Data-Driven Energy Management and Tariff Optimization in Power Systems
Shaping the Future of Electricity Distribution through Analytics
1st Edition
Will be published approx. on 11. December 2025
Book
Hardback
288 pages
978-1-394-29027-7 (ISBN)
Description
Presents a comprehensive guide to transforming power systems through data
Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning.
Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book's accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges.
Integrating rigorous analysis with application-oriented strategies, this book:
Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff design
Provides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrations
Highlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy data
Addresses the integration of renewable energy sources into existing infrastructures through data-driven optimization
Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.
Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning.
Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book's accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges.
Integrating rigorous analysis with application-oriented strategies, this book:
Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff design
Provides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrations
Highlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy data
Addresses the integration of renewable energy sources into existing infrastructures through data-driven optimization
Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 254 mm
Width: 178 mm
Thickness: 18 mm
Weight
721 gr
ISBN-13
978-1-394-29027-7 (9781394290277)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Hamidreza Arasteh | Pierluigi Siano | Niki Moslemi
Data-Driven Energy Management and Tariff Optimization in Power Systems
Shaping the Future of Electricity Distribution through Analytics
E-Book
10/2025
1st Edition
Wiley
€133.99
Available for download
Hamidreza Arasteh | Pierluigi Siano | Niki Moslemi
Data-Driven Energy Management and Tariff Optimization in Power Systems
Shaping the Future of Electricity Distribution through Analytics
E-Book
10/2025
1st Edition
Wiley
€137.99
Available for download
Persons
Hamidreza Arasteh is an Assistant Professor in the Power Systems Operation and Planning Research Department at the Niroo Research Institute, Tehran, Iran, and a Research Assistant at the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, Zhejiang, China. He specializes in energy management, smart grids, microgrids, and electricity markets, with numerous research contributions in energy management and the integration of data analytics into power system operations.
Pierluigi Siano is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory at the University of Salerno, Italy. A Senior Member of IEEE, his research focuses on demand response, distributed energy resources, and power system planning. He serves on editorial boards for several prestigious journals in the field.
Niki Moslemi is Head of the Power Systems Operation and Planning Research Department at the Niroo Research Institute in Tehran, Iran. She brings decades of experience in power quality, load forecasting, system resiliency, and data-driven energy strategies. Her leadership and research span multiple high-impact projects within the energy sector.
Josep M. Guerrero is with Zhejiang University, Hangzhou, Zhejiang, China, a Director of the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, China, and a Distinguished Senior Researcher at the Department of Electrical Engineering, University of Valladolid, Spain. His research interests include various aspects of microgrids, including power electronics and distributed energy resources.
Pierluigi Siano is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory at the University of Salerno, Italy. A Senior Member of IEEE, his research focuses on demand response, distributed energy resources, and power system planning. He serves on editorial boards for several prestigious journals in the field.
Niki Moslemi is Head of the Power Systems Operation and Planning Research Department at the Niroo Research Institute in Tehran, Iran. She brings decades of experience in power quality, load forecasting, system resiliency, and data-driven energy strategies. Her leadership and research span multiple high-impact projects within the energy sector.
Josep M. Guerrero is with Zhejiang University, Hangzhou, Zhejiang, China, a Director of the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, China, and a Distinguished Senior Researcher at the Department of Electrical Engineering, University of Valladolid, Spain. His research interests include various aspects of microgrids, including power electronics and distributed energy resources.
