Ali Parizad, PhD, is a Postdoctoral Associate at the Advanced Research Institute (ARI) of Virginia Polytechnic Institute and State University, VA, USA. Leveraging his extensive academic background, he served as a Senior Data Scientist in the IDA Data Science & Machine Learning (DSML) Department at Shell Energy. He holds the position of Staff Power Systems Machine Learning Engineer at Thinklabs AI, where he tackles critical challenges in power systems with cutting-edge AI applications. Hamid Reza Baghaee, PhD, is an Associate Research Professor at Amirkabir University of Technology, Tehran, Iran. Saifur Rahman, PhD, is the founding director of the Advanced Research Institute at Virginia Tech, where he is the Joseph R. Loring Professor of Electrical and Computer Engineering.

• About the Editors xxvList of Contributors xxixForeword (John D. McDonald) xxxviiForeword (Massoud Amin) xxxixPreface for Volume 1: Smart Cyber-Physical Power Systems: Fundamental Concepts, Challenges, and Solutions xliiiAcknowledgments xlv1 Overview of Smart Cyber-Physical Power Systems: Fundamentals, Challenges, and Solutions 1Ali Parizad, Hamid Reza Baghaee, and Saifur Rahman1.1 Introduction 11.2 Structural Overview and Roadmap of the Book 11.3 General Concepts of the Cyber-Physical Systems 71.4 Cyber-Physical Energy and Power Systems (CPEPSS) 161.5 From Conventional Distribution Networks to Smart Grids 211.6 Smart Grid Ecosystem (From Smart Buildings to Smart Grid) 421.7 Cybersecurity in Modern Power Systems 541.8 Conclusions 63References 642 Global Demand Response Status: Potentials, Barriers, and Solutions 71Sanchari Deb, Elahe Doroudchi, Sergio Motta, Matti Aro, and Amir Safdarian2.1 Background 712.2 Global Status of DR Programs 722.3 AI and ML Applications in DR 752.4 Case Study 772.5 Discussion 81References 813 Smart Power/Energy Management and Optimization in Microgrids 85Talal Saleh, Omar Mohamed, Seyed Farhad Zandrazavi, and Miadreza Shafie-khah3.1 Introduction 853.2 Materials and Methods 863.3 Simulation and Results 913.4 Discussion 943.5 Conclusions 95References 964 Smart City Energy Infrastructure as a Cyber-Physical System of Systems: Planning, Operation, and Control Processes 99Mahdi Nozarian, Alireza Fereidunian, and Masoud Barati4.1 Introduction 994.2 Cyber-Physical System of Systems 1004.3 Cyber-Physical System of System Application Domains 1034.4 Smart City Cyber-Physical System of Systems 1064.5 Smart City Energy Cyber-Physical System of Systems 1094.6 Planning, Operation, and Control Process in Smart City Energy Cyber-Physical System of Systems 1124.7 Emergence in Smart City Energy Cyber-Physical System of Systems 1154.8 Conclusions 116References 1175 Metaverse Local Energy Market in Smart City: A Descriptive Model and Strategic Development Analysis 125Mohammad Ghafourian Nasiri, Zahra Iranpour Mobarakeh, Mahdi Nozarian, Alireza Fereidunian, Sabrieh Choobkar, and Hossein Jobran5.1 Introduction 1255.2 Background 1265.3 Concepts 1275.4 Case Study: Local Energy Market in Metaverse 1295.5 Discussions and Conclusions 132References 1336 Cooperative and Distributed Control Strategies of Microgrids 135Mahmood Jamali and Mahdieh S. Sadabadi6.1 Introduction 1356.2 Fault-Tolerant Secondary Control Schemes in Islanded AC Microgrids 1376.3 Finite-Time Fault-Tolerant Voltage Control 1406.4 Case Studies 1436.5 Concluding Remarks 148References 1497 Interconnected Microgrid Systems: Architecture, Hierarchical Control, and Implementation 151Tung Lam Nguyen, Yu Wang, Ha Thi Nguyen, and Tran The Hoang7.1 Introduction 1517.2 Architecture 1527.3 Hierarchical Control of Interconnected MGs 1537.4 The Multi-Agent System 1577.5 The Implementation on a Real-Time Cyber-Physical Testbed 1587.6 Conclusions 164References 1658 Internet of Energy, and Internet of Microgrids (IoE, IoM) 167Jonatas Boas Leite and Mladen Kezunovic8.1 Introduction 1678.2 Interfacing of the IoT Node for Self-Healing Strategies 1688.3 Performance Assessment Results 1768.4 Concluding Remarks 183References 1839 Voltage Regulation and Reactive Power Optimization for Integration of Distributed Energy Resources into Smart Grids 187Firdous Ul Nazir, Bikash C. Pal, and Rabih A. Jabr9.1 Introduction 1879.2 Traditional Volt/Var Control 1889.3 Network Model 1899.4 