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Table of Contents
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.
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.