Content
About the Editors xiii
List of Contributors xv
Preface xix
1 Fundamentals of Power System Data and Analytics 1
Pouya Ramezanzadeh, Mohsen Parsa Moghaddam, and Reza Zamani
1.1 Introduction 1
1.2 Background 2
1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems 2
1.2.2 Transformation in the Power Industry 3
1.2.3 Drivers and Barriers 6
1.3 Data-rich Power Systems 6
1.3.1 Data Sources and Types 8
1.3.2 Data Structure 10
1.4 Data Analytics in Power Systems 11
1.4.1 What Is Data Analytics? 12
1.4.2 Analytics Techniques 12
1.5 Data Analytics-Based Decision-Making in Future Power Systems 13
1.5.1 Decision Framework 15
1.5.1.1 Uncertainty Issues 15
1.5.1.2 Behavioral Analytics 15
1.5.1.3 Policy Mechanisms 15
1.5.2 Computational Aspects 16
1.6 Conclusion 16
1.7 Future Trends and Challenges 16
References 17
2 Advanced Predictive Modeling for Energy Consumption and Demand 21
Seyed Mohsen Hashemi and Abbas Marini
2.1 The Role of Load Forecasting in Power System Planning 21
2.2 Need for Short-Term Demand Forecasting 22
2.3 Components of Power Demand and Factors Affecting Demand Growth 22
2.3.1 Electricity Demand from the Consumer Type Perspective 23
2.3.2 Electricity Demand from the Supply Perspective 23
2.4 Electricity Demand in Networks with High Renewable Energy Sources 24
2.5 Machine Learning and Its Applications in Demand Forecast 25
2.5.1 Application of Clustering in Load Forecasting 27
2.6 The Impact of Macro-decisions on Long-term Load Forecasting 28
2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand 29
2.7 Conclusion 34
References 35
3 Demand Response and Customer-Centric Energy Management 39
Alireza Mansoori, Mohsen Parsa Moghaddam, and Reza Zamani
3.1 Introduction 39
3.2 Background 39
3.3 Future Power Systems Aspects, Trends, and Challenges 41
3.4 Transforming to Customer-Centric Era 41
3.4.1 Differences Between Customer-Centric DR Solution and OtherWays in the Future
Power System 42
3.4.2 Drivers and Enablers 42
3.5 Customer-Centric Power System Structure 45
3.5.1 Physical Layer 45
3.5.1.1 Physical Resources 45
3.5.1.2 Physical Constraints of the System 46
3.5.2 Cyber-Social Layers 49
3.5.2.1 Centralized Approach (Traditional) 50
3.5.2.2 Decentralized Approach (Future) 50
3.6 Conclusion and Future Trends 54
References 57
4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights 61
Hamidreza Arasteh, Niki Moslemi, Majid Miri Larimi, Pierluigi Siano, Sobhan Naderian, andJosep M. Guerrero
4.1 Introduction 61
4.1.1 Data Mining: Concepts, Procedures, and Tools 61
4.1.2 Energy Management and the Role of Data Mining 65
4.1.3 Aims and Scope 66
4.2 Investigating Industrial Load Data: Analysis Through Various Indexes 67
4.3 Classification of Industries 86
4.4 Discussion and Conclusions 90
References 92
5 Data-Driven Tariff Design for Equitable Energy Distribution 95
Salah Bahramara, Hamidreza Arasteh, Asrin Seyedzahedi, and Khabat Ghamari
5.1 Introduction 95
5.1.1 Literature Review and Contributions 96
5.1.2 Chapter Organization 97
5.2 Proposed Approach and Formulations 97
5.3 Describing the Case Study 98
5.4 Simulation Results 100
5.5 Conclusions and Future Works 100
References 105
6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems 107
Abbas Marini and Seyed Mohsen Hashemi
6.1 Introduction 107
6.2 Machine Learning Techniques 109
6.2.1 Artificial Neural Network and Deep Neural Network 110
6.2.2 Convolutional Neural Network 111
6.2.3 Recurrent Neural Network 111
6.2.4 Long Short-Term Memory 112
6.3 General View of ML/DL Methods for RES Integration 112
6.3.1 Data Preprocessing 114
6.3.1.1 Normalization 115
6.3.1.2 Wrong/Missing Values and Outliers 115
6.3.1.3 Data Resolution 115
6.3.1.4 Inactive Time Data 116
