Smart Cyber-Physical Power Systems, Volume 2

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 xxiList of Contributors xxvForeword (John D. McDonald) xxxiForeword (Massoud Amin) xxxiiiPreface for Volume 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies xxxviiAcknowledgments xxxix1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation 1Ali Parizad and Constantine Hatziadoniu1.1 Introduction 11.2 Cyber-attacks on the State Variables of the Power System 21.3 Information Theory 41.4 Gray Level Transformation 61.5 Linear Transformation 71.6 Logarithmic Transformations 71.7 Power-Law Transformations 71.8 Simulation Results 81.9 Conclusion 44References 452 Artificial Intelligence and Machine Learning Applications in Modern Power Systems 49Sohom Datta, Zhangshuan Hou, Milan Jain, and Syed Ahsan Raza Naqvi2.1 The Need for AI/ML in Modern Power Systems 492.2 AL/ML Algorithms in Power System Applications 492.3 AI/ML-Based Applications in the Electricity Grid 522.4 Future of AI/ML in Power Systems 61References 623 Physics-Informed Deep Reinforcement Learning-Based Control in Power Systems 67Ramij Raja Hossain, Qiuhua Huang, Kaveri Mahapatra, and Renke Huang3.1 Introduction 673.2 Overview of RL/DRL 693.3 Grid Control Perspectives 703.4 Importance of Physics-Informed DRL in Grid Control and Different Methods 713.5 Grid Control Applications of Physics-Informed DRL 723.6 Discussion and Research Directions 743.7 Conclusions 75References 754 Digital Twin Approach Toward Modern Power Systems 79Sabrieh Choobkar4.1 Digital Twin Concept 794.2 Digital Twin: The Convergence of Recent Technologies 844.3 Cyber-Physical System and Digital Twin 874.4 Novelties and Suggestions of Digital Twin to Smart Grid Subsystems 884.5 Conclusions 90References 905 Application of AI and Machine Learning Algorithms in Power System State Estimation 93Behrouz Azimian, Reetam Sen Biswas, and Anamitra Pal5.1 Introduction 935.2 Motivation and Theoretical Background 955.3 DNN Architecture for DSSE and TI 975.4 SMD Measurement Selection for DSSE and TI 985.5 Smart Meter Data Consideration 1045.6 Implementation of DNN-Based TI and DSSE 1145.7 Conclusion 126Acknowledgment 127Appendix 127References 1286 ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings 131Mahoor Ebrahimi, Mahan Ebrahimi, Miadreza Shafie-khah, Hannu Laaksonen, and Pierluigi Siano6.1 Introduction 1316.2 Problem Formulation 1326.3 Application of AI in Energy Management of Smart Homes 1376.4 Simulation and Results 1396.5 Conclusion 145References 1467 Protection Challenges and Solutions in Power Grids by AI/Machine Learning 149Ali Bidram7.1 Introduction 1497.2 Zonal Setting-Less Modular Protection Using ml 1507.3 Traveling Wave Protection of dc Microgrids Using ml 1597.4 Conclusion 168References 1688 Deep and Reinforcement Learning for Active Distribution Network Protection 171Mohammed AlSaba and Mohammad Abido8.1 Introduction and Motivation 1718.2 Problem Statement 1738.3 Proposed Methodology for Fault Detection and Classification 1778.4 Case Study and Implementation 1788.5 Results and Discussion 1808.6 Hardware in-the-Loop Testing 1868.7 Conclusion 186Acknowledgments 187References 1879 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes 189Meghana Ramesh, Jing Xie, Monish Mukherjee, Thomas E. McDermott, Anjan Bose, and Michael Diedesch9.1 Introduction 1899.2 Intelligent Modeling and Its Applications 1909.3 Case Study 1939.4 Conclusions 206Acknowledgment 206References 20710 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation 209Yingqi Liang, Junbo Zhao, and Dipti Srinivasan10.1 Introduction 20910.2 Challenges for Using Big Data Techniques