{"product_id":"artificial-intelligence-empowered-smart-energy-systems-hardback-9781394253616","title":"Artificial Intelligence Empowered Smart Energy Systems (Hardback) 9781394253616","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence Empowered Smart Energy Systems\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eQiang Yang (Edited by), Yang (Author), Gang Huang (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394253616, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e28 x 19 x 2.1 cm, 0.767 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn illuminating and up-to-date exploration of the latest advances in AI-empowered smart energy systems\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eArtificial Intelligence Empowered Smart Energy Systems,\u003c\/i\u003e the editors along with a team of distinguished researchers deliver an original and comprehensive discussion of artificial intelligence enabled smart energy systems. The book offers a deep dive into AI’s integration with energy, examining critical topics like renewable energy forecasting, load monitoring, fault diagnosis, resilience-oriented optimization, and efficiency-driven control. \u003c\/p\u003e\n\u003cp\u003eThe contributors discuss the real-world applications of AI in smart energy systems, showing you AI’s transformative effects on energy landscapes. It provides practical solutions and strategies to address complicated problems in energy systems. \u003c\/p\u003e\n\u003cp\u003eThe book also includes: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA thorough introduction to cybersecurity, privacy, and virtual power plants\u003c\/li\u003e\n\u003cli\u003eComprehensive demonstrations of the effective leveraging of AI technologies in energy systems\u003c\/li\u003e\n\u003cli\u003ePractical discussions of the potential of AI to create sustainable, efficient, and resilient energy systems\u003c\/li\u003e\n\u003cli\u003eDetailed case studies and real-world examples of AI’s implementation in smart energy systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for researchers, data scientists, and policymakers, \u003ci\u003eArtificial Intelligence Empowered Smart Energy Systems\u003c\/i\u003e will also benefit graduate and senior undergraduate students in both the tech and energy industries.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Contributors xv\u003c\/p\u003e \u003cp\u003eAbout the Editors xxi\u003c\/p\u003e \u003cp\u003eForeword xxiii\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Machine Learning-Based Applications for Cyberattack and Defense in Smart Energy Systems 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSha Peng, Mengxiang Liu, Zhenyong Zhang, and Ruilong Deng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Machine Learning 1\u003c\/p\u003e \u003cp\u003e1.1.1 Overview of Machine Learning Approaches 1\u003c\/p\u003e \u003cp\u003e1.1.1.1 Unsupervised Learning 1\u003c\/p\u003e \u003cp\u003e1.1.1.2 Supervised Learning 3\u003c\/p\u003e \u003cp\u003e1.1.1.3 Semi-Supervised Learning 4\u003c\/p\u003e \u003cp\u003e1.1.1.4 Reinforcement Learning 4\u003c\/p\u003e \u003cp\u003e1.1.2 Advantages of