Because RL optimizes to achieve the maximum expected reward given the distribution of some environment’s parameters (e.g., distribution of exogenous data such as outdoor temperature), as a result, its performance on some rare events (e.g., extreme hot days) might not be optimal.

9.3.6 Summary In this section, we first discussed about some potential advantages of using RL for engineering problems, comparing with existing approaches. Then, the typical workflow to build an RL training environment is explained and demonstrated. Next, some examples for using RL in different

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domains are presented to show some existing efforts in this area. Finally, some potential interesting research topics and the limitations for using RL are also discussed. In all, we hope by reading this chapter, readers can have a better understanding on (1) the basic concepts of RL, (2) its potential to solve the engineering problems, (3) what types of problems are suitable for RL to solve, (4) how to use RL to solve real-life applications, and (5) what still need to be done in this area.

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[77] L. Yin, T. Yu, L. Zhou, L. Huang, X. Zhang, B. Zheng, Artificial emotional reinforcement learning for automatic generation control of large-scale interconnected power grids, IET Gener. Transm. Distrib. 11 (9) (2017) 23052313. [78] G. Andersson, P. Donalek, R. Farmer, N. Hatziargyriou, I. Kamwa, P. Kundur, et al., Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance, IEEE Trans. Power Syst. 20 (4) (2005) 19221928. [79] S. Zarrabian, R. Belkacemi, A.A. Babalola, Reinforcement learning approach for congestion management and cascading failure prevention with experimental application, Electr. Power Syst. Res. 141 (2016) 179190. [80] J. Yan, H. He, X. Zhong, Y. Tang, Q-learning-based vulnerability analysis of smart grid against sequential topology attacks, IEEE Trans. Inf. Forensics Security 12 (1) (2016) 200210. [81] Z. Zhang, R. Yao, S. Huang, Y. Chen, S. Mei, K. Sun, An online search method for representative risky fault chains based on reinforcement learning and knowledge transfer, IEEE Trans. Power Syst. 35 (3) (2019) 18561867. [82] Q. Huang, R. Huang, W. Hao, J. Tan, R. Fan, Z. Huang, Adaptive power system emergency control using deep reinforcement learning, IEEE Trans. Smart Grid 11 (2) (2019) 11711182. [83] J. Wu, B. Fang, J. Fang, X. Chen, K.T. Chi, Sequential topology recovery of complex power systems based on reinforcement learning, Phys. A: Stat. Mech. Appl. 535 (2019) 122487. [84] P. Graf, J. Annoni, C. Bay, D. Biagioni, D. Sigler, M. Lunacek, et al., Distributed reinforcement learning with ADMM-RL, in: 2019 American Control Conference (ACC), IEEE, 2019, pp. 41594166. [85] H. Zhao, J. Zhao, J. Qiu, G. Liang, Z.Y. Dong, Cooperative wind farm control with deep reinforcement learning and knowledge assisted learning, IEEE Trans. Ind. Inform. 16 (11) (2020). [86] C. Wei, Z. Zhang, W. Qiao, L. Qu, An adaptive network-based reinforcement learning method for MPPT control of PMSG wind energy conversion systems, IEEE Trans. Power Electron. 31 (11) (2016) 78377848. [87] J. Cao, D. Harrold, Z. Fan, T. Morstyn, D. Healey, K. Li, Deep reinforcement learning based energy storage arbitrage with accurate lithium-ion battery degradation model, IEEE Trans. Smart Grid 11 (5) (2020). [88] H. Xu, X. Li, X. Zhang, J. Zhang. Arbitrage of energy storage in electricity markets with deep reinforcement learning, arXiv preprint arXiv:190Jh 12232, 2019. [89] H. Wang, B. Zhang. Energy storage arbitrage in real-time markets via reinforcement learning, in: 2018 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2018, pp. 15. [90] A. Chi¸s, J. Lundén, V. Koivunen, Reinforcement learning-based plug-in electric vehicle charging with forecasted price, IEEE Trans. Veh. Technol. 66 (5) (2016) 36743684.

CHAPTER TEN

Power, buildings, and other critical networks: Integrated multisystem operation Kyri Baker University of Colorado Boulder, Boulder, CO, United States

Contents 10.1 Introduction 10.1.1 Aging infrastructure and climate-related impacts 10.1.2 Increasing electrification in the built environment 10.1.3 Increasing connectivity in power, water, and gas networks 10.2 Grid-interactive buildings 10.2.1 Distributed energy resources 10.2.2 Demand response 10.2.3 Emerging considerations for demand response 10.2.4 Climate and environment 10.2.5 Cybersecurity and privacy 10.3 Interdependent critical networks 10.3.1 Water and energy 10.3.2 Power and gas 10.3.3 Combined heat and power 10.4 Electrification of the transportation sector 10.4.1 Consumer vehicles 10.4.2 Public transportation 10.4.3 Rideshare services and emerging methods of transportation 10.4.4 Vehicle-to-grid 10.5 Considerations for future power systems 10.5.1 Physical considerations 10.5.2 Market and organizational considerations 10.5.3 Cyber considerations Acknowledgments References

New Technologies for Power System Operation and Analysis. DOI: https://doi.org/10.1016/B978-0-12-820168-8.00009-2

© 2021 Elsevier Inc. All rights reserved.

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10.1 Introduction Electrification is considered the greatest engineering achievement of the 20th century by the National Academy of Engineering and is an essential component of many parts of the world today. Without electricity, water pumping stations would not be able to deliver drinking water to a community; electricity-powered gas compressor stations could not provide natural gas for heating and cooking; cell towers could not receive and transmit signals; subway cars would cease to move, potentially stranding commuters in a dark underground tunnel without power for ventilation. Power, and reliable access to the electric power grid, is essential for the operation of critical networks such as water, gas, telecommunication, and transportation networks, and for ensuring adequate shelter, heating, cooling, and medical facilities. However, multiple emerging factors are challenging the ability of the grid to reliably deliver power to these critical infrastructure sectors. Due to increased electrification at the building level, low-voltage distribution networks may face challenges in the future without increases in distribution capacity or distributed energy resources (DERs). In addition, the effects of extreme weather and climate change have impacted the grid’s ability to reliably deliver power, with weatherrelated blackouts increasing in frequency in recent decades. Many of the recent developments in the electric power grid that are affected by these changes occur in the low-voltage, distribution networks that provide pathways for consumers to receive and, in some cases, deliver power to the main grid; however, changes in distribution networks are affecting high-voltage networks much more rapidly than grid operators had originally anticipated. In this chapter, we will discuss the impact of these changes in the distribution network and other interdependent networks, such as within buildings, water, gas, and communication networks. In particular, we will address the questions: How will the increasing interconnectedness of critical infrastructure sectors impact the power sector? and What are some of the needs of future power systems that will help us adapt to and be cognizant of these challenges? Toward this, we first take a look at some of the factors that continue to impact and influence the operation of the grid and its dependent networks.

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10.1.1 Aging infrastructure and climate-related impacts The American Society of Civil Engineers (ASCE) stated in their 2017 report, “Without greater attention to aging equipment, capacity bottlenecks, and increased demand, as well as increasing storm and climate impacts, Americans will likely experience longer and more frequent power interruptions.” From the 1950s to the 1980s, large-scale power outages in the grid were rare, averaging roughly five per year or less [1]. However, due to aging grid components and the growing uncertainty in load, generation, and weather, the grid has been increasingly stressed: from 2003 to 2012, roughly 679 outages occurred due to weather, with severe weather being the leading cause of outages in the United States. The Department of Energy estimates that the average cost to the US economy during this time period due to weather-related outages is $18 $33 billion [2], with numbers even higher during years with major storms such as Superstorm Sandy in 2012 ($27 $52 billion). Due to the reliance of many other critical networks (water, gas, transportation, communications, emergency services, and more) on electricity, the loss of this asset can prove detrimental to components beyond the immediate grid, putting lives at risk. Building loads and peak demands are also expected to increase during certain seasons, resulting in increased stress in power infrastructure [3]. It is important for grid planners and operators to be aware of the impacts on the power grid that go beyond just the power grid itself and design plans for grid resiliency that prioritize and ensure power delivery to critical networks and loads, coordinating dependent networks when necessary to prevent power delivery issues. Recent research works focus on the immediate impact of climate-related disasters and extreme weather events on the grid, offering frameworks for grid resiliency upgrades to preserve the operation of these critical networks [4,5]. In Ref. [4] the focus is placed on improving the resiliency of distribution networks to extreme weather, which are often impacted more heavily than transmission networks from these events. They consider four types of upgrades to improve resiliency: (1) hardening existing power lines (e.g., replacing power poles with stronger ones and adding guy wires, which is generally much cheaper than burying lines underground); (2) building new lines, adding redundancy in the network; (3) installing controllable devices that add operational flexibility, such as switches; and (4) adding more DERs, which reduces reliance on power provided by the main grid. Some public

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Table 10.1 Average lifespan and capacity-weighted age of coal, natural gas, and nuclear fueled operating power plants in the United States. Generator fuel type

Average lifespan (years)

Capacity-weighted average age (years)

Coal Natural gas Nuclear

50 60 40 50 30 40

39 22 36

Source: Data from EIA and A. Mills, R. Wiser, J. Seel, Power Plant Retirements: Trends and Possible Drivers, Lawrence Berkeley National Laboratory, Nov. 2017 [9].

service commissions, utilities, and system operators have recognized the impact of climate change and extreme weather events on the operation of the grid. For example, Consolidated Edison in New York has included climate predictions into their grid planning models [6]. These impacts are compounded by the fact that most power system infrastructure, including lines, transformers, and generators, are becoming less efficient with age, and in some cases nearing their end-of-life. According to Federal Energy Regulatory Commission (FERC), distribution utilities across the country continue to invest thousands of millions of dollars a year in distribution infrastructure, with this number increasing over time. However, due to a lack of visibility and sensor networks within distribution networks, equipment is often oversized, or sized for worst case scenarios. Despite these efforts in 2017, ASCE gave the US’s energy infrastructure the grade of a D 1 , indicating our infrastructure is “poor, at risk” and “requires attention.” This is not surprising, as 70% of transmission lines and transformers are over 25 years old [7]; wooden power poles tend to start to rot around 40 years old, depending on where they are located; steel poles and transformers corrode over time; and distribution and transmission lines are given roughly a 50-year expectancy [8] (but transmission lines can last up to 100 years, if kept well maintained). Power generation sources are also aging. In Table 10.1 the average lifespan of coal, natural gas, and nuclear plants in the United States is shown along with the current (as of 2017) capacity-weighted average age of these plants. It is evident that other than natural gas plants, which have experienced a growth in recent years, many traditional forms of generation are beginning to age.

10.1.2 Increasing electrification in the built environment Many aspects of the built environment, that is, the human-made structures and networks that surround us, are trending from other energy

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sources to the use of electrical energy. Electrification has many benefits: improved demand-side capabilities to provide grid services, air quality improvements, greenhouse gas reductions, and more. Adoption of heat pumps for water heating and space heating and cooling can also result in increase in the efficiency of heating and cooling systems within homes. If the full electrification potential in transportation, industrial processes, and buildings sectors is achieved, a potential 72% reduction in fossil fuel combustion for these end uses could be achieved by 2050 [10], with the transportation sector accounting for the majority of this reduction. Some processes within the industry and buildings sectors face additional challenges; some processes such as cement manufacturing require high temperatures that are difficult to achieve with electric sources of heat alone, and consumer acceptance may hinder the adoption of electric stoves, for instance. The electrification of the transportation sector is occurring in a rapid fashion, with the adoption of electric vehicles (EVs) increasing dramatically worldwide. By 2030 the International Energy Agency (IEA) predicts that the number of EVs on the road will increase from roughly 3 to 125 million [11]. As another example, in Colorado, the percentage of EVs has increased by over 150% since 2011. As indicated in Fig. 10.1, the industry, buildings, and transportation sectors contain a wide array of electrification opportunities, from space and water heating to transit electrification. The transportation sector in particular currently has much room for electrification; according to the Energy Information Administration (EIA), in 2018, petroleum products accounted for approximately 92% of the total US transportation sector energy use. Electricity was less than 1% of the total energy use in this sector, despite transportation’s significant share of overall energy consumption, indicating there is significant potential for future electrification. Despite the benefits from electrification, strains on aging generation resources and power infrastructure are exacerbated by the resulting increases in electric loads. Within the United States the National Renewable Energy Laboratory estimates that the overall electricity consumption will grow from approximately 3860 TWh in 2016 to 5260 TWh in 2050. The use of electric heating in the winter would increase winter peak demands, spurring utilities and grid planners to plan for shifting load curves and both summer and winter peaks. However, the impacts are hard to measure exactly: energy efficiency improvements in both building construction and electrical appliances and grid-interactive loads and other demand-side management (DSM) strategies will influence

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Figure 10.1 Current opportunities for electrification in the industry, buildings, and transportation sectors, along with the distribution of end use energy and electricity in these sectors. Source: Data from the EIA Monthly Energy Report.

the overall impact on both peak load and overall energy consumption from electrification. In this chapter, we will focus on the electrification of the transportation sector and the interaction between power and transportation, which has the highest potential of impacting the operation of the grid.

10.1.3 Increasing connectivity in power, water, and gas networks A distinguishing characteristic of the smart grid as compared to traditional power grids is the proliferation of sensor networks and increased wireless communication and control capabilities, with a two-way communication between the grid and consumers. With increase in phasor measurement units (PMUs) and machine-to-machine communication, the lack of physical wired communication infrastructure that has resulted in wireless communication is becoming ubiquitous throughout the smart grid. Increased visibility into distribution system operations, while not considered as important prior to the smart grid era, is now becoming essential to ensuring grid reliability and stability. PMUs, for instance, were typically only installed at the transmission level but are beginning to emerge at the distribution level, giving grid operators additional insight into the status of the low-voltage networks [12]. While wired connections are generally more reliable and secure and are far more costly than wireless networks.