6.3.1.5 Data Augmentation 116
6.3.1.6 Correlation 116
6.3.1.7 Data Clustering 116
6.3.2 Deterministic/Probabilistic Forecasting Methods 116
6.3.2.1 Deterministic Methods 116
6.3.2.2 Probabilistic Forecasting Methods 119
6.3.3 Evaluation Measures 119
6.4 ML/DL Application for Integration of RES 121
6.4.1 Renewable Resources Data Prediction/Planning 122
6.4.2 RES Power Generation Prediction/Operation 125
6.4.3 Electric Load and Demand Forecasting 126
6.4.4 Stability Analysis 127
6.4.4.1 Security Assessment 128
6.4.4.2 Stability Assessment 129
6.5 Integrated Machine Learning and Optimization Approach 129
6.6 Conclusion 131
References 132
7 Machine Learning-Based Solutions for Renewable Energy Integration: Applications, Optimization, and Grid Stability 135
Ali Paeizi, Mohammad Mehdi Amiri, Sasan Azad, and Mohammad Taghi Ameli
7.1 Introduction 135
7.2 Machine Learning Importance in RESs Sector 137
7.2.1 AI-Based Algorithms in RESs 137
7.2.2 ML Algorithms Application in RESs 140
7.3 Role of ML in Optimizing Renewable Energy Generation 150
7.3.1 Different Programming Models in RES Optimization 150
7.3.2 Optimization Objectives in RESs 150
7.3.3 ML Applications in Optimizing Renewable Energy Generation 151
7.4 Ensuring Grid Stability Through ML-Based Forecasting 155
7.4.1 Grid Stability Forecasting 155
7.4.2 Grid Stability Through ML-Based Forecasting 157
7.5 Challenges and Future Direction in ML-Based Approaches to RESs 159
7.5.1 Challenges in ML-Based Approaches to RESs 160
7.5.2 Future Directions in ML-Based Approaches to RESs 161
7.6 Conclusion 162
References 163
8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting 167
Hamid Jabari, Afshin Ebrahimi, Ardalan Shafiei-Ghazani, and Farkhondeh Jabari
8.1 RES Share inWorld Energy Transition 167
8.2 Applications of PV Panels in Energy Systems 168
8.3 Disadvantages of PV Panels 169
8.4 Importance of PV Power Forecasting 170
8.5 Proposed Algorithm for PV Power Prediction 170
8.6 Numerical Results and Discussions 172
8.7 Concluding Remarks 172
References 175
9 Power System Resilience Evaluation: Data Challenges and Solutions 179
Mohammad Reza Sheibani, Habibollah Raoufi, and Javad Nezafat Namini
9.1 Introduction 179
9.2 A Review of Power System Resilience Metrics 180
9.3 The General Framework for the Resilience Assessment of the Power System 182
9.4 Data Required for Power System Resilience Studies 182
9.4.1 Data of Natural Origin 184
9.4.2 Basic Data of the Power System 184
9.4.3 Data on Failure and Restoration Rates 186
9.5 Data Analysis and Correction 187
9.6 Disaster Forecasting in Power System Resilience Studies 188
9.7 Modeling the Impact of Disaster on Power System Performance 189
9.8 Static Model in Machine Learning 190
9.9 Spatiotemporal Random Process 192
9.9.1 Dynamic Model for Chain Failures 192
9.9.2 Nonstationary Failure-Recovery-Impact Processes 192
9.10 Lessons Learned and Concluding Remarks 193
9.11 Future Work 194
References 194
10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach 197
Sobhan Naderian and Hamidreza Arasteh
10.1 Introduction 197
10.2 Deep Learning Neural Networks 199
10.2.1 RNN 199
10.2.2 LSTM 199
10.2.3 CNN 200
10.2.4 Convolutional Layer 201
10.2.5 Pooling Layer 201
10.2.6 Fully Connected Layer 201
10.3 The Proposed Method 201
10.3.1 Pre-Processing and Preparing Data 201
10.3.2 Proposed Method Architecture 202
10.3.3 Proposed Method's Parameters 202
10.3.4 Performance Evaluation 203
10.4 Results and Discussion 204
10.5 Challenges and Future Trends 206
10.6 Conclusion 206
References 207
11 Power System Cyber-Physical Security and Resiliency Based on Data-Driven Methods 211
Hamed Delkhosh, Mahdi Ghaedi, and Maryam Azimi
11.1 Introduction 211