Machine Learning 5\u003c\/p\u003e \u003cp\u003e1.2 Machine Learning in Attack Design 6\u003c\/p\u003e \u003cp\u003e1.2.1 System Information Inference 7\u003c\/p\u003e \u003cp\u003e1.2.2 Attack Resource Allocation 9\u003c\/p\u003e \u003cp\u003e1.3 Machine Learning in Attack Protection 10\u003c\/p\u003e \u003cp\u003e1.3.1 Data Security 10\u003c\/p\u003e \u003cp\u003e1.3.2 Vulnerability Analysis 11\u003c\/p\u003e \u003cp\u003e1.4 Machine Learning in Attack Detection 12\u003c\/p\u003e \u003cp\u003e1.4.1 Anomaly Detection 13\u003c\/p\u003e \u003cp\u003e1.4.2 Line Outage Detection 18\u003c\/p\u003e \u003cp\u003e1.4.3 Electricity Theft Detection 18\u003c\/p\u003e \u003cp\u003e1.5 Machine Learning in Impact Mitigation 19\u003c\/p\u003e \u003cp\u003e1.5.1 Compromised Measurement Clearing 19\u003c\/p\u003e \u003cp\u003e1.6 Future Directions 20\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Enhancing Cybersecurity in Power Communication Networks: An Approach to Resilient CPPS Through Channel Expansion and Defense Resource Allocation 27\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYingjun Wu, Yingtao Ru, Jinfan Chen, Hao Xu, Zhiwei Lin, Chengjun Liu, and Xinyi Liang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 27\u003c\/p\u003e \u003cp\u003e2.2 Mechanisms for the Classification and Propagation of Cyberattacks 28\u003c\/p\u003e \u003cp\u003e2.2.1 Popularity in Academic Research and Frequency of Actual Cyberattacks 30\u003c\/p\u003e \u003cp\u003e2.2.2 Propagation Mechanism of Hotspot Cyberattacks 30\u003c\/p\u003e \u003cp\u003e2.2.2.1 Denial of Service (DoS) Attack 30\u003c\/p\u003e \u003cp\u003e2.2.2.2 False Data Injection Attacks (FDIAs) 32\u003c\/p\u003e \u003cp\u003e2.2.2.3 The Black Hole Attack (BHA) 34\u003c\/p\u003e \u003cp\u003e2.2.2.4 Address Resolution Protocol (ARP) Spoofing Attack 35\u003c\/p\u003e \u003cp\u003e2.2.2.5 Man-In-The-Middle (MITM) Attack 36\u003c\/p\u003e \u003cp\u003e2.2.2.6 Eavesdropping Attack (EA) 37\u003c\/p\u003e \u003cp\u003e2.2.2.7 Replay Attack 38\u003c\/p\u003e \u003cp\u003e2.2.2.8 Load Altering Attack (LAA) 39\u003c\/p\u003e \u003cp\u003e2.2.3 Propagation Mechanism of Non-Hotspot Cyberattacks 41\u003c\/p\u003e \u003cp\u003e2.2.3.1 Active Non-Hotspot Cyberattack 41\u003c\/p\u003e \u003cp\u003e2.2.3.2 Passive Non-Hotspot Cyberattack 42\u003c\/p\u003e \u003cp\u003e2.2.4 Targeting Equipment of Cyberattacks in TAL 43\u003c\/p\u003e \u003cp\u003e2.3 Power Communication Network Planning Based on Information Transmission Reachability Against Cyberattacks 47\u003c\/p\u003e \u003cp\u003e2.3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e2.3.2 Planning Framework for Grid Communication Network Planning Object 48\u003c\/p\u003e \u003cp\u003e2.3.2.1 Planning Goals 49\u003c\/p\u003e \u003cp\u003e2.3.2.2 Planning Framework 50\u003c\/p\u003e \u003cp\u003e2.3.3 Topology Planning Model of Power Communication Network 51\u003c\/p\u003e \u003cp\u003e2.3.3.1 Planning Goal of the TP to Ensure Information Transmission of Regular Operation 51\u003c\/p\u003e \u003cp\u003e2.3.3.2 Enhancing Cyberattack Defense Capabilities in ACAP Planning Goals 53\u003c\/p\u003e \u003cp\u003e2.3.4 A Game