About the Editors -- List of Contributors -- Foreword (John D. McDonald) -- Foreword (Massoud Amin) -- Preface for Volume 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies -- Acknowledgments -- 1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation 1 Ali Parizad and Constantine Hatziadoniu -- 1.1 Introduction -- 1.2 Cyber-attacks on the State Variables of the Power System -- 1.3 Information Theory -- 1.4 Gray Level Transformation -- 1.5 Linear Transformation -- 1.6 Logarithmic Transformations -- 1.7 Power-Law Transformations -- 1.8 Simulation Results -- 1.9 Conclusion -- References -- 2 Artificial Intelligence and Machine Learning Applications in Modern Power Systems 49 Sohom Datta, Zhangshuan Hou, Milan Jain, and Syed Ahsan Raza Naqvi -- 2.1 The Need for AI/ML in Modern Power Systems -- 2.2 AL/ML Algorithms in Power System Applications -- 2.3 AI/ML-Based Applications in the Electricity Grid -- 2.4 Future of AI/ML in Power Systems -- References -- 3 Physics-Informed Deep Reinforcement Learning-Based Control in Power Systems 67 Ramij Raja Hossain, Qiuhua Huang, Kaveri Mahapatra, and Renke Huang -- 3.1 Introduction -- 3.2 Overview of RL/DRL -- 3.3 Grid Control Perspectives -- 3.4 Importance of Physics-Informed DRL in Grid Control and Different Methods -- 3.5 Grid Control Applications of Physics-Informed DRL -- 3.6 Discussion and Research Directions -- 3.7 Conclusions -- References -- 4 Digital Twin Approach Toward Modern Power Systems 79 Sabrieh Choobkar -- 4.1 Digital Twin Concept -- 4.2 Digital Twin: The Convergence of Recent Technologies -- 4.3 Cyber-Physical System and Digital Twin -- 4.4 Novelties and Suggestions of Digital Twin to Smart Grid Subsystems -- 4.5 Conclusions -- References -- 5 Application of AI and Machine Learning Algorithms in Power System State Estimation 93 Behrouz Azimian, Reetam Sen Biswas, and Anamitra Pal -- 5.1 Introduction -- 5.2 Motivation and Theoretical Background -- 5.3 DNN Architecture for DSSE and TI -- 5.4 SMD Measurement Selection for DSSE and TI -- 5.5 Smart Meter Data Consideration -- 5.6 Implementation of DNN-Based TI and DSSE -- 5.7 Conclusion -- Acknowledgment -- Appendix -- References -- 6 ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings 131 Mahoor Ebrahimi, Mahan Ebrahimi, Miadreza Shafie-khah, Hannu Laaksonen, and Pierluigi Siano -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.3 Application of AI in Energy Management of Smart Homes -- 6.4 Simulation and Results -- 6.5 Conclusion -- References -- 7 Protection Challenges and Solutions in Power Grids by AI/Machine Learning 149 Ali Bidram -- 7.1 Introduction -- 7.2 Zonal Setting-Less Modular Protection Using ml -- 7.3 Traveling Wave Protection of dc Microgrids Using ml -- 7.4 Conclusion -- References -- 8 Deep and Reinforcement Learning for Active Distribution Network Protection 171 Mohammed AlSaba and Mohammad Abido -- 8.1 Introduction and Motivation -- 8.2 Problem Statement -- 8.3 Proposed Methodology for Fault Detection and Classification -- 8.4 Case Study and Implementation -- 8.5 Results and Discussion -- 8.6 Hardware in-the-Loop Testing -- 8.7 Conclusion -- Acknowledgments -- References -- 9 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes 189 Meghana Ramesh, Jing Xie, Monish Mukherjee, Thomas E. McDermott, Anjan Bose, and Michael Diedesch -- 9.1 Introduction -- 9.2 Intelligent Modeling and Its Applications -- 9.3 Case Study -- 9.4 Conclusions -- Acknowledgment -- References -- 10 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation 209 Yingqi Liang, Junbo Zhao, and Dipti Srinivasan -- 10.1 Introduction -- 10.2 Challenges for Using Big Data Techniques in Smart Grids -- 