An illuminating and up-to-date exploration of the latest advances in AI-empowered smart energy systems
In Artificial Intelligence Empowered Smart Energy Systems, 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.
The 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.
The book also includes:
- A thorough introduction to cybersecurity, privacy, and virtual power plants
- Comprehensive demonstrations of the effective leveraging of AI technologies in energy systems
- Practical discussions of the potential of AI to create sustainable, efficient, and resilient energy systems
- Detailed case studies and real-world examples of AI’s implementation in smart energy systems
Perfect for researchers, data scientists, and policymakers, Artificial Intelligence Empowered Smart Energy Systems will also benefit graduate and senior undergraduate students in both the tech and energy industries.
Table of Contents:
List of Contributors xv
About the Editors xxi
Foreword xxiii
Preface xxv
Acknowledgments xxvii
1 Machine Learning-Based Applications for Cyberattack and Defense in Smart Energy Systems 1
Sha Peng, Mengxiang Liu, Zhenyong Zhang, and Ruilong Deng
1.1 Introduction to Machine Learning 1
1.2 Machine Learning in Attack Design 6
1.3 Machine Learning in Attack Protection 10
1.4 Machine Learning in Attack Detection 12
1.5 Machine Learning in Impact Mitigation 19
1.6 Future Directions 20
2 Enhancing Cybersecurity in Power Communication Networks: An Approach to Resilient CPPS Through Channel Expansion and Defense Resource Allocation 27
Yingjun Wu, Yingtao Ru, Jinfan Chen, Hao Xu, Zhiwei Lin, Chengjun Liu, and Xinyi Liang
2.1 Introduction 27
2.2 Mechanisms for the Classification and Propagation of Cyberattacks 28
2.3 Power Communication Network Planning Based on Information Transmission Reachability Against Cyberattacks 47
2.4 Survivability-Oriented Defensive Resource Allocation for Communication and Information Systems Under Cyberattack 71
3 Multi-Objective Real-Time Control of Operating Conditions Using Deep Reinforcement Learning 101
Ruisheng Diao, Tu Lan, Zhiwei Wang, Haifeng Li, Chunlei Xu, Fangyuan Sun, Bei Zhang, Yishen Wang, Siqi Wang, Jiajun Duan, and Di Shi
3.1 Introduction 101
3.2 Principles of Deep Reinforcement Learning 102
3.3 Real-Time Line Flow Control Using PPO 107
3.4 Dueling DQN-Based Topology Control for Maximizing Available Transfer Capabilities 110
3.5 Real-Time Multi-Objective Power Flow Control Using Soft Actor-Critic 119
4 Smart Generation Control Based on Multi-Agents 127
Lei Xi, Yixiao Wang, Lu Dong, and Jianyu Ren
4.1 Overview 127
4.2 Research on Intelligent Power Generation Control Based on Multi-Agents 128
4.3 Intelligent Power Generation Control for Islands and Microgrids 139
5 Power System Fault Diagnosis Method Under Disaster Weather Based on Random Self-Regulating Algorithm 149
Tao Wang, Liyuan Liu, Ruixuan Ying, Chunyu Zhou, Hanyan Wu, and Quanlin Leng
5.1 Introduction 149
5.2 Analytic Model for Fault Diagnosis 151
5.3 Random Self-Regulating Algorithm 157
5.4 Experiment and Analysis 165
6 Statistical Machine Learning Model for Production Simulation of Power Systems with a High Proportion of Photovoltaics 173
Xueqian Fu, Feifei Yang, Qiaoyu Ma, Na Lu, and Chunyu Zhang
6.1 Introduction 173
6.2 Methodology 174
6.3 Case Studies 185
7 Dynamic Reconfiguration of PV-TEG Hybrid Systems via Improved Whale Optimization Algorithm 199
Bo Yang, Jiarong Wang, and Yulin Li
7.1 Introduction 199
7.2 PV-TEG Hybrid System Model 202
7.3 Improved Whale Optimization Algorithm 208
7.4 Case Study 212
7.5 Conclusion 226
8 Coordinating Transactive Energy and Carbon Emission Trading Among Multi-Energy Virtual Power Plants for Distributed Learning 233
Peiling Chen and Yujian Ye
8.1 Introduction 233
8.2 Overall Transactive Trading Market in Heterogeneous Networked MEVPPs 236
8.3 Mathematical Formulation of MEVPP Coordination Problem 238
8.4 Adaptive Consensus ADMM 243
8.5 Case Studies 249
8.6 Conclusions 258
9 Cluster-Based Heuristic Algorithm for Collection System Topology Generation of a Large-Scale Offshore Wind Farm 263
Jincheng Li, Zhengxun Guo, and Xiaoshun Zhang
9.1 Introduction 263
9.2 Mathematical Model for CS Optimization in LSOWFs 266
9.3 Cluster-Based Topology Generation Method 270
9.4 Case Study 277
9.5 Conclusion 282
10 Transmission Line Multi-Fitting Detection Method Based on Implicit Space Knowledge Fusion 287
Qianming Wang, Congbin Guo, Xuan Liu, and Yongjie Zhai
10.1 Introduction 287
10.2 Overall Overview of Methods 292
10.3 Implicit Spatial Knowledge Fusion Structure 294
10.4 Improved Post-Processing Structure 301
10.5 Experimental Results and Analysis 303
10.6 Summary 311
References 312
Index 315
About the Author :
Qiang Yang, PhD, is a Full Professor at the College of Electrical Engineering at Zhejiang University, China. He is a Senior Member of the IEEE and a Distinguished Member of the China Computer Federation. His primary research interests include smart energy systems, learning-based optimization and control.
Gang Huang, PhD, is an Assistant Professor at the College of Electrical Engineering at Zhejiang University, China. He is a Senior Member of the IEEE and a Senior Member of the China Computer Federation. His primary research interests include artificial intelligence for power and energy systems.