11.2 Fundamental Concepts 212
11.2.1 Cyber-Physical Power System (CPPS) 212
11.2.2 Security and Resiliency 214
11.3 Role of Data Analytics 215
11.3.1 Basic Methods 215
11.3.1.1 Supervised Learning (SL) 215
11.3.1.2 Unsupervised Learning (UL) 216
11.3.2 Advanced Techniques 216
11.3.2.1 Dimensionality Reduction (DR) 217
11.3.2.2 Feature Engineering 217
11.3.2.3 Reinforcement Learning 217
11.3.2.4 Integrated Models 218
11.4 Interdependency Modeling 218
11.4.1 Direct Modeling 220
11.4.2 Testbeds 220
11.4.3 Game-Theoretic 221
11.4.4 Machine Learning 222
11.5 Cyber-Physical Threats 223
11.5.1 Physical Attacks 224
11.5.2 Cyberattacks 225
11.5.2.1 Confidentiality 225
11.5.2.2 Availability 226
11.5.2.3 Integrity 226
11.5.3 Coordinated Attacks 227
11.6 Defense Framework 228
11.6.1 Preventive Measures 228
11.6.1.1 Supply Chain Security 229
11.6.1.2 Access Control 229
11.6.1.3 Personnel Training 230
11.6.1.4 Resource Allocation 230
11.6.1.5 Infrastructure Hardening 231
11.6.1.6 Moving Target Defense 231
11.6.2 Mitigation Actions 232
11.6.2.1 Attack Detection 232
11.6.2.2 Data Recovery 233
11.6.2.3 Reconfiguration and Restoration 233
11.6.2.4 Forensic Analysis 234
11.7 Conclusion 234
References 235
12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems: An In-Depth Review 239
Nazanin Pourmoradi, Sasan Azad, Mohammad Mehdi Amiri, and Miadreza Shafie-khah
12.1 Introduction 239
12.2 Load-Shedding Strategies 240
12.2.1 Conventional LS 240
12.2.2 Adaptive LS 240
12.2.3 AI-Based LS 241
12.3 Principles of UVLS 242
12.3.1 Amount of Load Shed 242
12.3.2 Location for LS 243
12.3.3 Application of VSI for UVLS 243
12.4 AI-Based Methods 244
12.5 Case Study 248
12.5.1 Database Generation 248
12.5.2 Offline Training 248
12.5.3 Online Application 249
12.6 Future Challenges and Transfer Learning 249
12.7 Conclusion 251
References 252
Index 257
List of Contributors xv
Preface xix
1 Fundamentals of Power System Data and Analytics 1
Pouya Ramezanzadeh, Mohsen Parsa Moghaddam, and Reza Zamani
1.1 Introduction 1
1.2 Background 2
1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems 2
1.2.2 Transformation in the Power Industry 3
1.2.3 Drivers and Barriers 6
1.3 Data-rich Power Systems 6
1.3.1 Data Sources and Types 8
1.3.2 Data Structure 10
1.4 Data Analytics in Power Systems 11
1.4.1 What Is Data Analytics? 12
1.4.2 Analytics Techniques 12
1.5 Data Analytics-Based Decision-Making in Future Power Systems 13
1.5.1 Decision Framework 15
1.5.1.1 Uncertainty Issues 15
1.5.1.2 Behavioral Analytics 15
1.5.1.3 Policy Mechanisms 15
1.5.2 Computational Aspects 16
1.6 Conclusion 16
1.7 Future Trends and Challenges 16
References 17
2 Advanced Predictive Modeling for Energy Consumption and Demand 21
Seyed Mohsen Hashemi and Abbas Marini
2.1 The Role of Load Forecasting in Power System Planning 21
2.2 Need for Short-Term Demand Forecasting 22
2.3 Components of Power Demand and Factors Affecting Demand Growth 22
2.3.1 Electricity Demand from the Consumer Type Perspective 23
2.3.2 Electricity Demand from the Supply Perspective 23
2.4 Electricity Demand in Networks with High Renewable Energy Sources 24
2.5 Machine Learning and Its Applications in Demand Forecast 25
2.5.1 Application of Clustering in Load Forecasting 27
2.6 The Impact of Macro-decisions on Long-term Load Forecasting 28
2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand 29
2.7 Conclusion 34
References 35
3 Demand Response and Customer-Centric Energy Management 39
Alireza Mansoori, Mohsen Parsa Moghaddam, and Reza Zamani
3.1 Introduction 39
3.2 Background 39
3.3 Future Power Systems Aspects, Trends, and Challenges 41
3.4 Transforming to Customer-Centric Era 41