Theory-Based Planning Method 59\u003c\/p\u003e \u003cp\u003e2.3.4.1 Criterion Derived from the Nash Equilibrium 59\u003c\/p\u003e \u003cp\u003e2.3.4.2 Model Solution Using Enhanced Particle Swarm-Based Optimization Algorithm 60\u003c\/p\u003e \u003cp\u003e2.3.5 Simulations 61\u003c\/p\u003e \u003cp\u003e2.3.5.1 System Overview Under Study 61\u003c\/p\u003e \u003cp\u003e2.3.5.2 Cyberattacks Factored in During the Planning Phase 63\u003c\/p\u003e \u003cp\u003e2.3.5.3 Planning Results in Different Cases 63\u003c\/p\u003e \u003cp\u003e2.3.5.4 Relative to Planning Methods that Disregard Cyberattacks 65\u003c\/p\u003e \u003cp\u003e2.3.5.5 Practical Case Application 67\u003c\/p\u003e \u003cp\u003e2.3.5.6 PSO-Based Analysis of Planning Outcomes 69\u003c\/p\u003e \u003cp\u003e2.4 Survivability-Oriented Defensive Resource Allocation for Communication and Information Systems Under Cyberattack 71\u003c\/p\u003e \u003cp\u003e2.4.1 Introduction 71\u003c\/p\u003e \u003cp\u003e2.4.2 Systematic Evaluation of Survivability for CPPS Communication and Information Systems 73\u003c\/p\u003e \u003cp\u003e2.4.2.1 Breaking Down Power Businesses into Atomic Services 73\u003c\/p\u003e \u003cp\u003e2.4.2.2 Indexes for Evaluating the Survivability of Atomic Services 74\u003c\/p\u003e \u003cp\u003e2.4.2.3 Survivability Evaluation Framework 74\u003c\/p\u003e \u003cp\u003e2.4.2.4 Calculation Method for Survivability Evaluation Indices 74\u003c\/p\u003e \u003cp\u003e2.4.2.5 Calculation Method for Survivability Evaluation Indexes 79\u003c\/p\u003e \u003cp\u003e2.4.3 Defensive Resource Allocation Model for Enhancing CPPS Survivability Against Cyber Threats 79\u003c\/p\u003e \u003cp\u003e2.4.3.1 Objective Function 80\u003c\/p\u003e \u003cp\u003e2.4.3.2 Constraints 80\u003c\/p\u003e \u003cp\u003e2.4.4 Modified Genetic Algorithm for the Proposed Model 80\u003c\/p\u003e \u003cp\u003e2.4.5 Simulations 82\u003c\/p\u003e \u003cp\u003e2.4.5.1 Introduction of the Studied System 82\u003c\/p\u003e \u003cp\u003e2.4.5.2 Simulation Results and Analysis 85\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multi-Objective Real-Time Control of Operating Conditions Using Deep Reinforcement Learning 101\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRuisheng Diao, Tu Lan, Zhiwei Wang, Haifeng Li, Chunlei Xu, Fangyuan Sun, Bei Zhang, Yishen Wang, Siqi Wang, Jiajun Duan, andDiShi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 101\u003c\/p\u003e \u003cp\u003e3.2 Principles of Deep Reinforcement Learning 102\u003c\/p\u003e \u003cp\u003e3.2.1 Deep Q Network (DQN) 103\u003c\/p\u003e \u003cp\u003e3.2.2 Proximal Policy Optimization (PPO) 104\u003c\/p\u003e \u003cp\u003e3.2.3 Soft Actor-Critic (SAC) 106\u003c\/p\u003e \u003cp\u003e3.3 Real-Time Line Flow Control Using PPO 107\u003c\/p\u003e \u003cp\u003e3.3.1 Problem Formulation 107\u003c\/p\u003e \u003cp\u003e3.3.1.1 State Space 107\u003c\/p\u003e \u003cp\u003e3.3.1.2 Action Space 107\u003c\/p\u003e \u003cp\u003e3.3.1.3 Reward Function 107\u003c\/p\u003e \u003cp\u003e3.3.1.4 Training Process of PPO-Based Agents 108\u003c\/p\u003e \u003cp\u003e3.3.2 Case Studies 109\u003c\/p\u003e \u003cp\u003e3.4 Dueling DQN-Based