in Smart Grids 20910.3 Solutions Using Big Data Techniques for Smart Grid Situational Awareness 21110.4 Applications of Big Data Techniques for Smart Grid Operation 22810.5 Numerical Results 23110.6 Concluding 250References 25111 Data-Driven Methods in Modern Power System Stability and Security 255Jinpeng Guo, Georgia Pierrou, Xiaoting Wang, Mohan Du, and Xiaozhe Wang11.1 Introduction 25511.2 Data-Driven Wide-Area Damping Control 25611.3 Data-Driven Wide-Area Voltage Control 26611.4 Data-Driven Inertia Estimation for Frequency Control 27411.5 A Data-Driven Polynomial Chaos Expansion Method for Available Transfer Capability Assessment 28411.6 Using PCE to Assess the Ramping Support Capability of a Microgrid 297References 30512 Application of Quantum Computing for Power Systems 313Yan Li, Ganesh K. Venayagamoorthy, and Liang Du12.1 Quantum Computing in Renewable Energy Systems 31312.2 Quantum Approximate Optimization Algorithm for Renewable Energy Systems 31612.3 Typical Applications of Quantum Computing 319Acknowledgment 320References 32013 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts 323Zejia Jing, Ali Parizad, and Saifur Rahman13.1 Introduction 32313.2 Principles and Concepts of Building Hourly Energy Consumption Forecasting 32513.3 Conclusion 359References 35914 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results 363Zejia Jing, Ali Parizad, and Saifur Rahman14.1 Introduction 36314.2 Case Study and Result of Building Hourly Energy Consumption Forecasting 36414.3 Building Occupancy Measurement 39414.4 Conclusion 40915 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems 417Zejia Jing, Ali Parizad, and Saifur Rahman15.1 Introduction 41715.2 PV Energy Forecasting 41815.3 Conclusion 447References 44716 An Intelligent Reinforcement-Learning-Based Load Shedding to Prevent Voltage Instability 449Pouria Akbarzadeh Aghdam, Hamid Khoshkhoo, and Ahmad Akbari16.1 Introduction 44916.2 Stability Control Methods 45016.3 Characteristics of Optimal Stability Controller 45116.4 Utilizing Reinforcement Learning for Enhancing Voltage Stability 45216.5 Taxonomy of RL 45516.6 Proposed Algorithm 45616.7 Reinforcement Learning Algorithm Components 45616.8 Algorithm Implementation Process 45816.9 Simulations and Results 46016.10 Scenario I 46216.11 Scenario II 46316.12 Scenario III 46516.13 Conclusion 466References 46617 Deep Learning Techniques for Solving Optimal Power Flow Problems 471Vassilis Kekatos and Manish K. Singh17.1 Introduction 47117.2 Sensitivity-Informed Learning for OPF 47317.3 Deep Learning for Stochastic OPF 48717.4 Conclusions 497References 49718 Research on Intelligent Prediction of Spatial–Temporal Dynamic Frequency Response and Performance Evaluation 501Xieli Sun, Longyu Chen, and Xiaoru Wang18.1 Introduction 50118.2 Modeling Process and Evaluation Method 50318.3 Case Study 51518.4 Conclusion 522References 52219 Emerging Technologies and Future Trends in Cyber-Physical Power Systems: Toward a New Era of Innovations 525Ali Parizad, Hamid Reza Baghaee, Vahid Alizadeh, and Saifur Rahman19.1 Introduction 52519.2 Paradigm Shifts in Power Transmission and Management 52619.3 Innovations in Electric Mobility and Sustainable Transportation 53019.4 Digital Transformation and Technological Convergence in Cyber-Physical Power Systems 53019.5 Cyber-Physical Systems Enhancing Societal Well-Being 53919.6 Toward a Decentralized and Automated Future 54019.7 Overcoming Challenges with Advanced Technologies 54119.8 Revolutionizing Modern Power Systems with Real-Time Simulators 54719.9 Emerging Trends Shaping the Future Energy Landscape 54919.10 Conclusion 552References 553Index 567