3.4.1 Differences Between Customer-Centric DR Solution and OtherWays in the Future
Power System 42
3.4.2 Drivers and Enablers 42
3.5 Customer-Centric Power System Structure 45
3.5.1 Physical Layer 45
3.5.1.1 Physical Resources 45
3.5.1.2 Physical Constraints of the System 46
3.5.2 Cyber-Social Layers 49
3.5.2.1 Centralized Approach (Traditional) 50
3.5.2.2 Decentralized Approach (Future) 50
3.6 Conclusion and Future Trends 54
References 57
4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights 61
Hamidreza Arasteh, Niki Moslemi, Majid Miri Larimi, Pierluigi Siano, Sobhan Naderian, andJosep M. Guerrero
4.1 Introduction 61
4.1.1 Data Mining: Concepts, Procedures, and Tools 61
4.1.2 Energy Management and the Role of Data Mining 65
4.1.3 Aims and Scope 66
4.2 Investigating Industrial Load Data: Analysis Through Various Indexes 67
4.3 Classification of Industries 86
4.4 Discussion and Conclusions 90
References 92
5 Data-Driven Tariff Design for Equitable Energy Distribution 95
Salah Bahramara, Hamidreza Arasteh, Asrin Seyedzahedi, and Khabat Ghamari
5.1 Introduction 95
5.1.1 Literature Review and Contributions 96
5.1.2 Chapter Organization 97
5.2 Proposed Approach and Formulations 97
5.3 Describing the Case Study 98
5.4 Simulation Results 100
5.5 Conclusions and Future Works 100
References 105
6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems 107
Abbas Marini and Seyed Mohsen Hashemi
6.1 Introduction 107
6.2 Machine Learning Techniques 109
6.2.1 Artificial Neural Network and Deep Neural Network 110
6.2.2 Convolutional Neural Network 111
6.2.3 Recurrent Neural Network 111
6.2.4 Long Short-Term Memory 112
6.3 General View of ML/DL Methods for RES Integration 112
6.3.1 Data Preprocessing 114
6.3.1.1 Normalization 115
6.3.1.2 Wrong/Missing Values and Outliers 115
6.3.1.3 Data Resolution 115
6.3.1.4 Inactive Time Data 116
6.3.1.5 Data Augmentation 116
6.3.1.6 Correlation 116
6.3.1.7 Data Clustering 116
6.3.2 Deterministic/Probabilistic Forecasting Methods 116
6.3.2.1 Deterministic Methods 116
6.3.2.2 Probabilistic Forecasting Methods 119
6.3.3 Evaluation Measures 119
6.4 ML/DL Application for Integration of RES 121
6.4.1 Renewable Resources Data Prediction/Planning 122
6.4.2 RES Power Generation Prediction/Operation 125
6.4.3 Electric Load and Demand Forecasting 126
6.4.4 Stability Analysis 127
6.4.4.1 Security Assessment 128
6.4.4.2 Stability Assessment 129
6.5 Integrated Machine Learning and Optimization Approach 129
6.6 Conclusion 131
References 132
7 Machine Learning-Based Solutions for Renewable Energy Integration: Applications, Optimization, and Grid Stability 135
Ali Paeizi, Mohammad Mehdi Amiri, Sasan Azad, and Mohammad Taghi Ameli
7.1 Introduction 135
7.2 Machine Learning Importance in RESs Sector 137
7.2.1 AI-Based Algorithms in RESs 137
7.2.2 ML Algorithms Application in RESs 140
7.3 Role of ML in Optimizing Renewable Energy Generation 150
7.3.1 Different Programming Models in RES Optimization 150
7.3.2 Optimization Objectives in RESs 150
7.3.3 ML Applications in Optimizing Renewable Energy Generation 151
7.4 Ensuring Grid Stability Through ML-Based Forecasting 155
7.4.1 Grid Stability Forecasting 155
7.4.2 Grid Stability Through ML-Based Forecasting 157
7.5 Challenges and Future Direction in ML-Based Approaches to RESs 159
7.5.1 Challenges in ML-Based Approaches to RESs 160
7.5.2 Future Directions in ML-Based Approaches to RESs 161
7.6 Conclusion 162
References 163
8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting 167
Hamid Jabari, Afshin Ebrahimi, Ardalan Shafiei-Ghazani, and Farkhondeh Jabari
8.1 RES Share inWorld Energy Transition 167
8.2 Applications of PV Panels in Energy Systems 168
8.3 Disadvantages of PV Panels 169