10.3 Solutions Using Big Data Techniques for Smart Grid Situational Awareness -- 10.4 Applications of Big Data Techniques for Smart Grid Operation -- 10.5 Numerical Results -- 10.6 Concluding -- References -- 11 Data-Driven Methods in Modern Power System Stability and Security 255 Jinpeng Guo, Georgia Pierrou, Xiaoting Wang, Mohan Du, and Xiaozhe Wang -- 11.1 Introduction -- 11.2 Data-Driven Wide-Area Damping Control -- 11.3 Data-Driven Wide-Area Voltage Control -- 11.4 Data-Driven Inertia Estimation for Frequency Control -- 11.5 A Data-Driven Polynomial Chaos Expansion Method for Available Transfer Capability Assessment -- 11.6 Using PCE to Assess the Ramping Support Capability of a Microgrid -- References -- 12 Application of Quantum Computing for Power Systems 313 Yan Li, Ganesh K. Venayagamoorthy, and Liang -- 12.1 Quantum Computing in Renewable Energy Systems -- 12.2 Quantum Approximate Optimization Algorithm for Renewable Energy Systems -- 12.3 Typical Applications of Quantum Computing -- Acknowledgment -- References -- 13 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts 323 Zejia Jing, Ali Parizad, and Saifur Rahman -- 13.1 Introduction -- 13.2 Principles and Concepts of Building Hourly Energy Consumption Forecasting -- 13.3 Conclusion -- References -- 14 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results 363 Zejia Jing, Ali Parizad, and Saifur Rahman -- 14.1 Introduction -- 14.2 Case Study and Result of Building Hourly Energy Consumption Forecasting -- 14.3 Building Occupancy Measurement -- 14.4 Conclusion -- 15 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems 417 Zejia Jing, Ali Parizad, and Saifur Rahman -- 15.1 Introduction -- 15.2 PV Energy Forecasting -- 15.3 Conclusion -- References -- 16 An Intelligent Reinforcement-Learning-Based Load Shedding to Prevent Voltage Instability 449 Pouria Akbarzadeh Aghdam, Hamid Khoshkhoo, and Ahmad Akbari -- 16.1 Introduction -- 16.2 Stability Control Methods -- 16.3 Characteristics of Optimal Stability Controller -- 16.4 Utilizing Reinforcement Learning for Enhancing Voltage Stability -- 16.5 Taxonomy of RL -- 16.6 Proposed Algorithm -- 16.7 Reinforcement Learning Algorithm Components -- 16.8 Algorithm Implementation Process -- 16.9 Simulations and Results -- 16.10 Scenario I -- 16.11 Scenario II -- 16.12 Scenario III -- 16.13 Conclusion -- References -- 17 Deep Learning Techniques for Solving Optimal Power Flow Problems 471 Vassilis Kekatos and Manish K. Singh -- 17.1 Introduction -- 17.2 Sensitivity-Informed Learning for OPF -- 17.3 Deep Learning for Stochastic OPF -- 17.4 Conclusions -- References -- 18 Research on Intelligent Prediction of Spatial-Temporal Dynamic Frequency Response and Performance Evaluation 501 Xieli Sun, Longyu Chen, and Xiaoru Wang -- 18.1 Introduction -- 18.2 Modeling Process and Evaluation Method -- 18.3 Case Study -- 18.4 Conclusion -- References -- 19 Emerging Technologies and Future Trends in Cyber-Physical Power Systems: Toward a New Era of Innovations 525 Ali Parizad, Hamid Reza Baghaee, Vahid Alizadeh, and Saifur Rahman -- 19.1 Introduction -- 19.2 Paradigm Shifts in Power Transmission and Management -- 19.3 Innovations in Electric Mobility and Sustainable Transportation -- 19.4 Digital Transformation and Technological Convergence in Cyber-Physical Power Systems -- 19.5 Cyber-Physical Systems Enhancing Societal Well-Being -- 19.6 Toward a Decentralized and Automated Future -- 19.7 Overcoming Challenges with Advanced Technologies -- 19.8 Revolutionizing Modern Power Systems with Real-Time Simulators -- 19.9 Emerging Trends Shaping the Future Energy Landscape -- 19.10 Conclusion -- References -- Index.