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Various wireless communication technologies are currently used throughout the grid: ZigBee (e.g., smart meters), Bluetooth (e.g., connected appliances), cellular networks (e.g., Global System for Mobile Communications—GSM), and many more [13]. The link between communication networks and power networks enhances the capabilities of the smart grid to solicit demand response (DR) and provide situational awareness to grid operators. Despite the aging of infrastructure and conventional generation sources, rapid additions of distributed solar, storage, and smart thermostats, to name a few, are assisting these conventional controllable generation sources by providing additional resources for the grid. On the demand side, where grid interactivity is increasing with the deployment of DR and dynamic pricing programs, communication between behind-the-meter devices and appliances is essential to continued success. The electricity meter, invented in the late 19th century, made it possible for consumers to be billed for electricity. However, traditional analog electricity meters throughout the country are being rapidly replaced with digital meters to facilitate the efficiency of meter readings as well as to enable grid-responsive buildings. Automatic meter reading (AMR) systems, for instance, emerged in the mid-1980s and enable a one-way communication between building to grid through telephone protocols (both wired and wireless), radio frequency (RF, by far the most common), or power-line communication, where data is carried over existing power lines. Many residences in the United States today are still fitted with AMR meters. Advanced metering infrastructure (AMI), which is generally what many refer to when they mention “smart meters,” can communicate bidirectionally between building and grid on faster timescales, reducing costs from meter readings and enabling faster outage pinpointing and restoration. For example, smart meters helped grid operators in Houston, Texas locate power outages during Hurricane Harvey, fixing damages before power loss spread. While AMR infrastructure installations have stalled since the early 2010s, AMI installations continue to increase (Fig. 10.2), with many states recognizing the benefits of increased communication capabilities between buildings and the grid. AMI meters use both home area networks to communicate to connected devices within a building and wide area networks, generally over cellular networks or RF mesh networks, to communicate with energy providers. Typically, electricity consumption is recorded in the meter’s memory in 5-, 15-, 30-, or 60-minute intervals, with 15 minutes readings being

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Figure 10.2 Total installed electricity meters on residential and commercial buildings in the United States from 2008 to 2017. The increases in AMI infrastructure is promising for improved demand-side capabilities of providing grid services. AMI, Advanced metering infrastructure. Source: Data from the EIA.

one of the most common frequencies, and typically read by the utility anywhere from monthly to 15-minute intervals. XCEL Energy, for example, reads some electricity and natural gas meters every 15-minute in order to gather data to help predict system demand. Increased communication capabilities throughout the power and power-dependent networks are not limited to electrical devices, and AMI systems are gaining traction in smart natural gas, water, and even transportation networks. ABI Research estimates that the number of smart meters deployed throughout the world will reach 780 million for electricity, 150 million for gas, and 90 million for water by 2020, meaning that the control and monitoring of water, gas, and power networks will have increased capabilities of working together to improve overall system efficiency and resiliency. Smart water meters began emerging in the 2000s, and harness the two-way communication to inform infrastructure upgrades, improve billing accuracy, perform remote upgrades, and reduce or shut off water supply to customers who have not paid their bills on time. Unlike smart electricity meters, smart water meters are generally battery powered (see Fig. 10.3) as electrical infrastructure to these devices was initially not needed and do not receive power for transmission from the electric grid. This means that the batteries must be in a waterproof casing, have a long-lasting lifespan, and that the communication system cannot make transmissions with the utility too frequently without risking draining the battery. However, the use of wireless communications throughout the smart grid has its own suite of downsides. For example, the use of GSM

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Figure 10.3 The casing covering an AMR water meter in Boulder, CO, which allows water utilities to record consumption remotely, multiple times throughout the day. The battery supply (right) should last for over a decade under present day communication frequency. AMR, Automatic meter reading.

networks for smart grid communication is subject to the same pitfalls as the cellular networks themselves, such as being sensitive to network congestion during peak hours. ZigBee technology is versatile, low-power, and allows power, water, and gas utilities to send dynamic pricing signals and consumers to monitor their real-time usage but is susceptible to interference effects from other technologies such as Wi-Fi and Bluetooth [14]. Wireless communication also introduces increased risks over wired networks; cybersecurity risks abound [15 17], and cyberattacks targeted toward vulnerable devices within a network could have the potential of propagating throughout the network to other assets. Public perception about the dangers and health effects of smart meters is also a nonnegligible concern for water and energy utilities. Coordination between these systems, enabled by two-way communication technologies, will be discussed further on in this chapter.

10.2 Grid-interactive buildings In the United States, buildings consume roughly 40% of the total energy consumption and over 70% of the electricity generated. Traditionally, power flowed throughout distribution networks in a unidirectional fashion—from substation down to building electricity meters. However, with the increase in DERs, including distributed generation and storage, reverse power flows are now occurring, resulting in voltage issues [18] and wear-and-tear on transformers and other substation equipment [19].

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Despite these emerging issues, increases in DER penetration, when coupled with enhanced communication and control capabilities, have great potential of increasing the flexibility of distribution networks [20]. The communication capabilities of many smart electricity meters, for example, allow for the ease of information exchange between buildings and utility companies on granular timescales, helping utilities request services from buildings or pinpoint outages during a disaster. This opens up an array of valuable resources for both building owner and utility: automated, price-responsive building loads, better grid-level forecasts of consumption, heightened consumer awareness, and more. In particular, the term “building-to-grid” or “grid-interactive buildings” encapsulates this two-way communication structure that allows for the ease of communication from built environment to the electric power grid. Case study: 2017 solar eclipse On August 21, 2017 a total solar eclipse occurred that spanned the contiguous US grid operators, utilities, and other organizations invested significant amounts of time and money to determine how the eclipse would affect the operation of the power grid; the predicted generation drop would require rapidly ramping up dispatchable generation such as gas-fired power plants. The California Public Utilities Commission urged their consumers to reduce consumption by 3500 MW [21]. Smart thermostat manufacturer Nest also prepared for the effects of the solar eclipse. Customers that joined Nest’s Solar Eclipse Rush Hour program had their temperatures automatically adjusted during this time, lowering load by about 700 MW (roughly the equivalent of seven gas peaker plants [22]). The success of demand-side flexibility was evident; partially thanks to coordinating building loads with the grid, no eclipse-related outages occurred.

Increased interaction between buildings and the grid cannot be only beneficial for improving system efficiency but can also be a necessity during times of need to prevent blackouts or cascading failures. This is not a new concept: grid operators have been leveraging direct load control (DLC) opportunities from industrial facilities for decades, for instance, requesting load curtailment during times of high grid stress. Industrial facilities and large commercial buildings have also traditionally been subjected to demand charges, where an additional electricity charge is added to their bill based on the 15-minute period with their highest consumption throughout the month. This charge can be a significant portion of

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their overall bill, as the sizing of distribution lines and other equipment is often driven by the demand peaks of these large loads. Many other DR programs for residential building-to-grid have gained traction in recent years, which will be further discussed in this section.

10.2.1 Distributed energy resources DERs are assets such as rooftop solar, stationary energy storage, gridresponsive EVs, controllable heating, ventilation, and air conditioning (HVAC) systems, combined heat and power (CHP) plants, and other resources that are connected to low-voltage (distribution) grids and who usually have the potential of providing grid services. DERs are important for DR, which will be discussed in more detail in the following section. Distributed generation comprises one large aspect of DERs and refers to localized, low-voltage generation resources such as rooftop solar and small diesel generators. With DERs, large heat dissipation losses from delivering power generated by large, central power plants, down to end use loads, is significantly lowered due to more localized sources of energy. For example, Fig. 10.4 shows EIA’s estimation for total transmission and distribution losses in the United States as a percentage of overall power generated from 1950 to 2016. Note that for the past decade or so, transmission and distribution losses were around 6% 7%, which amounts to millions of kilowatt hour lost during power delivery. With increased proliferation of generation that is closer to the demand, some of these significant losses could be avoided. In addition to efficiency benefits, DERs can also improve system resiliency. Resilience in the context of power grids is usually defined as the

Figure 10.4 Estimated percent power losses due to transmission and distribution in the United States. Source: Data from the EIA.

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ability to withstand and rapidly recover from power outages and other grid contingencies [23]. When a natural disaster or failure in the transmission grid occurs, DERs can provide resources to communities during a time of need through local generation or energy storage. This differs from grid reliability, which refers to the ability of the power system to deliver power to meet the required load. This is not to say DERs do not improve system reliability—certainly, with a greater diversity of assets and geographically spread out resources, less reliance on transmission infrastructure can prove to be very helpful for reliably delivering power to loads.

10.2.2 Demand response Demand response is an important and essential component of modern power systems. Navigant research estimates that the revenue to residential, commercial, and industrial customers from DR will reach $1.3 billion by the year 2026 [24]. The FERC’s official definition of DR is “Changes in electric usage by end use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” Verbosity aside, DR can be defined as a term that refers to an end user changing their electricity consumption in response to a signal from the utility (usually with some monetary compensation). This was mentioned earlier with DLC of industrial facilities but expands far beyond the curtailment of large industrial loads. Broadly speaking, the different types of DR span two main categories, seen in Fig. 10.5: price-based programs and incentive-based programs. The availability of these programs varies widely by region, utility, and grid needs in that area. For example, utility company Commonwealth Edison is one of the only utilities in the United States to offer residential real-time pricing, which gives customers the option of having their electricity price vary hourly and reflect actual transmission-level marginal prices. Time-of-use (TOU) prices, on the other hand, are found throughout the country and offer users predictable prices across blocks of multiple hours, usually only changing seasonally with seasonal load variations. An example of a real TOU price from XCEL Energy is shown in Table 10.2 for the summer season, showing higher prices during times of peak system loading (2:00 4:00 p.m.) to incentivize users to shift when they consume power.

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Figure 10.5 Demand response can be considered in two categories: price-based and incentive-based. The availability of different programs varies depending on utility, geographical region, and load/building type.

Table 10.2 Summer time of use costs (opt-in) for residential consumers served by XCEL Energy. Last accessed 6/2019. XCEL Energy’s TOU rates

Off-peak price (9:00 p.m. to 9:00 a.m.)

Shoulder price (9:00 a.m. On-peak price to 2:00 p.m.) (6:00 p.m. (2:00 4:00 to 9:00 p.m.) p.m.)

Summer ($/kWh) (except holidays)

0.08

0.13

0.18

TOU, Time-of-use.

We pursue the balance between demand and generation in the grid at all times. Rather than having the grid handle generation capacity shortages, frequency fluctuations transmission congestion, and voltage control, why not let the demand side help? In some cases, this is more economical than installing additional capacity or turning on another power plant on the grid side—and, in some cases, there are limits to what the supply side can do, and the demand side must participate to avoid incurring outages. This was originally done with requesting load sheds at industrial facilities, but DR programs are rapidly emerging at the commercial and residential levels. Because the term “demand response” is so often confused with other concepts, a few examples are provided below to illustrate the concept of DR.

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Is it demand response? Determine if each of the following three examples are considered demand response, or something else. Replacing all of your light bulbs with energy-efficient LED lamps. This is not demand response. This can be considered as an instance of demand-side management (DSM), which is often used interchangeably with demand response but actually is a larger term encompassing both demand response and implementing measures to improve energy efficiency. Here, replacing your light bulbs with LED lamps is considered DSM, but not DR. Participating in Nest’s Rush Hour Rewards. The Rush Hour Rewards program states that “your energy company will reward you to reduce load on the electrical grid during Rush Hours (times when demand for energy is high) like a heat wave or cold snap.” In this case, you would be changing your heating/ cooling demand in response to a varying price signal from the utility, so this would be considered demand response. Enrolling in XCEL Energy’s Windsource Program. For an extra charge on your utility bill each month, you can have a portion (or all) of your electricity consumption generated from wind power. This is not demand response. This is considered a renewable energy credit (REC) and does not incentivize the consumer to change their electricity use in any way. Enrolling in FPL’s on call program. Florida Power and Light (FPL) will pay you to let them install switches on some of your appliances, such as your air conditioner, central heater, electric water heater, or pool pump. Then, they are allowed to turn off these appliances in times of need when the grid is heavily loaded. This does count as DR; you are allowing your electricity consumption to be changed in response to a grid signal.

Lawrence Berkeley National Laboratory has classified DR into four categories (from longer timescale services to faster timescale services): shape, shift, shed, and shimmy [25]. In addition to being catchy, these terms have provided a common reference when discussing potential DR services that loads can provide. Shape, for instance, refers to DR that reshapes load profiles through dynamic pricing or through behavioral campaigns, on the scale of days to years. This includes DSM strategies that improve energy efficiency. As one example, TOU rates, while held constant for multiple months, need to be updated across the years to reflect long-term changes in load. Shift, as the name suggests, refers to DR services that occur on the daily to hourly timescale, shifting loads to shave peaks, fill valleys, and better coincide with renewable generation. Overlapping somewhat but providing minute-level responses is Shed,

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which describes loads that can be curtailed when needed. This can happen during either emergency or contingency events within transmission or distribution networks or to relieve line congestion or make up for a lack of generation capacity. And finally, Shimmy refers to seconds to minutes level adjustments in load for frequency regulation and shortterm ramping. Shimmy can be achieved with energy storage charging/ discharging, advanced lighting controls, and variable frequency drives, for instance.