Topology Control for Maximizing Available Transfer Capabilities 110\u003c\/p\u003e \u003cp\u003e3.4.1 Problem Formulation 112\u003c\/p\u003e \u003cp\u003e3.4.1.1 Architecture Design 113\u003c\/p\u003e \u003cp\u003e3.4.1.2 Dueling DQN Agent 113\u003c\/p\u003e \u003cp\u003e3.4.1.3 Imitation Learning 114\u003c\/p\u003e \u003cp\u003e3.4.1.4 Guided Exploration Training 115\u003c\/p\u003e \u003cp\u003e3.4.1.5 Early Warning 116\u003c\/p\u003e \u003cp\u003e3.4.2 Case Studies 116\u003c\/p\u003e \u003cp\u003e3.4.2.1 Environment and Framework 116\u003c\/p\u003e \u003cp\u003e3.4.2.2 Effectiveness of Imitation Learning 117\u003c\/p\u003e \u003cp\u003e3.4.2.3 Improved Performance with Guided Exploration 117\u003c\/p\u003e \u003cp\u003e3.4.2.4 Performance Comparison of Different Agents 118\u003c\/p\u003e \u003cp\u003e3.5 Real-Time Multi-Objective Power Flow Control Using Soft Actor-Critic 119\u003c\/p\u003e \u003cp\u003e3.5.1 Problem Formulation 119\u003c\/p\u003e \u003cp\u003e3.5.1.1 Architecture Design 120\u003c\/p\u003e \u003cp\u003e3.5.1.2 Episode and Terminating Conditions 122\u003c\/p\u003e \u003cp\u003e3.5.1.3 State Space 122\u003c\/p\u003e \u003cp\u003e3.5.1.4 Control Space 122\u003c\/p\u003e \u003cp\u003e3.5.1.5 Reward Definition 122\u003c\/p\u003e \u003cp\u003e3.5.2 Case Studies 123\u003c\/p\u003e \u003cp\u003eReferences 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Smart Generation Control Based on Multi-Agents 127\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLei Xi, Yixiao Wang, Lu Dong, and Jianyu Ren\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Overview 127\u003c\/p\u003e \u003cp\u003e4.2 Research on Intelligent Power Generation Control Based on Multi-Agents 128\u003c\/p\u003e \u003cp\u003e4.2.1 Function and Architecture of Multi-Agent System 128\u003c\/p\u003e \u003cp\u003e4.2.2 Virtual Power Generation Tribe Control Based on Multi-Agent Theory 129\u003c\/p\u003e \u003cp\u003e4.2.2.1 First-Order Multi-Agent Consistency Algorithm 130\u003c\/p\u003e \u003cp\u003e4.2.2.2 Consistent AGC Power Allocation Algorithm 132\u003c\/p\u003e \u003cp\u003e4.2.2.3 Robust Consistency Algorithm in Nonideal Communication Networks 136\u003c\/p\u003e \u003cp\u003e4.3 Intelligent Power Generation Control for Islands and Microgrids 139\u003c\/p\u003e \u003cp\u003e4.3.1 AGC Cooperative Control Based on Equal Incremental Rate Consistency Algorithm 141\u003c\/p\u003e \u003cp\u003e4.3.1.1 Intelligent Distribution Network Decentralized Autonomy Framework 141\u003c\/p\u003e \u003cp\u003e4.3.1.2 AGC Power Allocation Model of Smart Distribution Network 142\u003c\/p\u003e \u003cp\u003e4.3.1.3 Consistency Algorithm of Equal Incremental Rate 143\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Power System Fault Diagnosis Method Under Disaster Weather Based on Random Self-Regulating Algorithm 149\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTao Wang, Liyuan Liu, Ruixuan Ying, Chunyu Zhou, Hanyan Wu, and Quanlin Leng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 149\u003c\/p\u003e \u003cp\u003e5.2 Analytic Model for Fault Diagnosis 151\u003c\/p\u003e \u003cp\u003e5.2.1 Three Types of Self-Regulating Trust