Data-Driven Energy Management and Tariff Optimization in Power Systems : Shaping the Future of Electricity Distribution Through Analytics

• Record Nr.: UNINA9911042410503321
• Autore: Arasteh Hamidreza
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2025; ©2026
• ISBN: 1-394-29030-6; 1-394-29028-4
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (320 pages)
• Altri autori (Persone): SianoPierluigi; MoslemiNiki; GuerreroJosep M
• Disciplina: 333.7932
• Soggetti: Electric power systems - Management; Energy consumption - Forecasting
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Chapter 1 Fundamentals of Power System Data and Analytics -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems -- 1.2.2 Transformation in the Power Industry -- 1.2.3 Drivers and Barriers -- 1.3 Data‐rich Power Systems -- 1.3.1 Data Sources and Types -- 1.3.2 Data Structure -- 1.4 Data Analytics in Power Systems -- 1.4.1 What Is Data Analytics? -- 1.4.2 Analytics Techniques -- 1.5 Data Analytics‐Based Decision‐Making in Future Power Systems -- 1.5.1 Decision Framework -- 1.5.1.1 Uncertainty Issues -- 1.5.1.2 Behavioral Analytics -- 1.5.1.3 Policy Mechanisms -- 1.5.2 Computational Aspects -- 1.6 Conclusion -- 1.7 Future Trends and Challenges -- References -- Chapter 2 Advanced Predictive Modeling for Energy Consumption and Demand -- 2.1 The Role of Load Forecasting in Power System Planning -- 2.2 Need for Short‐Term Demand Forecasting -- 2.3 Components of Power Demand and Factors Affecting Demand Growth -- 2.3.1 Electricity Demand from the ; Consumer Type Perspective -- 2.3.2 Electricity Demand from the Supply Perspective -- 2.4 Electricity Demand in Networks with High Renewable Energy Sources -- 2.5 Machine Learning and Its Applications in Demand Forecast -- 2.5.1 Application of Clustering in Load Forecasting -- 2.6 The Impact of Macro‐decisions on Long‐term Load Forecasting -- 2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand -- 2.7 Conclusion -- References -- Chapter 3 Demand Response and Customer‐Centric Energy Management -- 3.1 Introduction -- 3.2 Background -- 3.3 Future Power Systems Aspects, Trends, and Challenges -- 3.4 Transforming to Customer‐Centric Era -- 3.4.1 Differences Between Customer‐Centric DR Solution and Other Ways in the Future Power System.; 3.4.2 Drivers and Enablers -- 3.5 Customer‐Centric Power System Structure -- 3.5.1 Physical Layer -- 3.5.1.1 Physical Resources -- 3.5.1.2 Physical Constraints of the System -- 3.5.2 Cyber‐Social Layers -- 3.5.2.1 Centralized Approach (Traditional) -- 3.5.2.2 Decentralized Approach (Future) -- 3.6 Conclusion and Future Trends -- References -- Chapter 4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights -- 4.1 Introduction -- 4.1.1 Data Mining: Concepts, Procedures, and Tools -- 4.1.2 Energy Management and the Role of Data Mining -- 4.1.3 Aims and Scope -- 4.2 Investigating Industrial Load Data: Analysis Through Various Indexes -- 4.3 Classification of Industries -- 4.4 Discussion and Conclusions -- References -- Chapter 5 Data‐Driven Tariff Design for Equitable Energy Distribution -- 5.1 Introduction -- 5.1.1 Literature Review and Contributions -- 5.1.2 Chapter Organization -- 5.2 Proposed Approach and Formulations -- 5.3 Describing the Case Study -- 5.4 Simulation Results -- 5.5 Conclusions and Future Works -- References -- Chapter 6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems -- 6.1 Introduction -- 6.2 Machine Learning Techniques -- 6.2.1 Artificial Neural Network and Deep Neural Network -- 6.2.2 Convolutional Neural Network -- 6.2.3 Recurrent Neural Network -- 6.2.4 Long Short‐Term Memory -- 6.3 General View of ML/DL Methods for RES Integration -- 6.3.1 Data Preprocessing -- 6.3.1.1 Normalization -- 6.3.1.2 Wrong/Missing Values and Outliers -- 6.3.1.3 Data Resolution -- 6.3.1.4 Inactive Time Data -- 6.3.1.5 Data Augmentation -- 6.3.1.6 Correlation -- 6.3.1.7 Data Clustering -- 6.3.2 Deterministic/Probabilistic Forecasting Methods -- 6.3.2.1 Deterministic Methods -- 6.3.2.2 Probabilistic Forecasting Methods -- 6.3.3 Evaluation Measures.; 6.4 ML/DL Application for Integration of RES -- 6.4.1 Renewable Resources Data Prediction/Planning -- 6.4.2 RES Power Generation Prediction/Operation -- 6.4.3 Electric Load and Demand Forecasting -- 6.4.4 Stability Analysis -- 6.4.4.1 Security Assessment -- 6.4.4.2 Stability Assessment -- 6.5 Integrated Machine Learning and Optimization Approach -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning‐Based Solutions for Renewable Energy Integration -- 7.1 Introduction -- 7.2 Machine Learning Importance in RESs Sector -- 7.2.1 AI‐Based Algorithms in RESs -- 7.2.2 ML Algorithms Application in RESs -- 7.3 Role of ML in Optimizing Renewable Energy Generation -- 7.3.1 Different Programming Models in RES Optimization -- 7.3.2 Optimization Objectives in RESs -- 7.3.3 ML Applications in Optimizing Renewable Energy Generation -- 7.4 Ensuring Grid Stability Through ML‐Based Forecasting -- 7.4.1 Grid Stability Forecasting -- 7.4.2 Grid Stability Through ML‐Based Forecasting -- 7.5 Challenges and Future Direction in ML‐Based Approaches to RESs -- 7.5.1 Challenges in ML‐Based Approaches to RESs -- 7.5.2 Future Directions in ML‐Based Approaches to RESs -- 7.6 Conclusion -- References -- Chapter 8 ; Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting -- 8.1 RES Share in World Energy Transition -- 8.2 Applications of PV Panels in Energy Systems -- 8.3 Disadvantages of PV Panels -- 8.4 Importance of PV Power Forecasting -- 8.5 Proposed Algorithm for PV Power Prediction -- 8.6 Numerical Results and Discussions -- 8.7 Concluding Remarks -- References -- Chapter 9 Power System Resilience Evaluation Data Challenges and Solutions -- 9.1 Introduction -- 9.2 A Review of Power System Resilience Metrics -- 9.3 The General Framework for the Resilience Assessment of the Power System -- 9.4 Data Required for Power System Resilience Studies.; 9.4.1 Data of Natural Origin -- 9.4.2 Basic Data of the Power System -- 9.4.3 Data on Failure and Restoration Rates -- 9.5 Data Analysis and Correction -- 9.6 Disaster Forecasting in Power System Resilience Studies -- 9.7 Modeling the Impact of Disaster on Power System Performance -- 9.8 Static Model in Machine Learning -- 9.9 Spatiotemporal Random Process -- 9.9.1 Dynamic Model for Chain Failures -- 9.9.2 Nonstationary Failure‐Recovery‐Impact Processes -- 9.10 Lessons Learned and Concluding Remarks -- 9.11 Future Work -- References -- Chapter 10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach -- 10.1 Introduction -- 10.2 Deep Learning Neural Networks -- 10.2.1 RNN -- 10.2.2 LSTM -- 10.2.3 CNN -- 10.2.4 Convolutional Layer -- 10.2.5 Pooling Layer -- 10.2.6 Fully Connected Layer -- 10.3 The Proposed Method -- 10.3.1 Pre‐Processing and Preparing Data -- 10.3.2 Proposed Method Architecture -- 10.3.3 Proposed Method's Parameters -- 10.3.4 Performance Evaluation -- 10.4 Results and Discussion -- 10.5 Challenges and Future Trends -- 10.6 Conclusion -- References -- Chapter 11 Power System Cyber‐Physical Security and Resiliency Based on Data‐Driven Methods -- 11.1 Introduction -- 11.2 Fundamental Concepts -- 11.2.1 Cyber‐Physical Power System (CPPS) -- 11.2.2 Security and Resiliency -- 11.3 Role of Data Analytics -- 11.3.1 Basic Methods -- 11.3.1.1 Supervised Learning (SL) -- 11.3.1.2 Unsupervised Learning (UL) -- 11.3.2 Advanced Techniques -- 11.3.2.1 Dimensionality Reduction (DR) -- 11.3.2.2 Feature Engineering -- 11.3.2.3 Reinforcement Learning -- 11.3.2.4 Integrated Models -- 11.4 Interdependency Modeling -- 11.4.1 Direct Modeling -- 11.4.2 Testbeds -- 11.4.3 Game‐Theoretic -- 11.4.4 Machine Learning -- 11.5 Cyber‐Physical Threats -- 11.5.1 Physical Attacks -- 11.5.2 Cyberattacks -- 11.5.2.1 Confidentiality.; 11.5.2.2 Availability -- 11.5.2.3 Integrity -- 11.5.3 Coordinated Attacks -- 11.6 Defense Framework -- 11.6.1 Preventive Measures -- 11.6.1.1 Supply Chain Security -- 11.6.1.2 Access Control -- 11.6.1.3 Personnel Training -- 11.6.1.4 Resource Allocation -- 11.6.1.5 Infrastructure Hardening -- 11.6.1.6 Moving Target Defense -- 11.6.2 Mitigation Actions -- 11.6.2.1 Attack Detection -- 11.6.2.2 Data Recovery -- 11.6.2.3 Reconfiguration and Restoration -- 11.6.2.4 Forensic Analysis -- 11.7 Conclusion -- References -- Chapter 12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems -- 12.1 Introduction -- 12.2 Load‐Shedding Strategies -- 12.2.1 Conventional LS -- 12.2.2 Adaptive LS -- 12.2.3 AI‐Based LS -- 12.3 Principles of UVLS -- 12.3.1 Amount of Load Shed -- 12.3.2 Location for LS -- 12.3.3 Application of VSI for UVLS -- 12.4 AI‐Based Methods -- 12.5 Case Study -- 12.5.1 Database Generation -- 12.5.2 Offline Training -- 12.5.3 Online Application -- 12.6 Future Challenges and Transfer Learning -- 12.7 Conclusion -- References -- Index -- EULA.
• Sommario/riassunto: 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.