10.2.3 Emerging considerations for demand response If too many buildings or loads respond to DR requests, a “rebound effect” could be observed. Presently, due to lack of consumer awareness, participation/enrollment apathy due to the relative magnitude of electricity costs, and a shortage of connected devices and buildings, this is generally not a problem, but under a high penetration of demand-responsive loads [26], enough buildings can shift their electricity consumption through techniques such as precooling before high price periods that the resulting peak demand is even greater than without TOU pricing. During June 2016 in California, the demand within the Southern California Edison (SCE) territory was nearing all-time peak levels. SCE requested DR and received a very sudden and sharp 500 MW drop in load within 7 minutes [27]—more and more rapidly than they were expecting. Another emerging issue is that consumer perception and behavioral patterns stand in the way of large-scale successful deployment of DR. Consumers are not used to changing when they use energy, and often the savings are not significant enough for them to change their behavior. To address this, utilities are emphasizing other positive effects of enrolling in DR programs, such as sustainability and environmental impact, but a high level of automation [28] and “set it and forget it” strategies will prove useful for engaging consumers in DR programs. Lastly, equipment lifetime and maintenance can be impacted by participating in DR. With particular focus on providing ancillary services, loads can be rapidly turned on or off and cycled, which is likely different than the original design specifications for those loads. This could incur early replacement or increased maintenance frequency costs; however, there may be financial benefits from proving DR services, and a full life cycle cost analysis must be performed to determine the overall impact of participating in DR [29]. Long-term planning strategies must also be taken into account by utilities and grid

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operators to ensure that effects such as the rebound effect do not impact the efficacy of DR as it becomes more widespread.

10.2.4 Climate and environment Extreme weather events, long-term changes in building loads, and other environmental factors are making it more challenging to reliably deliver power throughout the country. Between 2003 and 2012, weather accounted for 80% of all power outages in the United States; by 2012 the average annual weather-related power outages had doubled since 2003 [30]. In Table 10.3 the total number of severe weather-related major power outages in the United States between 2013 and 2018 is shown in addition to the number of customers affected in the case of the largest outage. Weather-related outages accounted for the majority of major outages in each of the considered years, and the severity of the worst case outages appears to be getting worse. However, large-scale outages are not always due to ice storms, hurricanes, or thunderstorms; sometimes, extreme temperatures are to blame. In 2003 a heat wave in the northeastern United States resulted in 50 million people losing power within a mere 8 minutes, and the lack of needed generation due to extreme cold weather left 32,000 customers in New Mexico and Texas without heating in 2011. Caused by a heat wave in 2007, the Brown’s Ferry nuclear plant in Alabama was forced to shut down due to high river water temperatures. There is a clear increasing trend in the outages due to climate and extreme weather events, and grid operators recognize this; for example, Hurricane Sandy resulted in 8.5 million people losing power across 21 states and spurred investments in grid resiliency upgrades. One of the US’s largest power outages occurred in 2017. On September 20, 2017, Hurricane Maria hit the island of Puerto Rico. By Table 10.3 Total number of major weather-related power outages and disturbances and number of customers affected in the largest outage. Year

2013

2014

2015

2016

2017

2018

Major weatherrelated outages Largest outage (no. customers affected)

40

77

51

39

62

80

400,000 715,000 500,000 1,200,000 3,500,000 4,200,000

Source: EIA Electric Power Monthly report, Table B.1. Courtesy of the author.

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8:00 p.m., nearly 100% residents were without power (with the exception of those with backup generators). The total damage to the territory exceeded the previous most costly hurricane by an order of magnitude. Six months after the hurricane, 7% of residents were still without power, and the resulting lack of basic services caused over a quarter million people to leave the island. Schools in rural areas were still without access to electricity; to cope, schools shifted class times earlier, shortening them to 4-hour days due to fuel costs for backup generation. Finally, 11 months after the hurricane occurred, power was fully restored on the island. As a result of this disaster, the perception of power and the grid has changed in Puerto Rico: many consumers have opted for off-grid solar and battery solutions, with innovative hurricane-resistant additions such as storm-shutter like protections for solar arrays and concrete pads to raise energy storage systems above ground level. The government has also set forth their own goals of power system resiliency; in March 2019 the Puerto Rico Energy Public Policy Act was passed—which will set the island on a path to 100% renewable energy by 2050. A general move toward microgrids for increased energy resiliency is recognized throughout the country in many communities, which challenges and redefines how we think of the grid as a whole. Other disasters, less commonly thought of as threats to power infrastructure, are also increasing in frequency. The recent increase in wildfires and heatwaves [31], for instance, may only seem like a threat to power infrastructure through burning power poles and substation fires, but one of the greatest risks arises from smoke and particulate matter, which can ionize the air and create unintended and dangerous electrical pathways for current to flow [32]. California utility PG&E rolled out a “wildfire mitigation plan” in early 2019, proposing to remove 375,000 additional trees, burying many existing power lines, and wrapping exposed wire in insulation. When high winds and low humidity conditions are present, the utility cuts power to customers in high-risk areas in attempts to prevent fires from starting. Looking outside of direct grid impacts from extreme weather and natural disasters, the impact of climate change and increasing global temperatures on building demand will continue to increase stress on the power grid [33 35], meaning that coordination between buildings and the grid and increases in localized resources will become even more essential to avoid further line congestion and transformer overloading. One study found that higher peak electricity demand as a result of climate

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change related temperature increases will require an additional 34 GW of generation capacity in the western United States alone by 2050, which is roughly equivalent to more than 100 new power plants [36]. Further, there are additional infrastructure upgrades that may be required for this increase in load, such as higher capacity power lines, or larger transformers. While in some areas of the country, the overall annual electricity demand may decrease over time [37], overall, electricity demand will increase, with peak summer demand increasing almost everywhere. The increases in peaks, driving costs of infrastructure, and generation upgrades further motivate increases in demand-responsive loads and heightened building-to-grid interaction.

10.2.5 Cybersecurity and privacy The number of devices connected to the internet is estimated to be 30 billion by 2020 [38], and the number of connected devices within commercial buildings and homes is increasing rapidly. While convenient to consumers and potentially relevant for providing grid services, many devices have hit the market before the corresponding cybersecurity measures have been thoroughly tested. For example, in 2014, 100,000 devices, including routers, televisions, and fridges, were taken over by a hacker and used to send spam emails. In 2016 a huge portion of the internet (including sites such as Netflix, Reddit, Spotify, and Twitter) experienced overloaded servers due to a distributed denial-of-service (DDoS) attack and shut down, resulting in a large outcry from consumers. The DDoS attack overwhelmed servers with requests from tens of millions of Wi-Fi connected smart devices. These devices usually have simple privacy interfaces and passwords and were turned into a botnet capable of repeatedly bombarding the servers of many popular website. AMR technologies, which a significant number of buildings use to communicate with the grid, do not communicate encrypted data. Using off the shelf tools, researchers were able to intercept AMR smart meter packets from up to 300 m away, fast enough to be able to determine the average power consumption of a house and start to deduce when someone is home [39]. Further, by using nonintrusive load monitoring, which takes an aggregate smart meter signal and disaggregates it into end use loads, even more private data can be recovered. For instance, researchers in Germany were able to uncover what television show a resident was watching from smart meter data and lighting energy patterns, stating

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“Our test results show that two 5-minute chunks of consecutive viewing without major interference by other appliances is sufficient to identify the content [40].” The Federal Bureau of Investigation (FBI) released a statement in 2010 in response to exploited smart meters in Puerto Rico, where meters were hacked to underreport consumption data, resulting in millions of dollars of revenue lost for the local utility [41]. The FBI stated, “as Smart Grid use continues to spread throughout the country, this type of fraud will also spread because of the ease of intrusion and the economic benefit to both the hacker and the electric consumer.” Further concerns with consumer privacy arise when considering that the data gleaned from smart meters could potentially be used for targeted advertising and data collection [42]. The concerns do not necessarily stop at passive invasions; however, connected appliances and electricity meters equipped with AMI, due to their two-way communication capabilities, could potentially have capabilities to remotely control smart appliances and devices within a building. On a grid-wide scale, intercepting smart meter signals and manipulating the actual power consumption can result in utilities receiving false information about energy usage, charging the consumer more or less than necessary, or in the long-term, determining planning and operational decisions that do not actually reflect the real state of the grid. Manipulation of the true energy consumption of a building, for example, can make system operators think a potential contingency situation might exist, or falsely lead them to change the operation of grid elements such as switches, capacitor banks, and transformers, threatening the security and resilience of grid operations. Due to the complexity and interconnectedness of the power grid, even a slight deviation from a stable operating state could cause small problems to affect large regions. The first large-scale power utility cyberattack caused widespread blackouts in Ukraine in 2016. The Russian hacker group “Sandworm” remotely opened breaker switches at substations, which had to be manually closed. In one case, they took over a remote desktop help tool that grid operators used and hijacked the computer mouse of grid engineers, clicking through circuit breakers and turning them off. In 2017 the US power grid was also subject to a cyberattack; a group called Dragonfly 2.0 targeted power companies, gaining access to grid controls, including circuit breakers which have the potential of causing a widespread blackout. Researchers conjecture that the group did not exercise any grid actions during this time, instead exploring the capabilities of potential attacks and possibly waiting for a time that would be more

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strategic. Efforts to mitigate grid security risks are underway and an active area of research within industry, academia, and government labs.

10.3 Interdependent critical networks As power engineers, it is important to consider the grid not as an isolated system but as an interconnection and pathway spanning multiple energy systems. Holding myopic views of energy networks when designing operational strategies or planning for power grid upgrades can result in overall system inefficiencies and unexpected behavior. In addition to the buildings and transportation sector, many other energy-dependent networks and systems, such as water and natural gas networks, are highly interconnected with the power system. Concepts such as energy hubs [43] aim to address the optimal control of these multienergy systems, providing multiobjective considerations and joint control decisions that benefit the overall system from economic and environmental perspectives. However, there is much room for improved coordination between power, water, and gas systems, and current practice often lags far behind what is found in the research literature. In this section, we explore these interactions and shed light onto how water and gas systems impact the planning and operation of grid resources. Table 10.4 shows an overview of the difference between these three networks, with common objectives and constraints that are considered in their (independent) optimization.

10.3.1 Water and energy Dubbed the “water energy nexus,” the interactions between water resources, water networks, and energy production and consumption have been a topic of much discussion and research in recent years [47,48]. Focusing on electrical energy within the water energy nexus, we see that electricity is used to transport and treat water, while water is needed in many cases to generate electricity (such as from thermoelectric or hydroelectric power plants). In 2014 the US DOE cited the need for joint water energy policies and national security concerns around climate change impacts on the nexus. The intersection between water use and power production, for example, is complex. Broadly speaking, there are three main ways that water-related issues can impact power generation:

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Table 10.4 Brief comparison of power, water, and gas networks, and associated operational goals/constraints. Power networks

Water distribution networks

Natural gas networks

Approximate number of customers connected

145 million

300 million

75 million

Bidirectional flows

Yes, often in transmission (high-voltage) networks and more recently in distribution

Rarely

Storage elements

Transmission and distribution scale energy storage (i.e., batteries, flywheels, and pumped hydropower); electric vehicles as storage

Common cost and operational objectives

Deliver power to loads in the most economical way and maximize renewable energy use/minimize curtailment Common Voltage magnitude operational ranges; frequency requirements regulation, N 2 1 and network criteria at the constraints transmission level, and power flow dynamics LNG, Liquefied natural gas. Source: Data from [44 46].

Sometimes, since 2013, the number of pipes modified for bidirectional flows has grown to account for seasonal variations in gas demand Water tanks, Underground towers, and storage facilities reservoirs such as salt caverns, aquifers, and depleted natural gas or oil fields; gas can also be liquefied (LNG) and stored in tanks Minimize energy Minimize energy costs for costs for gas pumping, losses compressors and due to pipe maximizing line friction pack (volume of gas stored in the pipeline) Water pressure Pressure and flow limits, requirements, maximum/ temperature minimum water requirements, levels in storage compressor tanks, water flow station constraints, dynamics and gas flow dynamics

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lack of water for cooling (e.g., nuclear and coal) or generation (e.g., hydropower); • high intake water temperatures; and • high discharge water temperatures. Thermoelectric power plants are often defined by their fuel source (“coal plants,” “natural gas plants,” etc.) and typically use steam turbines to generate electricity. This is performed by using the fuel source to boil water, producing high-pressure steam which rotates the turbine, enabling the generator to produce electricity due to the principles of electromagnetic induction. In order to continue this process the steam must be condensed back into water. This is performed by the power plant cooling system. By far, the two most common cooling systems for thermoelectric power plants in the United States are “once-through” systems and “closed-loop” or “recirculating” systems. As the name suggests, oncethrough cooling systems draw water from nearby sources (e.g., oceans, aquifers, and rivers) and circulate it in pipes that run throughout the plant. As the water in the pipes is cooler than the surrounding hot steam, the steam is condensed into water and can be used by the steam turbine to generate electricity. After the water absorbs the heat the warmer water is discharged back into the water supply. If the intake water is too warm, the plant may struggle to condense the steam and thus struggle to effectively generate electricity. If the discharge water is too warm, plants and animals in the water supply can face harmful environmental effects (which is why there are typically strict regulations on discharge water temperatures for power plants). However, in dire circumstances where power must be generated, these restrictions on water temperature have been known to have been waived in order to keep generators running—during 2012 in Chicago, for instance, discharge temperatures were temporarily relaxed to 97 from the typical limit of 90 in order to maintain enough power generation during high summer temperatures [49]. There can be resulting ecological impacts from once-through cooling systems, especially when discharge regulations are relaxed to ensure power generation. Closed-loop, or recirculating cooling systems use less water overall because they reuse the majority of the water they withdraw. Closed-loop systems typically use cooling towers to expose the hot water to air after it has been circulated through the system, allowing the air to cool the water before it is recirculated back through the system. While some of the water is lost to evaporation, much of it can be reused for cooling. Closed-loop

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cooling systems are more common in newer thermoelectric power plants and in the western part of the country where water resources are more scarce, while once-through systems are more common in the eastern part of the United States. Water used for thermoelectric power generation represents over 40% of US freshwater withdrawals (Fig. 10.6, [51]), and many power plants throughout the United States have had to shut down or reduce output in recent years due to water-related issues. Droughts continue to impact the water supply, heavily affecting the generation capabilities of power plants, particularly nuclear, hydroelectric, and coal [52]. Hydroelectricity, a source of renewable generation, is one clear instance of the water energy nexus. According to the National Hydropower Association, hydroelectric generation provides over half of the nation’s renewable energy, and 7% of all generation in the United States. Consider the Hoover Dam as one example. The Hoover Dam was constructed in between 1931 and 1935, primarily to regulate the flow of the Colorado River. The Colorado River marks the border between Nevada and Arizona and feeds into the Lake Mead reservoir, a man-made lake that represents the largest reservoir in the country in terms of water capacity. A hydroelectric power plant was constructed in 1936 to harness the energy of the flowing water, providing power to nearly 8 million people. However, after nearly two

Figure 10.6 Distribution of total water use in the United States (freshwater and saline). Power plants account for nearly half of the total water use in the United States and 41% of freshwater withdrawals. Data from Natural Resources Defense Council, Inc., Power Plant Cooling and Associated Impacts: The Need to Modernize U.S. Power Plants and Protect Our Water Resources and Aquatic Ecosystems, NRDC Issue Brief, Apr. 2014.