Chance-Constrained Volt/Var Control 1909.5 Solution Algorithm 1929.6 Results 1959.7 Approximate Load Models for Advanced VVC Functions 1979.8 Binomial Approximation Method 1989.9 Linear Regression Method 1999.10 Results 2009.11 Conservation Voltage Reduction 2029.12 Conclusions 202References 20310 The Role of Data Analysis in Hosting Capacities of Distribution Power Systems for Electric Vehicles 207Alireza Ghadertootoonchi, Mehdi Davoudi, Mohaddeseh Koochaki, and Moein Moeini-AghatieNomenclature 20710.1 EVs’ Power Demand Forecast Methods 20810.2 Review of EVs’ Energy Management Strategies 21210.3 Uncertainties Regarding EVs and Their Impact on the Power Networks 21710.4 Data Analyses Application in Technical Issues of EVs 22510.5 Concluding Remarks 235References 23611 Energy Efficiency in Smart Buildings Through IoT Sensor Integration 247Saifur Rahman and Ali Parizad11.1 Introduction 24711.2 Building Automation Solution Landscape 25211.3 Bemoss Tm Features 25311.4 Targeted Buildings and Loads 25611.5 BEMOSS TM Architecture 26111.6 BEMOSS TM Auxiliary Functions 26611.7 Multiple-protocol Interoperability 26711.8 Test Results 26811.9 BEMOSS TM Platform for Campus Applications 28911.10 Conclusion 29011.11 Exploring Other Capabilities of the BEMOSS TM Platform 290References 29012 Optimal Dispatch of Smart Energy System Based on Cyber–Physical–Social Integration 293Jizhong Zhu, Ziyu Chen, Wanli Wu, and Chenke He12.1 Introduction 29312.2 CPSS Model 29412.3 The Cooperative Operation in V2G 30212.4 Framework of a Charging Station with Battery Swapping Mode 30712.5 Conclusion 313References 31313 Power Distribution Systems Self-Healing 315Konrad Schmitt, Manohar Chamana, Meisam Mahdavi, Stephen Bayne, and Luciane Neves13.1 Introduction 31513.2 Historical Notes 31613.3 Self-Healing Concept 31913.4 Mathematical Formulation 32313.5 Case Studies 33013.6 Concluding Remarks 338References 33914 Resiliency, Reliability, and Security of Cyber-Physical Power System 343Mohsen Chegnizadeh, Mahmoud Fotuhi-Firuzabad, and Sajjad Fatahian dehkordiAbbreviations 34314.1 Introduction and Motivation 34414.2 Conceptual and Definitional Studies 34614.3 Application of Machine Learning in Power Systems 35014.4 Case Study 35514.5 Conclusion 360Acknowledgments 360References 36015 Cyberattacks on Power Systems 365Alfan Presekal, Vetrivel Subramaniam Rajkumar, Alexandru Ştefanov, Kaikai Pan, and Peter Palensky15.1 Introduction 36515.2 Cyber Kill Chain 36615.3 Review of Major Cyberattacks 36815.4 Taxonomy of Cyberattacks on Power Grids 37415.5 Impact of Cyberattacks on Power Grids 38915.6 Study Case and Simulation Results 39115.7 Conclusion 393Acknowledgement 394List of Acronyms 396References 39816 Vulnerabilities of Machine Learning Algorithms to Adversarial Attacks for Cyber-Physical Power Systems 405Tapadhir Das, Raj Mani Shukla, Mohammed Ben-Idris, and Shamik Sengupta16.1 Introduction 40516.2 Vulnerabilities of ML Algorithms to Adversarial Attacks 40716.3 Theoretical Foundations and Applications of Adversarial Attacks 41216.4 Attack Models Under Different Scenarios Including Full, Limited, and No Knowledge About the Target Model 41416.5 Real-Life Practical Adversarial Example Generation and Implementation in CPPS 41716.6 Protection Strategies Against Adversarial Attacks 41816.7 Conclusion and Recommendation 421References 42217 Synchrophasor Data Anomaly Detection for Wide-Area Monitoring and Control in Cyber-Power Systems 425A.K. Srivastava, S. Pandey, A. Ahmed, S. Basumalik, and S.K. Sadanandan17.1 Introduction 42517.2 Synchrophasor-Based Wide-Area Monitoring and Control 42617.3 Synchrophasor Data Flow, Anomalies, and Impacts 42717.4 Synchrophasor Anomalies Detection and Classification (SyADC) 42917.5 Quality-Aware Synchrophasor-Based Monitoring and Control Applications 44117.6 Summary 445Acknowledgements 446References 44618 Application of State Observers and Filters in Protection and Cyber-Security of Power Grids 451Mohammadmahdi Asghari, Amir Ameli, Mohsen Ghafouri, and Mohammad N. Uddin18.1 Introduction 45118.2 State–Space Model of Systems 45218.3 Properties of State–Space Models 45418.4 State Observers and Filters 45518.5 Application of Observers and Filters in Improving the Authenticity and Accuracy of Measured Data 46718.6 