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Data-Driven Energy Management and Tariff Optimization in Power Systems
Shaping the Future of Electricity Distribution through Analytics
Hamidreza Arasteh (Edited by), Arasteh (Author), Pierluigi Siano (Edited by), Niki Moslemi (Edited by), Josep M. Guerrero (Edited by)
9781394290277, Wiley
Hardback, published 11 December 2025
288 pages
28 x 19 x 2.5 cm, 0.866 kg
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: 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.
About the Editors xiii List of Contributors xv Preface xix 1 Fundamentals of Power System Data and Analytics 1 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 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 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 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 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 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 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 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 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 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 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 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
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Hamidreza Arasteh, Niki Moslemi, Majid Miri Larimi, Pierluigi Siano, Sobhan Naderian, andJosep M. Guerrero
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Subject Areas: History [HB]