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decades of drought and increased demand due to rapid population growth in the area, Lake Mead’s water level continues to decrease [53], heavily affecting the power generation capabilities of Hoover Powerplant, one of the country’s largest hydroelectric plants. Coupled with increasing summer building loads in the area, a lack of generation capacity threatens the area’s peak demand electricity supply. Case study: Venezuela’s Guri dam The Guri dam in Venezuela, which generates hydroelectric power and provides power to 65% of the country, produces five times the power of the Hoover Powerplant. The plant struggled to provide power during a drought in 2016 and continued to struggle due to a 45% decline in rainfall since 2013. To account for the lack of generation the government implemented 4-hour daily power cuts to conserve capacity.

The relationship between energy and water also traverses the opposite direction: electricity is essential for pumping, treating, and transporting water. There exist around 200,000 drinking water treatment systems in the country and approximately 15,000 wastewater treatment plants, many of which use electricity as their main source of energy. Electric pumps can be used to draw water from a source (e.g., a lake or reservoir) and deliver water through a distribution network when a difference in gravity is not present or pressure levels are insufficient to deliver water to consumers at the specified pressure levels. In California an estimated 19% of the state’s entire electricity consumption and 30% of its natural gas consumption is used for dealing with water and wastewater due to the energy requirements from pumping, treating, and more [54]. At drinking water plants, most of the energy consumed is for operating pumps; at wastewater plants, most energy is consumed by aeration, pumping, and solids processing. At the network level, water and power networks, typically owned, operated, and maintained independently, have opportunities to coordinate for improvements in overall system efficiency [55]. Controlling electric water pumps in a grid-responsive manner has load-shifting potential which can be significant, considering operating wastewater and drinking water networks in the United States comprises roughly 4% of the national electricity consumption [56]. Variable-speed pumps, water tanks, reservoirs, and pressure valves within a water network all interact together to

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maintain water delivery throughout the network within pressure and flow requirements, and all have potential opportunities to shift their consumption to impact the power network, providing operational, environmental, and economic benefits [57]. Similar to power networks, water distribution networks also maintain reserves and “ancillary services” such as balancing storage, though tanks, towers, and/or reservoirs, which help balance fluctuations in water demand. During a power loss event, joint control between power and water networks can help prevent damage to the water network: power outages halt pump operation, leading to pressure changes that can damage pipes, resulting in power outages being one of the main causes of damage to water networks [58]. While these concerns are presently mitigated by backup diesel generation and pressurized surge tanks, improved joint control can improve system efficiency and prevent damage in future water networks [59,60]. Some water utilities are beginning to consider or are already participating in grid services [61], but there exist many further opportunities for these two industries to connect. For example, pumps at filtration plants at the Eastern Municipal Water District (EMWD) in California participate in DR by shutting off when requested and can stay off for multiple hours. EMWD receives $100,000 each year from SCE for providing this grid service [62]. Pennsylvania American Water also participated in a pilot program where a single 700-hp motor was used to provide frequency regulation, varying its output but never shutting off. The operators observed no notable change in service or operation, and Pennsylvania American Water received $20,000 annually for providing this service. These, along with other case studies, have proven the efficacy of coordinating these two sectors. Further potential venues for joint control within the water energy nexus include, but are not limited to, • performing intelligent pump scheduling to improve system efficiency, coincide with renewable generation, decrease power grid loading, and reduce electricity costs; • installing on-site generation at water facilities, reducing demand during peak hours or potentially providing power back to the main grid; and • adjusting the speed of variable-speed pumps to provide grid ancillary services or reduce/increase demand when needed. While some studies state that the current lack of coordination between power and water utilities is a large barrier to their joint success [63], these two sectors have an abundance of potential for achieving joint financial and environmental benefits. Financial incentives, and increased awareness,

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are two of the main factors that could help facilitate future coordination within the water energy nexus.

10.3.2 Power and gas Natural gas is a nonrenewable fuel source that is used for heating, clothes drying, cooking, as fuel for power plants, and as transportation fuel. In 2018 natural gas production in the United States hit a record high. As seen in Fig. 10.7, natural gas used in the electric power sector comprises a larger portion of consumption compared to any other sector. This has increased over recent years; partially due to technological advancements in hydraulic fracturing (fracking), many gas-fired power plants are replacing aging and cost-ineffective coal plants—in 2015 natural gas surpassed coal as the United States’ dominant fuel for power generation. Gas-fired power plants were traditionally used as “peaker plants,” that is, power plants that are typically turned on for a few hours of the day to deliver power during peak grid loading, but due to decreases in fuel costs, in recent years they have also been used as base-load power plants [64]. The backbone of natural gas delivery in the commercial, residential, and industrial sectors is the natural gas piping and storage infrastructure. The natural gas pipelines in the United States, which has the largest pipeline system in the world, has its own transmission, distribution, and storage infrastructure, just like water and power networks. Also similar to the power network, but differing from water distribution networks, gas pipelines can be

Figure 10.7 2018 US natural gas consumption. Total use in 2018 averaged over 80 billion cubic feet per day. Data from the EIA.

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upgraded to support bidirectional flows of gas, which is useful in regions where natural gas use is seasonal, and can avoid the need of building additional pipelines [65]. Over the years, temperature fluctuations and increases in natural gas extraction have driven peak gas demand higher in the winter months, motivating a DR program for gas consumers. For example, Consolidated Edison (ConEd) in New York is piloting a 3-year, $5 million gas DR program, in attempts to manage peak demand and avoid additional pipeline installations [66] after concluding that peak gas demand in their territory will increase significantly in the next decade. Changing behavioral patterns toward electricity and gas use is a challenging problem; however, Southern California Gas Company’s Smart Therm DR program saw 40% of consumers override DR requests (out of approximately 10,000 thermostats) during their 2017 18 winter program season [67]. Both engineering challenges and societal challenges continue to pose roadblocks for demand-responsiveness in power, water, and gas networks. On the supply side, despite a tight coupling between power and gas networks, system operators for these networks rarely communicate to coordinate planning or operational decisions. Benefits from joint planning of these networks, for example, can take into account coincident loads for minimizing the costs due to expansion planning [68]. Optimizing the joint control of these networks can also help improve overall system efficiency and lower fuel costs [69,70]. Gas networks, such as water and power networks have complex, nonlinear dynamics and are not trivial to model or optimize [71]. Typical system characteristics, operational objectives, and constraint considerations for both power and gas networks can be compared in Table 10.4. Solving the joint planning and operation optimization problems is not trivial; often, integer variables are included (e.g., for on/off or include/exclude design decisions) and constraints that render the problem nonconvex (e.g., gas and power flow dynamics). Various methods of reducing problem complexity and convexifying problem constraints have been developed to help make joint gas power problems computationally feasible [68].

10.3.3 Combined heat and power Multigeneration systems that use gas to produce electricity, such as CHP plants (e.g., cogeneration), reduce losses due to power transmission and

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introduce many opportunities to coordinate electrical with thermal loads. Natural gas fueled CHP plants can have highly coupled gas power dynamics and can offer efficiency gains over conventional gas-fired generation sources. Unlike conventional thermoelectric power plants, the waste heat from generating electricity is used to provide heating to consumers (and cooling in the case of tri-generation plants) [72]. As discussed previously in the water and energy section, heat generated by creating electricity is typically rejected into a water source or into the air. In contrast, CHP systems harness this heat, using it to deliver steam or hot water to buildings, reducing the need for on-site heat sources. This can reduce strain on the electric distribution grid due to decreased electrical loads and decrease overall electrical losses. However, while there are many benefits from using CHP systems, they can require high capital costs due to the need for additional steam and hot water distribution infrastructure. In addition, they are most appropriate for energy districts that supply power and heat to buildings that have coincident electrical and heating loads such as hospitals and university campuses. Lastly, similar to conventional thermoelectric power plants, they require fossil fuels to operate, so while the use of CHPs can improve overall system efficiency, it is not an end-all be-all solution for clean energy. The interconnection between gas use and power generation is evident here; however, due to this coupling, CHP plants have opportunities to participate in DR programs [73] and coordinate with energy storage [74] to provide grid services while ensuring a reliable delivery of electricity and heating to consumers. Due to their localized nature of providing electricity and heating to districts or communities of buildings, they are also often coupled with microgrids, introducing opportunities for joint microgrid and CHP coordination as well [75]. Despite many encouraging results in the literature and positive pilot studies, there is still much left to be desired for joint the coordination of water power, gas power, and even water gas power networks. Engineering roadblocks include a lack of communication infrastructure and sensor networks necessary for the coordination of these networks; roadblocks outside of engineering and technical considerations can stem from a lack of communication between sectors, risk-adverse mentalities, and a lack of consumer awareness or willingness to participate in DR programs.

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10.4 Electrification of the transportation sector Like water and gas systems, the transportation sector is also energyintensive, although presently less linked to the electrical grid than these other systems. Transportation currently comprises about 30% of primary energy consumption in the United States, but less than 1% of electricity demand, meaning that there could be large potentials for electrification. However, as the transportation sector becomes more electrified, it means more strain on the electric grid—and a pressing need for innovation in grid planning, operations, and markets.

10.4.1 Consumer vehicles By 2030 the IEA predicts that the number of EVs on the road will increase from roughly 3 million to 125 million [11]. Most charging of these EVs will occur at residences, as many consumers find home charging more convenient than charging at other locations, with some studies showing over 80% of charging activities occurring at home [76]. While convenient to the consumer, the associated distribution infrastructure needed to accommodate this additional increase in load is astounding, especially with the introduction of fast-charging EV stations. Level 3 charging stations (which are typically not installed at residences), for example, can charge at 50 times the rate of Level 1 chargers (which use standard 120 V residential electrical receptacles). A comparison of Levels 1, 2, and 3 charging is seen in Fig. 10.8, with typical ranges of current draws and charging rates taken from multiple sources [78,79]. Level 1 chargers use standard 120 V AC outlets on 15 20 A branch circuits, typically pulling up to 16 A, and thus require no additional infrastructure or adapters for residential use. Level 2 chargers use standard split-phase 240 V AC outlets, similar to 240 V appliances such as electric clothes dryers, fully charging most EVs in 4 6 hours [77]. Level 3 charging is high-voltage, DC “fast-charging” that requires a 480 V DC supply and can pull up to 300 A of current, fully charging most EVs in under an hour. Considering this is a higher current than the entire rated electrical service entrance of most residences, Level 3 chargers are very expensive to install in places outside of commercial and public settings. Charging at such high currents will also create a lot of heat and may also degrade the EV battery when used frequently. Considering the slow charge rate of Level 1 chargers, most public chargers are Levels 2 and 3; the city of Boulder, CO,

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Figure 10.8 Typical ranges of voltage, current, and power ratings of Levels 1, 2, and 3 charging stations, with various plug/charger appearances for each option. Note that Level 3 chargers have vehicle-dependent charging ports (e.g., the middle EV connector under Level 3 charging is for Tesla Model S and X vehicles [77]). EV, Electric vehicle.

for instance, has 207 public Level 2 chargers (94%) and 13 Level 3 chargers (6%), with 31 stations being free of charge. Wireless EV charging is also an active area of research and development, whose ultimate goal is to utilize wireless, inductive charging between vehicles and the road, dynamically charging the EV as the user drives [80]. Residential distribution networks and feeders, which generally are nonmeshed, low-voltage networks that deliver power to consumers, were originally constructed for supplying power to traditional building loads such as water heating, HVAC, and lighting. In comparison, an EV with a daily commuting distance of 25 mi would require roughly 6 8 kWh to recharge, which is equivalent to the entire daily energy needs of a small household. Some consumer EVs, such as the Tesla Model S, have very large batteries—100 kWh—and have the potential of putting a very large strain on the grid. With the increasing adoption of EVs the deterioration of distribution infrastructure and overloading of distribution lines and transformers is a real and immediate problem that must be addressed. Installing higher capacity infrastructure to accommodate these loads will result in enormous costs, motivating the use of dynamic pricing schemes or other DR programs. The optimal placement of charging stations throughout a city, taking into account the distribution infrastructure and grid constraints [81], is an open and interesting research problem incorporating stochasticity and uncertainty, binary or integer variables, and traffic pattern models. Finding efficient ways of modeling and solving these problems can help cities avoid further infrastructure upgrades and costs in the near future.