Case Study 1: Attack Detection and Identification for Automatic Generation Control Systems 46918.7 Case Study 2: Developing Wide-Band Current Transformers for Traveling-wave-based Protection 48018.8 Case Study 3: Fault Diagnosis in Transformers Using LPV Observers 48918.9 Conclusion 498References 49819 Anomaly Detection and Mitigation in Cyber-Physical Power Systems Based on Hybrid Deep Learning and Attack Graphs 505Alfan Presekal, Alexandru Ştefanov, Vetrivel Subramaniam Rajkumar, and Peter PalenskyAbbreviations 50519.1 Power Grid Cyber Resilience 50619.2 Operational Technologies and Secure Communication Protocols 50819.3 Cyber-Physical System Co-Simulation and Cyber Ranges 51219.4 Network Security Controls 51619.5 Hybrid Deep Learning for Anomaly Detection in Power System OT Networks 52119.6 Hybrid Deep Learning Model for Anomaly Detection 52219.7 Attack Graph for Situational Awareness 52519.8 Cyber Attack Case Studies 52719.9 Conclusions 531Acknowledgments 531References 53220 Attack Detection and Countermeasures at Edge Devices 539Fahim Ahmed and Md Tanvir Arafin20.1 Introduction 53920.2 Attack Surfaces for Edge Devices 54020.3 Security Issues and Common Attacks in Edge Devices 54320.4 Attack Detection Techniques and Countermeasures 54720.5 Conclusions and Future Research Directions 550Acknowledgments 550References 55121 Privacy-Preserving Outage Detection in Modern Distribution Grids: Challenges and Opportunities 555Chenhan Xiao, Yizheng Liao, and Yang Weng21.1 Introduction 55521.2 Preliminaries 55721.3 Privacy-Aware Line Outage Detection with Boosted Performance 55921.4 Validation on Extensive Outage Scenarios with Real-World Data 56521.5 Conclusions 572References 57222 Transactive Energy Management and Distribution System Reform Using Market Concepts 575Amr A. Mohamed, Bala Venkatesh, Carlos Sabillon, and Ali GolrizNomenclature 57522.1 Introduction 57622.2 Proposed TEM Market Platform 57822.3 Demonstrative Case Studies 58322.4 Conclusion Remarks and Prospects for the Future 590References 591Appendix 22.A Line Segment Data of the 34-bus Test System (ohms) 59323 Transactive Energy Systems in Decentralized Autonomous Renewable Energy Communities 597Riccardo Trevisan, Emilio Ghiani, Marco Galici, Susanna Mocci, and Fabrizio Pilo23.1 Introduction 59723.2 RECs as DAOs 59923.3 Toward the Tokenization of the Governance 60223.4 Conclusions 612References 61224 Transactive Coordination Paradigm for Efficient Charging Management of Plug-in Electric Vehicles in Future Distribution Networks 617Hossein Saber, Hossein Ranjbar, and Moein Moeini-Aghtaie24.1 Introduction 61724.2 Transportation Electrification 61824.3 Demand-Side Management Approaches 62024.4 Examples of TE Model Worldwide Projects 62224.5 TE Paradigm in Charging Management of EVs 62524.6 Conclusions and Future Works 630References 63125 Optimal Peer-to-Peer Energy Trading Using Machine Learning: Architecture, Strategies, and Algorithms 635Nadya Noorfatima and Jaesung Jung25.1 Introduction 63525.2 P2P Energy Trading Architecture 63625.3 ml Operation in P2P Energy Trading 64625.4 Simulation 65025.5 Conclusion 654References 65426 Optimal Peer-to-Peer Power Sharing in DC Islanded Microgrids 657Rabia Khan and Noel N. Schulz26.1 Introduction 65726.2 Modeling of Islanded DC Microgrid System 65926.3 Optimal Power Flow Problem Formulation of DC Islanded Microgrid System 66226.4 Results and Discussion 66426.5 Conclusion and Future Work 673Acknowledgment 675Bibliography 67527 Blockchain-Based Energy Trading Employing Hyperledger and Anomaly Detection Algorithms 679Zejia Jing, Ali Parizad, and Saifur Rahman27.1 Introduction 67927.2 Literature Review 68027.3 Anomaly Detection 68827.4 Blockchain-Based Anomaly Detection Case Study 70327.5 Conclusion 709References 71028 Optimal Coordination of VSC-Interfaced Subsystems to Safeguard the Frequency Performance of Cyber-Physical Power Systems 715Georgios Giannakopoulos, Arcadio Perilla Guerra, José Luis Rueda Torres, and Peter Palensky28.1 Motivation and Scope of the Chapter 71528.2 The HVDC–HVAC Cyber-Physical Test Power System 71628.3 Statement of the Optimization of FFC for PEI 71928.4 Solution by Mean–Variance Optimization 72028.5 Simulation Analysis 72228.6 Concluding Reflections 724References 725Index 727