Factors 153\u003c\/p\u003e \u003cp\u003e5.2.1.1 Self-Regulating Expectation Trust Factor and Self-Regulating Warning Trust Factor 153\u003c\/p\u003e \u003cp\u003e5.2.1.2 Self-Regulating Weather Trust Factor 155\u003c\/p\u003e \u003cp\u003e5.2.2 Expected States of Protection Devices 156\u003c\/p\u003e \u003cp\u003e5.2.2.1 Expected States of Main Protective Relays 157\u003c\/p\u003e \u003cp\u003e5.2.2.2 Expected States of Primary Backup Protective Relays 157\u003c\/p\u003e \u003cp\u003e5.2.2.3 Expected States of Secondary Backup Protective Relays 157\u003c\/p\u003e \u003cp\u003e5.2.2.4 Expected States of Breaker Failure Protective Relays 157\u003c\/p\u003e \u003cp\u003e5.2.2.5 Expected States of Circuit Breakers 157\u003c\/p\u003e \u003cp\u003e5.3 Random Self-Regulating Algorithm 157\u003c\/p\u003e \u003cp\u003e5.3.1 Random Self-Regulating Algorithm 158\u003c\/p\u003e \u003cp\u003e5.3.2 Bionic Self-Regulating Function 160\u003c\/p\u003e \u003cp\u003e5.3.2.1 Increment Operator of Guiding Probability 160\u003c\/p\u003e \u003cp\u003e5.3.2.2 Iterative Mutation Operator 162\u003c\/p\u003e \u003cp\u003e5.3.3 Fault Diagnosis Process 164\u003c\/p\u003e \u003cp\u003e5.4 Experiment and Analysis 165\u003c\/p\u003e \u003cp\u003e5.4.1 Case Study 166\u003c\/p\u003e \u003cp\u003e5.4.2 Accuracy Test 169\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Statistical Machine Learning Model for Production Simulation of Power Systems with a High Proportion of Photovoltaics 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eXueqian Fu, Feifei Yang, Qiaoyu Ma, Na Lu, and Chunyu Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 173\u003c\/p\u003e \u003cp\u003e6.2 Methodology 174\u003c\/p\u003e \u003cp\u003e6.2.1 Long Time Scale 174\u003c\/p\u003e \u003cp\u003e6.2.1.1 Bidirectional LSTM Style-Based Generative Adversarial Networks 174\u003c\/p\u003e \u003cp\u003e6.2.1.2 Clustering with Adaptive Neighbors 176\u003c\/p\u003e \u003cp\u003e6.2.2 Short Time Scale 180\u003c\/p\u003e \u003cp\u003e6.2.2.1 Decomposition Strategy and Attention-Based Long Short-Term Memory Network 180\u003c\/p\u003e \u003cp\u003e6.2.2.2 Forecasting with Dynamic Mask 183\u003c\/p\u003e \u003cp\u003e6.3 Case Studies 185\u003c\/p\u003e \u003cp\u003e6.3.1 Long Time Scale 185\u003c\/p\u003e \u003cp\u003e6.3.1.1 Year-Round Photovoltaic Scenario Generation 185\u003c\/p\u003e \u003cp\u003e6.3.1.2 Typical Scenarios Extraction 188\u003c\/p\u003e \u003cp\u003e6.3.2 Short Time Scale 191\u003c\/p\u003e \u003cp\u003e6.3.2.1 Power Load Forecast 191\u003c\/p\u003e \u003cp\u003e6.3.2.2 Photovoltaic Power Generation Forecast 193\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Dynamic Reconfiguration of PV-TEG Hybrid Systems via Improved Whale Optimization Algorithm 199\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBo Yang, Jiarong Wang, and Yulin li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 199\u003c\/p\u003e \u003cp\u003e7.2 PV-TEG Hybrid System Model 202\u003c\/p\u003e \u003cp\u003e7.2.1 PV System Model 202\u003c\/p\u003e \u003cp\u003e7.2.2 TEG System Model 204\u003c\/p\u003e \u003cp\u003e7.2.3 PV-TEG Hybrid System Model 206\u003c\/p\u003e \u003cp\u003e7.2.4 