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10.4.2 Public transportation The current dearth of transportation electrification is heavily impacting air quality and the environment. According to [82], the average car in the United States produces roughly 0.96 lb of CO2 (which makes up 95% of all transportation-related emissions) per passenger mile (which is defined as one mile traveled per one passenger). Bus transit, due to the ability to transport more passengers in a smaller space, emits roughly 0.64 lb of CO2 per passenger mile, with light rail transit at 0.36 lb/CO2 per passenger mile. In terms of life cycle emissions the amount of CO2 per passenger mile for any vehicle mostly comprises the operational emissions of that vehicle. Emissions from sedans, SUVs, and pickup trucks is higher than any form of public transportation but from public transportation is significant [83]. From these numbers, we can gather than electrifying the public transportation sector would make a significant impact on air pollution and emissions reduction. Some cities leverage electric trolley systems (e.g., San Francisco), and light rail systems (e.g., Chicago), but these require overhead wires to operate. Many other cities are deploying battery-electric buses, despite concerns with hill-climbing torque capabilities, range anxiety, or extreme heat and cold effects on battery efficiency. Due to various emissions-cutting goals, it is estimated that by 2045, a third of the public buses in the United States will be zero-emissions [84]. Internationally, many other countries have ambitious goals for electrification—within the Chinese city of Shenzhen, all buses are now electric, with many cities forming plans to follow suit. Switching from diesel buses to electric not only has emissions benefits but also noise pollution benefits as well. Case study: school bus electrification Many school buses around the United States have been converted from diesel-powered to all-electric powertrains, reducing children’s exposure to air pollution. On average, school buses travel roughly 100 mi per day, making them a prime candidate for electrification. During the day, while not in use, the bus batteries can potentially be used to provide grid services such as load shifting.

A challenge for both electric public transportation infrastructure and public use is that battery-powered buses typically will not have the same range as a diesel-powered bus, meaning that buses would have to charge

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before fully completing their routes, or additional buses would have to be provided as alternatives. As many of these batteries will likely be charged at night, after bus service has ended, peak demand charges due to bus charging has also been identified as a major roadblock to the deployment of electric buses. Under high demand charges the cost to charge a single electric bus at night can increase by $0.24 per mile, for example [85]. In order to avoid these charges, and the associated stress and strain on the electrical distribution infrastructure, electric bus charging must be staggered, performed at different charging stations throughout the day, or, in the future, utilize wireless inductive charging.

10.4.3 Rideshare services and emerging methods of transportation Alternative modes of electric transportation have emerged in recent years, including EV rideshare programs and applications, electric scooter-sharing systems, and electric bicycle sharing systems. Rideshare company Lyft, for instance, offers a “Green Mode” that allows passengers to request rides from EVs, in addition to their commitment to maintaining a zero-emissions standard through the purchase of carbon credits. This further incentivizes Lyft drivers to consider purchasing or leasing EVs over gasoline-powered vehicles. An increase in EVs within rideshare programs may cause spatiotemporal changes in electrical loads throughout a city—drivers may now need to charge vehicles at a variety of hours and locations, rather than follow the traditional model of athome EV charging [76]. Near the end of 2017, new methods of electric transportation began to arise. GPS-tracked electric scooters started to litter streets throughout the United States, seemingly popping up overnight, providing dock-less modes of transportation for a marginal fee. These electric scooter companies pay users to track and pick up the scooters with depleted batteries and charge them, which usually entails collecting multiple scooters and charging them at home with company-provided adapters. Uber’s JUMP program extended this concept to dock-less electric bicycles, providing another convenient and inexpensive way to get around a city. While individual scooters and electric bikes alone may not have a significant additional stress on distribution infrastructure, en masse, their charging can be equivalent to an additional large building load. For example, if a battery on an electric bike is rated 20 Amp h at 48 V, completely charging the battery amounts to almost a kilowatt hour of energy. Simultaneously

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charge a few of these bikes at home, and the resulting increase in electrical load is equivalent to turning on an air conditioner or electric water heater. Further, if multiple homes on the same distribution feeder increase their load by a few kilowatt hour, the voltage in the feeder may sag further and create significant strain on the electrical infrastructure. It is unclear what regulations will emerge in this realm, or if providing easily accessible new modes of transportation will help decrease loads in general by providing a less energy-intensive mode of transportation. Due to their nascent presence within a limited number of cities, it is difficult to predict where and how electric scooters and bicycles will impact the power and transportation sector. However, considering electric scooter companies Bird and Lime reached $1 million valuations within a year of launching and have a rapidly expanding user base, continued use of these technologies is a reasonable assumption. With that said, alternate modes of electric transportation are transpiring so quickly that by the time you read this chapter, there will likely be an entirely new technology not mentioned here.

10.4.4 Vehicle-to-grid Typically, EV batteries, in contrast to stationary energy storage systems, draw power from the grid but do not push power back to the grid. However, because EVs have significant battery capacity, discharging these batteries through a vehicle-to-grid (V2G) system has the potential to provide grid services such as frequency regulation and peak shaving. Most car manufacturers do not allow V2G capabilities due to warranty issues and battery wear-and-tear, but in some locations such as Japan, V2G systems are being explored for resiliency benefits. Car manufacturer Nissan has developed a V2G kit for homes for this purpose: In Japan the average home uses 10 12 kWh a day. A Nissan Leaf can have a 24 kWh battery capacity, meaning that it could provide power for an entire home for 2 days during a disaster or grid failure. PJM Interconnection is one of the entities that is exploring this area in the United States; through an initiative with the University of Delaware, PJM is demonstrating how aggregate control over a fleet of EV batteries can help provide frequency regulation [86]. As EVs can be thought of as a form of distributed energy storage, the ability to use them as a grid resource can prove invaluable in times of need. Outside of fast-timescale grid services, considering that many EVs are left unattended for multiple hours during the middle of the day at office

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parks or public charging stations, many opportunities arise for the batteries within these vehicles to shift daily demand patterns from commercial building loads. By studying actual arrival/departure patterns, researchers were able to determine that coordinating EV charging with distributed solar and smart building loads can provide revenue for building owners in addition to alleviating grid stressors [87]. As the power system becomes more inverted dominated, we will have fewer conventional power plants and thus large-scale inertial resources to provide automatic frequency regulation services, making EV battery storage and other energy storage systems extremely valuable for achieving fast-timescale grid services [88].

10.5 Considerations for future power systems The previous sections in this chapter only begin to touch on the many topics that offer both challenges and opportunities for future power systems and power-dependent networks. As infrastructure ages, more renewable generation is connected to the grid and extreme weather events continue to impact the ability of generators to deliver power to loads; revolutions in traditional thinking around power systems operation must evolve to accommodate emerging challenges. Broadly speaking, there are three main considerations that the planning and operation of future power systems should take into account to address these issues and to harness the benefits from demand-side resources and interconnected system operations: physical considerations, market considerations, cyber considerations, and the overlap and intersection of the three.

10.5.1 Physical considerations In addition to transmission-level upgrades, distribution-level, distributed upgrades such as the installation of AMI or community-scale energy storage, and distributed generation installations can prove useful to supplying power to communities when the grid has been impacted by a hazard upstream. These resources can enhance the ability to supply power locally, reducing the dependence on the main grid when it is damaged, and provide more information for grid operators through deployed AMI, which can assist operators in identifying and localizing specific outage areas and expediting repair. Region-specific grid upgrades will become more

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important as the threats to the grid become more geographically dependent: Burying overhead lines may be advantageous in vulnerable regions where hurricane-force winds are expected to increase the frequency of grid outages; however, in coastal regions, underground wires can be susceptible to damage from flooding, and another grid strengthening measure may be more valuable. Increases in inverter-interfaced devices such as rooftop PV and stationary energy storage will necessitate developments in power electronics and associated controls in order to maintain and preserve grid stability.

10.5.2 Market and organizational considerations Without corresponding economic benefits and enabling markets for DR and for other entities to provide grid services, it would be nearly impossible for the coordination and collaboration between systems discussed in this chapter to occur. A two-way communication infrastructure, on relatively fast timescales (i.e., seconds or minutes), will be essential for building loads, transportation, and other networks to assist the grid and redirect load during a contingency or other grid event. Frameworks such as transactive energy markets [89], not discussed in this chapter, prove promising solutions to coordinating demand-responsive assets with grid needs through the use of pricing or carbon signals. As more building loads gain digital controls or are replaced by variable frequency drives, the fasttimescale capabilities of these assets to provide grid services is enhanced, but the markets and communication infrastructure must also be updated to facilitate these transactions. Lastly, consumer awareness and the willingness for different sectors to work together to achieve common goals is an important step that has yet to be overcome.

10.5.3 Cyber considerations Lastly, the increasingly connected nature of modern power systems provides bountiful opportunities for increased coordination and control between multienergy networks. However, the cyber threats introduced by the Internet-of-Things and wirelessly connected grid assets is an important area of consideration when designing and operating future power systems. One weak link in the system could open further vulnerabilities in the larger power grid, causing widespread outages and damage to propagate throughout multiple networks. As systems become more interconnected, these threats become more and more pressing, and design

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considerations that encompass the physical and cyber systems are necessary. Thorough testing and validation through gray and white hat hacking of IoT devices, communication frameworks, transactive platforms, and connected EVs will help identify vulnerabilities that could prevent catastrophic grid failures in future power systems.

Acknowledgments The author would like to thank Bridget Toomey, Joseph Kasprzyk, Jonathan Donadee, Kaitlyn Garifi, and the University of Colorado Boulder Spring 2019 Grid Connected Systems class for inspiring him to research this material in the first place.

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Index Note: Page numbers followed by “f,” “t,” and “b” refer to figures, tables, and boxes, respectively.

A AC induction motor, 4 AC power flow model, 24, 42 44. See also Decoupled linear AC power flow models, voltage magnitude Actor-critic using Kronecker-factored trust region (ACTKR), 15 16 Adaptive boosting model (AdaBoost), 215, 245 Advanced grid operational tools, state estimation model validation, 165 178 erroneous parameters from large model dataset, 165 largest normalized Lagrange multiplier test, 166 173 measurement errors, 165, 177 178 model parameter errors, 165 parameter errors, detectability and identifiability of, 177 178 protective relaying, 192 204 challenges of, 193 dynamic state estimation based protection, 193 196 event study, 201 204, 202f, 203f legacy protection functions, 201 series compensated transmission line, 197 system monitoring, 178 192 Bayesian dynamic state estimation, nonlinear regression, 184 192 motivations for dynamic state estimation, 178 180 problem formulation of dynamic state estimation, 180 183 Advanced metering infrastructure (AMI), 210, 237 238, 281 282, 293, 308 309 Aggregated microgrids, 261 262

Aging infrastructure and climate-related impacts, 277 278 average lifespan of coal, natural gas and nuclear plants, United States, 278t power generation sources, 278 Algebraic network model, 51 AMI. See Advanced metering infrastructure (AMI) AMUs. See Asynchronous measurement units (AMUs) Analog ensemble model, 215 216 Ancillary services, 99 100, 298 299 and associated timescales, 125 126, 125t Artificial intelligence (AI), 12, 209 210 CNN-LSTM deep learning architecture, 238 243 advantage, 249 250 convolutional neural network (CNN), 238 243 developed CNN-LSTM model, 241 243, 242f evaluation metrics, mean of, 249t experiments, 243 245, 243t, 245t hyperparameters, 243 245, 244t long short-term memory network (LSTM), 239 241, 240f occupancy detection results, confusion matrices of, 248f parameters, 245t typical binary classification problem, 246 247 deep learning gated recurrent unit (DLGRU), 231 232 forecasting, 210 and machine learning, 10 11 building occupancy detection, 235 250 forecasting, 11 heterogeneous, 10

359

360

Index

Artificial intelligence (AI) (Continued) large volume and high velocity, 11 low value density, 11 operation and control, 11 perception, 11 structured data, 10 unstructured data, 10 smart grid, applications to, 211f state estimation, 210 212 Artificial neural networks (ANNs), 213 216, 218 220, 229 231 Asynchronous advantage actor-critic (A3C), 14 16 pseudocode for, 14 15, 16b Asynchronous measurement units (AMUs), 79, 81 83, 84f, 86 87 Asynchronous voltage phasors, 268 Augmented Lagrangian relaxation, 230 231 Automated machine learning (AutoML), 20 21 Automatic generation control (AGC), 35 36, 122, 127 129, 229 230 Automatic meter reading (AMR) systems, 281, 283f, 292 293 Automatic voltage regulators (AVRs), 229 230 Autoregressive integrated moving average model, 213 215 Autoregressive model, 213 214 Autoregressive moving average model, 213 214 Avista distribution system, 268 269

proposed unified framework, 185 188 robustifying, proposed framework for, 188 191 Bayesian inference, 137 Bayes’ rule, 184 185 Behind-the-meter (BTM), 114, 119 120 Behind-the-meter (BTM) distributed energy resources (DERs), 99 100, 131 132 Bellman equations, 3 6 expectation equations, 5 optimal equations, 5 6 Bidirectional long short-term memory network (BiLSTM) layer, 240 241 Binary confusion matrix, 246 248, 246f Blockchain technology in electric power systems, 138 139, 139f Bluetooth, 280 281 Boosting, 215 Bootstrapping, 5 Branch power flow models, 53 54, 74 75 Bronzeville microgrid, 262 263 Brown’s Ferry nuclear plant, 290 Building-to-grid, 284 285 Bulk electric power grid dispatch in inverter-based resources systems, 119 120, 120f Buses in power grids, 9f Bus injection power flow equations, 53, 55, 68 2-bus system, 34 35, 34f

B

California independent system operators (CAISO), 100 101, 105 106, 118, 126, 133 135 Energy Imbalance Market program, 130 131 typical marginal price for energy, 122 123 Cauchy distribution, 191 Centralized dynamic state estimation for power system, 191 192 Centralized versus distributed transactional platform, 139f Chapman Kolmogorov equation, 184

Bagging, 215 Balanced distribution systems, 41 44 Balancing Authority’s area control error (ACE), 128 129 Base flexibility, 105 Bayesian dynamic state estimation, nonlinear regression, 184 192 decentralized vs. centralized dynamic state estimation for power system, 191 192 for nonlinear dynamic system models, 184 185