8.4 Importance of PV Power Forecasting 170
8.5 Proposed Algorithm for PV Power Prediction 170
8.6 Numerical Results and Discussions 172
8.7 Concluding Remarks 172
References 175
9 Power System Resilience Evaluation: Data Challenges and Solutions 179
Mohammad Reza Sheibani, Habibollah Raoufi, and Javad Nezafat Namini
9.1 Introduction 179
9.2 A Review of Power System Resilience Metrics 180
9.3 The General Framework for the Resilience Assessment of the Power System 182
9.4 Data Required for Power System Resilience Studies 182
9.4.1 Data of Natural Origin 184
9.4.2 Basic Data of the Power System 184
9.4.3 Data on Failure and Restoration Rates 186
9.5 Data Analysis and Correction 187
9.6 Disaster Forecasting in Power System Resilience Studies 188
9.7 Modeling the Impact of Disaster on Power System Performance 189
9.8 Static Model in Machine Learning 190
9.9 Spatiotemporal Random Process 192
9.9.1 Dynamic Model for Chain Failures 192
9.9.2 Nonstationary Failure-Recovery-Impact Processes 192
9.10 Lessons Learned and Concluding Remarks 193
9.11 Future Work 194
References 194
10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach 197
Sobhan Naderian and Hamidreza Arasteh
10.1 Introduction 197
10.2 Deep Learning Neural Networks 199
10.2.1 RNN 199
10.2.2 LSTM 199
10.2.3 CNN 200
10.2.4 Convolutional Layer 201
10.2.5 Pooling Layer 201
10.2.6 Fully Connected Layer 201
10.3 The Proposed Method 201
10.3.1 Pre-Processing and Preparing Data 201
10.3.2 Proposed Method Architecture 202
10.3.3 Proposed Method's Parameters 202
10.3.4 Performance Evaluation 203
10.4 Results and Discussion 204
10.5 Challenges and Future Trends 206
10.6 Conclusion 206
References 207
11 Power System Cyber-Physical Security and Resiliency Based on Data-Driven Methods 211
Hamed Delkhosh, Mahdi Ghaedi, and Maryam Azimi
11.1 Introduction 211
11.2 Fundamental Concepts 212
11.2.1 Cyber-Physical Power System (CPPS) 212
11.2.2 Security and Resiliency 214
11.3 Role of Data Analytics 215
11.3.1 Basic Methods 215
11.3.1.1 Supervised Learning (SL) 215
11.3.1.2 Unsupervised Learning (UL) 216
11.3.2 Advanced Techniques 216
11.3.2.1 Dimensionality Reduction (DR) 217
11.3.2.2 Feature Engineering 217
11.3.2.3 Reinforcement Learning 217
11.3.2.4 Integrated Models 218
11.4 Interdependency Modeling 218
11.4.1 Direct Modeling 220
11.4.2 Testbeds 220
11.4.3 Game-Theoretic 221
11.4.4 Machine Learning 222
11.5 Cyber-Physical Threats 223
11.5.1 Physical Attacks 224
11.5.2 Cyberattacks 225
11.5.2.1 Confidentiality 225
11.5.2.2 Availability 226
11.5.2.3 Integrity 226
11.5.3 Coordinated Attacks 227
11.6 Defense Framework 228
11.6.1 Preventive Measures 228
11.6.1.1 Supply Chain Security 229
11.6.1.2 Access Control 229
11.6.1.3 Personnel Training 230
11.6.1.4 Resource Allocation 230
11.6.1.5 Infrastructure Hardening 231
11.6.1.6 Moving Target Defense 231
11.6.2 Mitigation Actions 232
11.6.2.1 Attack Detection 232
11.6.2.2 Data Recovery 233
11.6.2.3 Reconfiguration and Restoration 233
11.6.2.4 Forensic Analysis 234
11.7 Conclusion 234
References 235
12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems: An In-Depth Review 239
Nazanin Pourmoradi, Sasan Azad, Mohammad Mehdi Amiri, and Miadreza Shafie-khah
12.1 Introduction 239
12.2 Load-Shedding Strategies 240
12.2.1 Conventional LS 240
12.2.2 Adaptive LS 240
12.2.3 AI-Based LS 241
12.3 Principles of UVLS 242
12.3.1 Amount of Load Shed 242
12.3.2 Location for LS 243
12.3.3 Application of VSI for UVLS 243
12.4 AI-Based Methods 244
12.5 Case Study 248
12.5.1 Database Generation 248
12.5.2 Offline Training 248
12.5.3 Online Application 249
12.6 Future Challenges and Transfer Learning 249
12.7 Conclusion 251
References 252
Index 257