Objective Function 207\u003c\/p\u003e \u003cp\u003e7.2.5 Performance Evaluation 208\u003c\/p\u003e \u003cp\u003e7.3 Improved Whale Optimization Algorithm 208\u003c\/p\u003e \u003cp\u003e7.3.1 Whale Optimization Algorithm 208\u003c\/p\u003e \u003cp\u003e7.3.1.1 Encircling Prey 208\u003c\/p\u003e \u003cp\u003e7.3.1.2 Bubble-Net Attack 209\u003c\/p\u003e \u003cp\u003e7.3.1.3 Random Search 209\u003c\/p\u003e \u003cp\u003e7.3.2 Design of Improved Whale Optimization Algorithm 210\u003c\/p\u003e \u003cp\u003e7.3.2.1 The Roulette Mechanism 210\u003c\/p\u003e \u003cp\u003e7.3.2.2 Nonlinear Convergence Factor 212\u003c\/p\u003e \u003cp\u003e7.4 Case Study 212\u003c\/p\u003e \u003cp\u003e7.4.1 6 × 6SquareArray 213\u003c\/p\u003e \u003cp\u003e7.4.2 6 × 10 Non-Square Array 219\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 226\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Coordinating Transactive Energy and Carbon Emission Trading Among Multi-Energy Virtual Power Plants for Distributed Learning 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePeiling Chen and Yujian Ye\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 233\u003c\/p\u003e \u003cp\u003e8.1.1 Background and Motivation 233\u003c\/p\u003e \u003cp\u003e8.1.2 Review of Previous Work 234\u003c\/p\u003e \u003cp\u003e8.2 Overall Transactive Trading Market in Heterogeneous Networked MEVPPs 236\u003c\/p\u003e \u003cp\u003e8.3 Mathematical Formulation of MEVPP Coordination Problem 238\u003c\/p\u003e \u003cp\u003e8.3.1 Objective Function 238\u003c\/p\u003e \u003cp\u003e8.3.2 Constraints 239\u003c\/p\u003e \u003cp\u003e8.3.2.1 Energy Balance Constraint 239\u003c\/p\u003e \u003cp\u003e8.3.2.2 CER Balance Constraint 239\u003c\/p\u003e \u003cp\u003e8.3.2.3 Operating Constraints of Conversion Devices 241\u003c\/p\u003e \u003cp\u003e8.3.2.4 Constraints of Energy Storage Devices 242\u003c\/p\u003e \u003cp\u003e8.3.2.5 Trading Constraints 242\u003c\/p\u003e \u003cp\u003e8.3.2.6 Network Constraints 242\u003c\/p\u003e \u003cp\u003e8.4 Adaptive Consensus ADMM 243\u003c\/p\u003e \u003cp\u003e8.4.1 Consensus ADMM 243\u003c\/p\u003e \u003cp\u003e8.4.2 Adaptive Tuning of Penalty Parameters 244\u003c\/p\u003e \u003cp\u003e8.4.3 AC-ADMM-Based Energy and CER Trading for Networked MEVPPs 244\u003c\/p\u003e \u003cp\u003e8.5 Case Studies 249\u003c\/p\u003e \u003cp\u003e8.5.1 Experimental Setup 249\u003c\/p\u003e \u003cp\u003e8.5.2 Impact of Transactive Trading 251\u003c\/p\u003e \u003cp\u003e8.5.2.1 Impact of Transactive CER Trading 252\u003c\/p\u003e \u003cp\u003e8.5.2.2 Impact of Transactive Heat Trading 254\u003c\/p\u003e \u003cp\u003e8.5.3 Impact of Network Constraints 255\u003c\/p\u003e \u003cp\u003e8.5.4 Convergence Analysis 256\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 258\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Cluster-Based Heuristic Algorithm for Collection System Topology Generation of a Large-Scale Offshore Wind Farm 263\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJincheng Li, Zhengxun Guo, and Xiaoshun Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 263\u003c\/p\u003e \u003cp\u003e9.1.1 Background and Importance 