C

361

Index

Chi-square value, 196 Cholesky decomposition, 186 Cholesky factorization, 175 176 Classification and regression trees (CARTs), 217 218 Closed-loop systems, 296 297 Close-loop cooperative control law, 59 60 Coal unit, 123 Cold-start DC power flow models, 25 26 Combined heat and power (CHP) plants, 120, 133 135, 137 138, 285, 301 302 Community choice aggregation (CCA), 130 131 Competitive ensemble methods, 215 bagging and boosting, 215 multi-model forecasting framework, 218 220, 219f wind speed forecasting based on, 223 227, 224t, 226f, 226t Comprehensive energy system, 137 138 Consolidated Edison (ConEd) in New York, 277 278, 301 Constraint optimization method, 195 Consumer vehicles, 303 304, 304f Contingency analysis (CA), 103 104 Contingency reserve, 127 129 Continuous-time models, 183 Control continuum in power industry, 129f Control functions vs. dispatch functions for utility-scale inverter-based resources, 122t Controllable load (Type II DR), 114 Conventional power flow techniques, 268 269 Convex relaxation techniques, 16 17 Convolutional neural network (CNN), 12, 237 239 Convolutional neural network-long shortterm memory network deep learning architecture, 238 243 advantage, 249 250 convolutional neural network (CNN), 238 239

developed CNN-LSTM model, 241 243, 242f evaluation metrics, mean of, 249t experiments, 243 245, 243t, 245t hyperparameters, 243 245, 244t long short-term memory network (LSTM), 239 241, 240f occupancy detection problem formulation, 238 occupancy detection results, confusion matrices of, 248f parameters, 245t typical binary classification problem, 246 247 Cooperative control theory, 58 59 Cooperative ensemble methods, 215 framework of, 221f probabilistic solar power forecasting, 215 216 probabilistic wind power forecasting, 215 216, 220 surrogate model, 220, 222 wind power forecasting based on, 227 228, 227t, 228f, 228t Cooperative real power control, 62f, 63 Critical parameter-measurement pair, 177 Critical parameter pair, 177 Curriculum learning, 19 20 Cybersecurity and privacy, 292 294

D Data-acquisition system, 193 Data-driven machine learning algorithms, 237 Data-driven models, 27 Data management and analytics, 136 137 Day-ahead market clearing process, 102 103, 102f Day-ahead unit commitment process, 121 122 DC power flow (DCPF) model, 9, 24 25, 28, 33 34, 40 41, 44 46 fast computation speed, 33 34 Decentralized dynamic state estimation for power system, 191 192 Decision-making, 2 Decision tree model (DT), 237

362 Decoupled linear AC power flow models, voltage magnitude balanced distribution systems, 41 44 linear power flow models for meshed transmission systems, 26 37 derivation and justification of FDLPF, 33 37 matrix formulation of DLPF model, 30 31 transformers and phase shifters, 31 33 voltage magnitude and phase angle, decoupling of, 26 29, 29f linear three-phase power flow models, unbalanced distribution systems, 37 39 meshed transmission systems, 40 41 computational efficiencies, for large-scale systems, 41t errors of different linear power flow models, 40t IEEE 118-bus system, voltage magnitudes and branch MW flows for, 41f 33-node ill-conditioned system (lumped overload), 42f 33-node ill-conditioned system (uniform overload), 42f unbalanced distribution systems, 44 46 Decoupled linearized power flow (DLPF) model, 25 26, 28, 40 41, 44 46 matrix formulation of, 30 31 voltage magnitude, 41f Deep deterministic policy gradient (DDPG), 15 16 Deep distributed recurrent Q-networksaction discovery (DDRQN-AD), 230 Deep learning, 210, 216, 231, 237 Deep learning gated recurrent unit (DLGRU), 231 232, 234, 236f block diagram, 234f generation units’ power, 236f reconfiguration switching of, 236f Deep Q-network (DQN), 12 14 Deep reinforcement learning (DRL), 12 22

Index

Demand management system (DMS), 133, 137 138 Demand response (DR), 33 35, 281 CHP plants, 302 definition of, 286 emerging considerations for, 289 290 FPL’s on call program, 288 incentive-based programs, 286, 287f price-based programs, 286, 287f Rush Hour Rewards program, 288 shape, 288 289 shed, 288 289 shift, 288 289 shimmy, 288 289 time-of-use (TOU) prices, 286, 287t XCEL Energy’s Windsource Program, 288 Demand response resource (DRR), 105 106, 114 117 Demand-side management (DSM), 279 280, 288 Department of Energy, 277 Dependability, 192 DERs. See Distributed energy resource (DER) Deterministic forecasting methods, 213 214 DHA. See Dynamic hosting allowance (DHA) method Differential and algebraic equations (DAEs), 13, 164, 180 181, 195 Direct load control (DLC), 284 285 Distance protection, 201 Distributed denial-of-service (DDoS), 292 Distributed energy resource (DER), 2, 5 8, 14 15, 21, 50, 87 88, 89f, 90 91, 99 102, 117 118, 131 135, 132f, 178 180, 182 183, 262 263, 266, 276, 283 284 abnormal frequencies, response to, 267t abnormal voltages, response to category I, 267t category II, 267t category III, 267t grid-interactive buildings, 285 286

Index

power losses, transmission and distribution in United States, 285f reliability, 285 286 resilience, 285 286 microgrids, 5 8 photovoltaic (PV), 5 7, 6f connecting with grids, 6f curves, 7, 7f DC output of, 6 7 Distributed generator (DGs), 138, 261, 270, 285 Distributed real power control, 63 Distributed subgradient algorithm, 58 59, 79 Distributed subgradient voltage control, 59 64 Distributed system platforms (DSPs), 101 102, 131 135 Distribution grids, 260 261 Distribution network model, 52 54, 52f, 54f with high penetration renewables, 66f with measurements, 83f Distribution system operator (DSO), 87 88, 132 133, 132f, 135 136 functionalities in different levels, 136 138, 137f advanced DMS/SCADA system, 137 comprehensive energy system, 137 138 data management and analytics, 136 137 resource dispatch system, 137 reliable and resilient operation of, 136 transmission and gas systems, connection with, 134f Distribution systems, 1, 266, 270 271 errors of different linear power flow models for, 43t Dragonfly 2.0 targeted power companies, 293 294 Droop control, 76, 79 80 Droop strategy, for PQ inverters, 75f Dynamic distributed generation (DG) models, 51, 56 57

363 Dynamic hosting allowance (DHA) method, 51, 87 90, 88f, 89f Dynamic microgrids concept of, 263 264 definition of, 263 networked microgrids operation in, 264f parallel inverters steady-state operation, 264 266 transient-state operation, 266 273 reconfiguration operation, 272f topology of, 263 Dynamic NN (DNN), 230 Dynamic security assessment (DSA), 103 104, 180 Dynamic simulations, 268 269 Dynamic state estimation (DSE), 164, 178, 184, 188 Bayesian dynamic state estimation, nonlinear regression, 184 192 decentralized vs. centralized dynamic state estimation for power system, 191 192 for nonlinear dynamic system models, 184 185 proposed unified framework, 185 188 robustifying, proposed framework for, 188 191 benefits of, 178 180 dependability and reliability of protection systems, improved, 179 180 enhanced reliability of system models, 180 hierarchical decentralized control, enhanced, 179 oscillations monitoring, improved, 178 179 motivations for, 178 180 problem formulation of, 180 183 Dynamic state estimation-based protection protection zone, dynamic model of, 194 quantification of consistency, 195 196 series compensated transmission line, 197, 197f, 199f

364 Dynamic state estimation-based protection (Continued) overall dynamic model, formulation of, 201 R, L, and C matrices, 197, 198t section k in multisection line, 198 200, 199f sequence parameters, 197, 198t source impedances, 197, 197t three-phase series capacitors, 200, 200f trip decision, 196

E Eastern Municipal Water District (EMWD), 299 Effective flexible capacity (EFC), 105 106 Electrical engineering, 144 Electric grid modernization, 260 261 Electricity Consumption and Occupancy (ECO) dataset, 243 Electric power system (EPS), 261 blockchain technology in, 138 139, 139f Electric pumps, 298 Electric Reliability Council of Texas, 100 101 Electric storage resources, 118 Electric system topology variation, 268 Electric vehicles (EVs), 279, 303 304, 306 308 Electrification, 276 benefits, 278 279 in built environment, 278 280 of transportation sector, 279, 280f, 303 308 consumer vehicles, 303 304, 304f public transportation, 305 306 rideshare services and emerging methods, 306 307 school bus, 305b vehicle-to-grid (V2G) system, 307 308 Electromagnetic induction, 296 Element-wise rectified linear unite (ReLU), 239 Emerging customer behavior, 99 100 Emerging dynamic microgrids, 263 264

Index

Empirical mode decomposition (EMD), 215 Energy hubs, 294 Energy Information Administration (EIA), 279 Energy storage market modeling, 110 114 capacity limits constraints with reserves, 110 111 cleared energy and reserve constraint, 111 112 downward reserve equation, 113 energy ramp-down constraints, 113 energy ramp-up constraints, 113 energy storage resources, 110 111, 114 power balance constraints, 111 112 pumped storage hydro, 110 111 ramp constraints for reserve deployment, 112 reserve activation constraints, 113 114 reserve ramp constraints, 113 resource limit constraints, 112 state-of-charge, 110 111, 113 upward reserve equation, 113 Energy storage resources (ESRs), 110 114 Energy storage systems, 76 Energy systems, reinforcement learning in, 23 40 combining prior knowledge, 38 controller training and evaluation, 38 demand response, 33 35 grid operation, 35 37 handling nonlinearity, 24 learning environment, 25 29 limitations and challenges, 39 model adaptability, 23 24 modeling uncertainty, 24 multiagent, 39 proper RL/MDP formulation, 38 real-time readiness, 23 renewable generation and battery control, 37 smart buildings, 30 33 transfer learning, 38 typical workflow, 24 25 Ensemble deterministic forecasting model, 222 223 Ensemble Kalman filter (EnKF), 184 190 Ensemble learning, 213 216

Index

competitive ensemble learning, 215, 218 220 bagging and boosting, 215 multi-model forecasting framework, 218 220, 219f wind speed forecasting based on, 223 227, 224t, 226f, 226t cooperative ensemble learning, 215, 220 222 framework of, 221f probabilistic solar power forecasting, 215 216 probabilistic wind power forecasting, 215 216, 220 surrogate model, 220, 222 wind power forecasting based on, 227 228, 227t, 228f, 228t probabilistic solar power forecasting, 215 216 probabilistic wind power forecasting, 215 216 single machine learning algorithm models, 216 218 ERCOT, 127 EriGRID, 118 Ethernet, 148 151 Euler methods, 183 Event-data-based oscillation modal analysis, 153, 154f Event replay and postevent analysis, 156, 156f Evolution strategies-based reinforcement learning (ES-RL), 16 18 Exogenous variable model, 215 216 Explicit branch model of distribution network, 55 56 Extended Kalman filter (EKF), 184 186, 195

F FACTS devices, 178 180 Fair utilization ratio method, 79 80 Fast and linear power flow model, 24 Fast decoupled linearized power flow (FDLPF) model, 26, 40 42, 44 46

365 computational efficiency, 40 41, 41t derivation and justification of, 33 37 2-bus system, 34 35, 34f numerical example, 36 37 theoretical derivation, 35 36 voltage magnitude, 41f Fast-decoupled-load-flow method, 9 Fast distribution state estimation method, 136 Fast-timescale grid services, 307 308 FDR, 147 148 components, 150t deployment in North America, 148, 149f first generation of, 148 151, 150f installation, 151, 151f low installation cost and high measurement accuracy, 151 second generation of, 148 151, 150f Feature learning blocks (FLB), 238 239 Federal Bureau of Investigation (FBI), 292 293 Federal Energy Regulation Commission (FERC), 118, 125 126, 278 Order 841, 110 111 Order 888, 100 101 Order 889, 100 101 Order 2000, 100 101 Feedback-based algorithms, 14 15 Feed forward neural network (FFNN), 210 212 Flexible ramp-down (FRD) capabilities, 106, 108, 110 Flexible ramp products (FRPs), 106 110 capacity limit constraints for eligible resources, 110 economic dispatch problem with, 107 108 flexible ramp-down, 106 flexible ramp-up, 106, 110 market-wide regulating down reserve requirements, 109 market-wide regulating up plus spinning plus nonspinning reserve requirement, 109

366 Flexible ramp products (FRPs) (Continued) market-wide regulating up plus spinning reserve requirement, 109 market-wide regulating up reserve requirement, 109 ramp capacity requirement, 106 107 ramp demand curves, 107, 108f resource ramping capability constraints, 110 system-wide power balance constraint, 108 109 transmission constraints, 109 Flexible ramp-up (FRU), 106, 110 Flexible resource adequacy and must offer obligation (FRAC-MOO), 105, 118 Florida Power and Light (FPL), 288 FNET/GridEye, wide-area measurement system, 147 151 architecture of, 147, 147f disturbance detection and location, 152 153, 153f event-data-based oscillation modal analysis, 153, 154f event replay and postevent analysis, 156, 156f FDR deployment in North America, 148, 149f first generation of FDR, 148 151, 150f hardware component, 147 interarea oscillation detection, 153, 154f islanding detection, 154 155, 155f machine learning based inertia estimation, 157 159, 159f model validation and parameter verification, 157, 158f North American grids, 148 online ambient-data-based oscillation modal analysis, 154, 155f second generation of FDR, 148 151, 150f software component, 147 statistical analysis of historical data, 156 157, 157f visualization, 154 worldwide power grid monitored, 148, 149f

Index

Forecasting technology, 11, 210, 212 228 categorization, 212 213, 213f deterministic forecasting methods, 213 214 ensemble learning, 214 216 probabilistic forecasting methods, 214 Fractional order control strategy, 230 231 Frequency control, 8, 14, 229 230 Frequency response mechanism design, 128 129, 129f Front-of-the-meter (FTM), 119 120 Future power systems cyber considerations, 309 310 market and organizational considerations, 309 physical considerations, 308 309