263\u003c\/p\u003e \u003cp\u003e9.1.2 Research Status 264\u003c\/p\u003e \u003cp\u003e9.1.2.1 Radial Topologys Optimization Methods 264\u003c\/p\u003e \u003cp\u003e9.1.2.2 Graph Theory-Based Methods 265\u003c\/p\u003e \u003cp\u003e9.1.2.3 Meta-Heuristic Optimization Methods 265\u003c\/p\u003e \u003cp\u003e9.2 Mathematical Model for CS Optimization in LSOWFs 266\u003c\/p\u003e \u003cp\u003e9.2.1 Composition of the LSOWF 266\u003c\/p\u003e \u003cp\u003e9.2.2 Objective Function 267\u003c\/p\u003e \u003cp\u003e9.2.3 Constraints 268\u003c\/p\u003e \u003cp\u003e9.2.3.1 Constraint on the Number of WTs per Feeder 268\u003c\/p\u003e \u003cp\u003e9.2.3.2 Constraint on the Load Capacity of Submarine Cable 269\u003c\/p\u003e \u003cp\u003e9.2.3.3 Non-Crossing Constraint of Submarine Cables 269\u003c\/p\u003e \u003cp\u003e9.2.3.4 Voltage Constraint 270\u003c\/p\u003e \u003cp\u003e9.3 Cluster-Based Topology Generation Method 270\u003c\/p\u003e \u003cp\u003e9.3.1 Polar Coordinate Clustering 270\u003c\/p\u003e \u003cp\u003e9.3.2 Radial Clustering 271\u003c\/p\u003e \u003cp\u003e9.3.2.1 Fuzzy C-Means Algorithm 272\u003c\/p\u003e \u003cp\u003e9.3.2.2 Radial Fuzzy C-Means Algorithm 272\u003c\/p\u003e \u003cp\u003e9.3.3 Dynamic Minimum Spanning Tree 274\u003c\/p\u003e \u003cp\u003e9.3.4 Firefly Algorithm-Based Optimization 276\u003c\/p\u003e \u003cp\u003e9.4 Case Study 277\u003c\/p\u003e \u003cp\u003e9.4.1 Test Case # 1 277\u003c\/p\u003e \u003cp\u003e9.4.2 Test Case # 2 278\u003c\/p\u003e \u003cp\u003e9.4.3 Cost Comparison 280\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 282\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Transmission Line Multi-Fitting Detection Method Based on Implicit Space Knowledge Fusion 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eQianming Wang, Congbin Guo, Xuan Liu, and Yongjie Zhai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 287\u003c\/p\u003e \u003cp\u003e10.2 Overall Overview of Methods 292\u003c\/p\u003e \u003cp\u003e10.3 Implicit Spatial Knowledge Fusion Structure 294\u003c\/p\u003e \u003cp\u003e10.3.1 Spatial Box Setting Module 295\u003c\/p\u003e \u003cp\u003e10.3.2 Spatial Context Extraction Module 297\u003c\/p\u003e \u003cp\u003e10.3.3 Spatial Context Memory Module 298\u003c\/p\u003e \u003cp\u003e10.4 Improved Post-Processing Structure 301\u003c\/p\u003e \u003cp\u003e10.5 Experimental Results and Analysis 303\u003c\/p\u003e \u003cp\u003e10.5.1 Experimental Dataset and Environment 303\u003c\/p\u003e \u003cp\u003e10.5.2 Comprehensive Comparative Experiment 304\u003c\/p\u003e \u003cp\u003e10.5.3 Ablation Experiment 307\u003c\/p\u003e \u003cp\u003e10.5.4 Visual Comparison Experiment 309\u003c\/p\u003e \u003cp\u003e10.6 Summary 311\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003eIndex 315\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52433237180696,"sku":"9781394253616","price":89.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394253616.jpg?v=1784852629","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/artificial-intelligence-empowered-smart-energy-systems-hardback-9781394253616","provider":"Freshly Printed Books","version":"1.0","type":"link"}