G Gain matrix, 167 168, 174 176 Game theory, 22 Gas and heating/cooling (GHC) systems, 133, 135 Gas-fired power plants, 288, 300 301 Gated recurrent units (GRU), 20 21 Gaussian distribution, 169 170, 180, 183, 188 191, 243 244 Gaussian measurement errors, 169 170 Gaussian mixture model, 214 Gaussian Process Regression (GPR), 230 Gauss-Newton algorithm, 210 212 Gauss Newton’s iterative method, 186 Gauss Seidel method, 9 General dynamic state-space model, 183 Generalized autoregressive conditional heteroskedasticity, 215 Generalized maximum (GM)-likelihoodestimator, 189 190 Generalized neurons (GN), 229 230 Generation system, 1 Generator bus, 9 Generator operators, 122 Genotypes, 17 Gird operation paradigm, 117 Global positioning system (GPS), 144 145 Global System for Mobile Communications (GSM), 280 283

367

Index

Gradient boosting machine (GBM), 213 214, 217, 218b, 224 225 Grainger & Stevenson 4-bus system, 36, 36f Green Mode, 306 Grid-connected mode, 262, 264 265 Grid-edge devices and networks, 138 Grid-edge situational inference process, 90 Grid-following inverters, 119, 119t Grid-following mode, 266 Grid-forming inverters, 119, 119t Grid-forming (GFM) mode, 266, 271 273 Grid-interactive buildings, 283 294 climate and environment, 290 292 cybersecurity and privacy, 292 294 demand response, 281, 286 289 definition of, 286 emerging considerations for, 289 290 FPL’s on call program, 288 incentive-based programs, 286, 287f price-based programs, 286, 287f Rush Hour Rewards program, 288 shape, 288 289 shed, 288 289 shift, 288 289 shimmy, 288 289 time-of-use (TOU) prices, 286, 287t XCEL Energy’s Windsource Program, 288 distributed energy resources, 285 286 power losses, transmission and distribution in United States, 285f reliability, 285 286 resilience, 285 286 2017 solar eclipse, 284b Grid resiliency, 277 278

H Hawaiian Electric Companies’ Power Supply Improvement Plan, 118 HC. See Hosting capacity (HC) Heating, ventilation, and airconditioning (HVAC) system, 212 Hidden Markov model (HMM), 237 High-performance computing technique, 178 179 Hoover Dam, 297 298 Hoover Powerplant, 297 298

Hosting capacity (HC), 86 88, 89f Hot-start models, 25 Hydroelectricity, 297 298 Hydroelectric power plants, 294 298 Hyperparameters, 243 245, 244t, 245t Hyper plane function, 217

I IEEE 14-bus transmission system, 92 93, 95 IEEE 33-bus system, single line diagram of, 235f IEEE 34-bus test feeder, 271 273 IEEE 57-bus systems, 210 212 IEEE 118-bus systems, 41f, 210 212 IEEE 123-bus system, 64, 84f, 86f, 90 91 DHA process, 90 91 simulation results of voltage inference on, 86f with SMUs, AMUs and PVs on bus 72, 77 and 89, 84f IEEE-DC1A exciter, 180 182 IEEE 8500-node system, 64, 73f, 75 76, 80, 95 frequency control of, 78f with 4 large-scale PVs, 78f, 80 IEEE 33-node test feeder, 44 46 voltage magnitudes and branch MW flows for, 44f IEKF, 186 187, 189 190 Importance Weighted Actor-Learner Architecture (IMPALA), 19 Incentive-based programs, 286, 287f Incremental learning, 8 9 Independent power producer (IPP), 100 101 Independent system operator (ISO), 100 101, 110 112, 119 121, 126, 132, 132f, 135 136, 210 security-constrained unit commitment (SCUC) formulation in, 111 Independent system operator (ISO) New England, 100 101, 127, 210 Integrated distributed electricity system (IDES), 133 136 Intelligent models, 213 214 Interarea oscillation detection, 153, 154f Interconnected microgrids, 261 262

368 Interdependent critical networks, 294 302 combined heat and power, 301 302 energy hubs, 294 power and gas, 295t, 300 301 water energy nexus, 294 300, 297f ancillary services, 298 299 closed-loop systems, 296 297 controlling electric water pumps, 298 299 electricity, 298 hydroelectricity, 297 298 once-through cooling systems, 296 thermoelectric power plants, 296 Venezuela’s Guri dam, 298b International Energy Agency (IEA), 279 Internet, 148 151 Internet-of-Things (IoT), 32, 138, 309 310 Interruptible load (Type I DR), 114 Intrusive sensors, 236 237 Inverter-based distributed generators (DGs), 264 266, 268 273 Inverter-based resources (IBRs), 118, 120 122, 120f, 124, 127 AGC design for, 130f bulk electric power grid dispatch in, 119 120, 120f plant-level control functions, 121 Islanded microgrid with high-penetration of DGs, 74 78, 74f Islanded mode, 262, 264 265, 268 Islanding detection, 154 155, 155f

J Jacobian matrix, 191, 195 measurement function, 167 168, 174

K Keras, 245 Kirchhoff law, 55 56, 68 69, 82 83 Kirchhoff’s Current Laws (KCLs), 193 194 k-nearest neighbor (kNN) point forecasts, 214, 237, 245 100K-node distribution circuits, 93 Kriging-based surrogate modeling technique, 230 231 Kriging modeling, 230 231

Index

100k synthetic circuits, 63f voltage control on, 65f

L Laplace distribution, 226 227 Laplacian probability distribution, 189 191 Large-campus district cooling systems, 135 Large-scale distributed generation (DG) resources, 133 135 Large-scale distribution system, 50 51, 68 Large-scale dynamic systems, 143 144 Large-scale power systems, 165, 229 230 Largest Normalized Lagrange Multiplier (LNLM) test, 165 166 computationally efficient implementation of, 173 177 extraction of Lagrange multipliers, state estimation problem, 166 167 model parameter errors, detection, identification and correction of, 171 173, 173f normalized Lagrange multipliers and hypothesis testing, 167 171 Largest normalized residual (LNR) test, 171 172 Lawrence Berkeley National Laboratory, 288 289 Learning environment action space, 27 28 data-driven models, 27 domain-specific simulators, 27 observation/state space, 28 reward structure, 28 simplified physics models, 26 27 termination condition, 29 Least squares support vector regression (LSSVR) model, 213 214 Linearized measurement model, 167 168 Linear three-phase power flow models, unbalanced distribution systems, 37 39 Line differential protection, 201 204 Load bus, 9 Load-following reserve, 127 128, 128f Load forecasting, 215 Load Serving Entities (LSEs), 105 Load tap changers (LTCs), 66

Index

Locational marginal price (LMP), 24, 103 104, 122 123 Long short-term memory (LSTM), 20 21, 231 232, 237 241, 240f Low-voltage distribution networks, 276

M Machine learning based inertia estimation, 157 159, 159f Machine learning methods, 2, 10 11 building occupancy detection, 235 250 CNN-LSTM deep learning architecture, 238 243 non-intrusive occupancy detection, 236 237 RFID-based occupancy detection system, 236 237 WiFi-based occupancy detection system, 236 237 forecasting, 11 heterogeneous, 10 large volume and high velocity, 11 low value density, 11 machine learning-based control, 229 230 machine learning-based optimization, 230 231 modern forecasting technology, 212 228 categorization, 212 213, 213f deterministic forecasting methods, 213 214 ensemble learning, 214 216 probabilistic forecasting methods, 214 network reconfiguration, 231 234 bus voltage and angle limits, 233 definition of, 231 DLGRU, 231 232, 234, 234f generation units constraints, 233 generation units features, 235t modified IEEE 33-bus test system, 234, 235f power balance constraints, 232 233 reconfiguration constraints, 233 234 operation and control, 11 perception, 11 structured data, 10

369 unstructured data, 10 Machine-to-machine communication, 280 281 MA-OpenDSS, 93 95 Market clearing engine (MCE), 102 103 Markov Decision Process, 3 6, 220 222 action set A, 3 Bellman equations, 5 Bellman expectation equations, 5 Bellman optimal equations, 5 6 definition of, 38 discount factor γ, 4 policy, 4 problems, 6 reward function R, 4 state set S, 3 state transition probability set P, 4 value function, 4 Markov model, 184 MATLAB program, 42 44, 268 269 MATPOWER 5.1, 42 44 Maximum power point tracking (MPPT), 5 7, 37 Max-pooling layer, 239 Measurement errors, 165, 168 169, 172, 177 178 detectability and identifiability of, 177 178 Merging units (MUs), 163 164 Meshed transmission systems, 26 37, 40 41 computational efficiencies, for large-scale systems, 41t derivation and justification of FDLPF, 33 37 2-bus system, 34 35, 34f numerical example, 36 37 theoretical derivation, 35 36 errors of different linear power flow models, 40t IEEE 118-bus system, voltage magnitudes and branch MW flows for, 41f matrix formulation of DLPF model, 30 31 33-node ill-conditioned system (lumped overload), 42f

370 Meshed transmission systems (Continued) 33-node ill-conditioned system (uniform overload), 42f transformers and phase shifters, 31 33 admittance matrix, contribution to, 31 32, 32f branch MW flow, influence on, 32 33 voltage magnitude and phase angle, decoupling of, 26 29, 29f Meta learning, 20 21 metric-based, 21 optimization-based, 21 RNN-based, 20 21 Microgrids, 2 3, 3f clusters, 261 262 controllers, 3, 7 8 DER units, 5 8 fractional order control strategy, 230 231 frequency control reserves, 8 grid-connected mode, 3 islanded mode, 3 Midcontinent independent system operator (MISO), 106 107, 126 Mid-voltage (MV) network, 76 Midwest independent system operator, 100 101 Min-microgrid configuration of, 271f definition of, 270 271 electric boundary of, 270 271 Mixed integer linear programming (MILP), 231 M3-Laplace model, 226 227 Model-free Q-learning, 230 Model parameter errors, 165 Model predictive control (MPC), 30 31 Model time-discretization errors, 183 Monte Carlo simulations, 7 8, 7b, 188 Motivations for dynamic state estimation, 178 180 Multi-agent optimization method, 58 59 Multiagent system, 21 22 competitive, 22 cooperative, 21 22 mixed, 22

Index

Multi-model probabilistic forecasting model, 222 223 Multiple microgrid system, 8 Multisector coupling, 15 17 Multi-variable regression, 214 Municipal aggregation. See Community choice aggregation (CCA)

N National Energy Policy Act, 5 National Renewable Energy Laboratory, 279 280 Natural gas, 295t, 300 301 definition of, 300 301 2018 US natural gas consumption, 300f Natural monopolies, 100 101 NERC Standard VAR-001, 122 Nested microgrids, 261 262 configuration of, 262f management of, 262 Net power injection, 81 Network dynamic operation, 11 15, 12f Networked microgrids with parallel inverters advanced microgrid structures, 261 263 dynamic microgrids, concept of, 263 264 Network sensitivity, 81, 82f, 89f, 91 Neural network (NN) training, 12 computation for, 16 for RL training, 22 Neuro-fuzzy network, 213 214 Newton Raphson solution method, 9 New York independent system operators, 100 101 33-node ill-conditioned system (lumped overload), 42f 33-node ill-conditioned system (uniform overload), 42f Node injection power flow, 53 11,000-node system, 95 100,000-node system, 95 123-node test feeders, 44 46 Nondiscriminatory access, 5 Non-Gaussian distribution, 180 Nonintrusive load monitoring, 292 293 Non-intrusive sensors, 236 237

371

Index

Nonlinear mapping-based kernel methods, 217 Nonlinear oscillator synchronization, 76 Nonlinear regression, Bayesian dynamic state estimation, 184 192 decentralized vs. centralized dynamic state estimation for power system, 191 192 for nonlinear dynamic system models, 184 185 proposed unified framework, 185 188 robustifying, proposed framework for, 188 191 Nonparametric approaches, 214 Nonspinning reserve, 126 Normalized Lagrange multipliers (NLMs), 170 172, 176 178 Normalized mean absolute error (nMAE), 222 224, 224t, 227, 227t Normalized residual (NR), 171 172, 178, 204 Normalized root mean square error (nRMSE), 222 224, 225t, 227, 227t North American grids, 148 NREL-100k system, 93

O Occupancy-based feedback control algorithm, 212 Off-the-shelf black-box tools, 15 16 Ohm’s law, 54 Olney Town Center microgrid project, 262 263 Once-through cooling systems, 296 Online ambient-data-based oscillation modal analysis, 154, 155f Online learning, 8 9 Open Access Same-Time Information System, 100 101 OpenAI, 15 OpenDSS, 80 Operating reserves, 124 125 Operating thermal resource, 118 119 Optimal policy, 6 Optimal power flow (OPF), 8 10, 14 15

P Parallel inverters dynamic microgrids steady-state operation, 264 266 transient-state operation, 266 273 networked microgrids with advanced microgrid structures, 261 263 dynamic microgrids, concept of, 263 264 Parameter errors, 168 169 detectability and identifiability of, 177 178 Parametric approaches, 214 Particle filter (PF), 184 186, 188 190 Peaker plants, 300 301 Peak flexibility, 105 Pearl Street DC system, 4 Pennsylvania American Water, 299 Phase locked loops (PLL), 6 7, 57 58, 76 77 Phasor, 144, 144f, 148 Phasor measurement unit (PMU), 144 146, 151, 163 164, 178 181, 191 192, 210, 280 281 components of, 145f Phasor measurement unit (PMU)-based wide-area measurement system (WAMS), 145 146, 146f Photovoltaics (PVs), 5 7, 6f, 47, 265 connecting with grids, 6f curves, 7, 7f DC output of, 6 7 Pinball loss, 222 228, 226t, 228t PJM Interconnection, 100 101, 307 Point of common coupling (PCC), 261 262 Points of interconnection (POIs), 263 264 Points on wave measurements, 193 194 Policy, 4 gradient theorem, 10 11 Policymakers, 131 132 Polynomial Response Surface (PRS), 230 Polynominal regression model, 215 216 Popular convex approximation, 10 Power and gas, 295t, 300 301 Power-dependent networks, 308

372 Power Power Power Power

electronics (PEs), 2 3 factor control, 64 68 flow analysis, 24 flow and generator stators, 180 181 Power flow calculation, 9 Power generation, 2 Power plant cooling system, 296 Power systems, 1 3, 2f, 16f, 17 community microgrid, 3, 4f development history in United States, 4 5 distribution system, 1 evolution, modernization, 260 261 generation system, 1 microgrids, 2 3, 3f transmission system, 1 Power system stability, 12, 13f Power system stabilizer (PSS), 178 179, 229 230 Power, water and gas networks, increasing connectivity in, 280 283 PQ inverters, 76 77 droop strategy for, 75f Price-based programs, 286, 287f Probabilistic forecasting methods, 214 Probabilistic solar power forecasting, 215 216 Probabilistic wind power forecasting, 215 216 Proximal policy optimization (PPO), 15 16 Public transportation, 305 306 Public Utilities Regulator Policies Act (PURPA), 5 Public Utility Holding Company Act, 5 Public Utility Regulatory Policies Act (PURPA), 100 101 Puerto Rico Energy Public Policy Act, 290 291 Pumped storage hydro (PSH), 110 111

Q Q-learning, 8 9 Q-learning enhanced deterministic forecasting method, 220 223, 227 228

Index

Q-learning reinforcement learning (RL) algorithm, 229 230

R Radial base function neural network (RBFNN), 230 Radial Basis Functions (RBF), 230 Radio-frequency identification (RFID), 236 237 Ramp demand curves (RDCs), 107 108, 108f Ramp-down capacity requirements, 106 107, 115 116 Ramping reserve, 126 Ramp-up capacity requirements, 106 107, 115 117 Random forest (RF), 213 214, 217 218, 224 225, 237 Reactive power control, 51, 57 58, 63 68, 67f, 68f, 75 76, 79 80 Real power control, 68, 79 Real-time closed-loop wide-area decentralized power system stabilizers (WD-PSSs), 229 230 Real-time economic dispatch processes, 121 122 Real-time market operation, 102 104, 103f Real time visualization of measurement data, 152f Rebound effect, 289 Receiver operating characteristic (ROC), 247 249 Recirculating cooling systems, 296 297 Recurrent neural network (RNN), 13, 210 212 Reforming the Energy Vision (REV), 131 132 Regional transmission organizations (RTOs), 100 101 Regulation down reserve, 126 Regulation mileage reserve, 125 Regulation reserves, 125 126 Reinforcement learning (RL), 210 actor-critic, 11 Bellman equations, 3 6 curriculum learning, 19 20

373

Index

deep reinforcement learning A3C, 14 16 DQN, 12 14 in energy systems, 23 40 combining prior knowledge, 38 controller training and evaluation, 38 demand response, 33 35 grid operation, 35 37 limitations and challenges, 39 multiagent, 39 proper RL/MDP formulation, 38 renewable generation and battery control, 37 smart buildings, 30 33 transfer learning, 38 evolution strategies-based, 16 18, 17b examples of, 3b learning environment action space, 27 28 data-driven models, 27 domain-specific simulators, 27 observation/state space, 28 reward structure, 28 simplified physics models, 26 27 termination condition, 29 Markov decision process, 3 6 action set A, 3 Bellman equations, 5 Bellman expectation equations, 5 Bellman optimal equations, 5 6 discount factor γ, 4 policy, 4 problems, 6 reward function R, 4 state set S, 3 state transition probability set P, 4 value function, 4 meta learning, 20 21 metric-based, 21 optimization-based, 21 RNN-based, 20 21 Monte Carlo, 7 8 multiagent system, 21 22 competitive, 22 cooperative, 21 22 mixed, 22 policy-based, 9 11

scalable reinforcement learning frameworks, 18 19 temporal difference, 8 9 value function approximation, 9 Reliability coordination, 122 Renewable energy credit (REC), 288 Renewable energy resources, 106 Renewable generation, 37 Residential distribution networks and feeders, 304 Resource adequacy analysis, 104 105 Resource commitment (RSC) application procedure, 102 103 Resource dispatch system, 137 Resource ramping capability constraints, 110 Retail electricity market blockchain technology in electric power systems, 138 139, 139f design of, 133 138 state-of-the-art for development, 130 132 Retail electric providers (REPs), 131 132 Retail market operators, functions, 99 100 Runge Kutta-based methods, 183 Rural Electrification Act, 5 Rush Hour Rewards program, 288

S Sandworm, 293 294 SARSA learning, 8 9, 8b Scalable distributed feedback control algorithms, 14 15 Scalable reinforcement learning frameworks, 18 19 Scale estimator, 189 190 Scikit-learn libraries, 245 Security assessment (SA), 103 104 Security-constrained economic dispatch (SCED), 103 104, 123 124 Security-constrained optimal power flow (SCOPF), 103 104 Security-constrained unit commitment (SCUC), 24, 111 112, 115, 123 124 Semi-definite program (SDP), 10 Semidefinite relaxation, 10

374 Sensitivity matrix, 88 Sequential Monte Carlo (SMC) method, 136 137 Shape, demand response, 288 289 Shed, demand response, 288 289 Shift, demand response, 288 289 Shimmy, demand response, 288 289 Short-term energy, 99 100 Short-term solar forecasting method, 213 214 Short-term wind power forecasting method, 213 214 Shunt elements, 27 Siamese neural network, 21 Sigmoid and hyperbolic tangent, 216 Simulink, 268 269 Simultaneous feasibility test (SFT), 102 103 Sinusoial/cosine function, 144f Slack bus, 9 Smart electricity meters, 281 282 Smart grid, 211f, 260, 280 281, 292 293 Smart Grids European Technology Platform, 260 Smart meters, 281 Smart switches (SSWs), 263 264, 270 Smart water meters, 281 282 SMUs. See Synchronous measurement and control units (SMUs) 2017 solar eclipse, 284b Solar forecasting, 215 Solar photovoltaic (PV) and battery storage, 99 100 Solvable second-order cone program (SOCP), 10 Southern California Edison (SCE) territory, 289 Southern California Gas Company’s Smart Therm DR program, 301 Southern Power Pool (SPP) system, 118, 126 Southwestern Power Pool, 100 101 Spatio-temporal correlation, 214 Spinning reserve, 125, 127 State estimation, 210 212 State estimation, advanced grid operational tools

Index

differential and algebraic equations (DAEs), 164 dynamic state estimation (DSE) algorithms, 164 model validation, 165 178 erroneous parameters from large model dataset, 165 largest normalized Lagrange multiplier test, 166 173 measurement errors, 165, 177 178 model parameter errors, 165 parameter errors, detectability and identifiability of, 177 178 protective relaying, 192 204 challenges of, 193 dynamic state estimation based protection, 193 196 event study, 201 204, 202f, 203f legacy protection functions, 201 series compensated transmission line, 197 static state estimation, 164 system monitoring, 178 192 Bayesian dynamic state estimation, nonlinear regression, 184 192 motivations for dynamic state estimation, 178 180 problem formulation of dynamic state estimation, 180 183 State evolution, probabilistic model of, 184 185 State-of-charge (SOC), 110 112 State-of-the-art artificial intelligence techniques, 231, 235 236 State-space model, 182 Static microgrids, 263 264, 264f Static security assessment, 103 104 Static state estimator (SSE), 164, 178 180 Statistical methods, 213 214 Steady-state operation, 8 10, 178 180 AC OPF, 9 10 definition of, 8 parallel inverter operation in microgrids operational constraints, 265 physical constraints, 265 renewable penetration, maximization of, 265

375

Index

system operation reliability maximization, 265 system topology optimization problem, 266 topology constraints, 265 transmission line loss minimization, 265 unbalance constraints, 265 popular convex approximation, 10 power flow calculation, 9, 9f Stiff source bus, 77f, 80 Stochastic gradient descent (SDG), 244 245 Stochastic sampling techniques, 137 Structured data, 10 SUMMER-GO, 210 Super peak flexibility, 105 Supervisory control and data acquisition (SCADA) system, 137 138, 143 144, 146, 146f, 178 180. See also Supervisory control and data acquisition (SCADA) system Supplemental reserves, 126 127 Support vector machine (SVM), 210 212, 217, 230, 237, 245 Support vector regression (SVR), 213 214, 217, 224 225 Synchronized reserve. See Spinning reserve Synchronized sensors, 10 Synchronous generators, 266 Synchronous measurement and control units (SMUs), 79, 81, 84f, 87 Synchrophasor measurement technology advanced control, 146 phasor, 144, 144f phasor measurement unit (PMU), 144 145 sinusoidal/cosine function, 144f situational awareness, 146 Synthetic 100,000-node NREL System, 93 System-and cluster-level real/reactive power control, 75 76 System transient stability analysis, 14 System-wide power balance constraint, 108 109 System-wide ramp capacity requirement (RCR), 106 107

T Temporal difference (TD) learning, 8 9 SARSA learning, 8 9, 8b Tensorflow, 245 Tesla Model S, 304 Texas Public Utility Commission, 131 132 TGOV1 turbine-governor, 180 182 Thermal generators, 124, 126 127 Thermoelectric power plants, 294 296 Time-domain numerical integration, 13 14 Time-of-use (TOU) prices, 286, 287t, 288 289 Timescale of power system studies, 12f Total installed electricity meters on residential and commercial buildings, 282f Traditional hosting analysis, 86 87 Transfer learning, 33, 38 Transformers and phase shifters, meshed transmission systems, 31 33 admittance matrix, contribution to, 31 32, 32f branch MW flow, influence on, 32 33 Transient-state operation, parallel inverter operation in microgrids, 266 273 advanced inverter control, 269 273, 272f, 273f improved inverter operation performance, 268 269 inverter dynamic stability, 266 268 Transmission constraints, 109, 124 Transmission constraints management (TCM), 103 104 Transmission systems, 1, 122 errors of different linear power flow models, 40t Transportation sector, electrification, 303 308 consumer vehicles, 303 304, 304f public transportation, 305 306 rideshare services and emerging methods, 306 307 school bus, 305b vehicle-to-grid (V2G) system, 307 308 Trapezoidal methods, 183

376 True negative rate, 246 247 True positive rate/recall, 246 247 Trust region policy optimization (TRPO), 15 16 Type I demand response resource, 114, 116 117 Type II demand response resource, 114 116, 115f

U Uber’s JUMP program, 305 306 Unbalanced distribution systems, 44 46 linear three-phase power flow models, 37 39 Unconstraint optimization method, 195 Unscented Kalman filter (UKF), 184 187, 189 190 Unstructured data, 10 Unsynchronized sensors, 10 US Department of Energy (DOE), 260 U.S. Energy Information Administration (EIA), 30 31 Utility-scale inverter-based resources, control functions vs. dispatch functions for, 122t Utility-scale plants, 119 120

V Value function, 4 approximation, 9 Vanilla policy gradient (VPG) algorithm, 30 Vehicle-to-grid (V2G) system, 307 308 Venezuela’s Guri dam, 298b VGGNet, 241, 243 244 Virtual leader (VL), 69 72, 70f Virtual synchronous generator model, 77f, 80 Virtual synchronous machine (VSM), 76, 80 Voltage and frequency (VF) inverters, 76 77 Voltage inference method, 79 80, 80f Voltage sensitivity, real and reactive power injections, 85f Voltage source inverter (VSI), 76 Voltage/Var control, 68

Index

Volt/VAR optimization, 138 VSM. See Virtual synchronous machine (VSM)

W Water and natural gas networks, 294, 295t Water energy nexus, interdependent critical networks, 294 300, 297f ancillary services, 298 299 closed-loop systems, 296 297 controlling electric water pumps, 298 299 electricity, 298 hydroelectricity, 297 298 once-through cooling systems, 296 thermoelectric power plants, 296 Venezuela’s Guri dam, 298b Watt-Sun, 210 Weather-related power outages, 290, 290t Weighted least squares (WLS) state estimation problem, 166 168, 171 174 Wholesale electricity market all zero marginal cost resources, 122 123 bulk electric power grid dispatch in inverter-based resources systems, 119 120, 120f driving forces of electricity market development, 100 102 frequency response mechanism design, 128 129, 129f increased complexity for controls of utility-scale inverter-based resources, 121 122, 122t increased uncertainty and variability, 120 121 motivations, 117 119, 119f new products and designs in considering flexibility in resource adequacy, 104 106 demand response resources market modeling, 114 117 energy storage market modeling, 110 114 flexible ramping products, 106 110 operation process of, 102 104

377

Index

reserve requirements, determination of, 124 128 unit commitment and economic dispatch for up to 100% renewables, 123 124 Wide-area measurement system (WAMS), 145 FNET/GridEye, 147 151 architecture of, 147, 147f disturbance detection and location, 152 153, 153f event-data-based oscillation modal analysis, 153, 154f event replay and postevent analysis, 156, 156f FDR deployment in North America, 148, 149f first generation of FDR, 148 151, 150f hardware component, 147 interarea oscillation detection, 153, 154f islanding detection, 154 155, 155f machine learning based inertia estimation, 157 159, 159f model validation and parameter verification, 157, 158f North American grids, 148

online ambient-data-based oscillation modal analysis, 154, 155f second generation of FDR, 148 151, 150f software component, 147 statistical analysis of historical data, 156 157, 157f visualization, 154 worldwide power grid monitored, 148, 149f structure, 145, 145f WiFi-based occupancy detection system, 236 237 Wildfire mitigation plan, 291 Wind forecast improvement project (WFIP), 210 WindView, 210 Wireless communication technologies, 280 283

X Xavier method, 243 244 XCEL Energy, 281 282, 286, 287t Windsource Program, 288

Z Zero marginal cost resources, 122 123 ZigBee technology, 280 283