With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch.

Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts.

(1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch.

(2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast.

(3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch.

The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.

Is the first book on up-to-date AI-enabled computational methods for smart grid forecast and dispatch Reports novel breakthroughs in intelligent decision-making approaches for optimization of smart grid dispatch Presents recent development of deep learning and machine learning in smart grid forecast problems

Autorentext
Yuanzheng Li received the M.S. degree in electrical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2011 and the Ph.D. degree in electrical engineering from the South China University of Technology, Guangzhou, China, in 2015. He was a visiting Ph.D. student in the Department of Electrical and Electronics Engineering, University of Liverpool, UK, during January to December 2014. After obtaining the Ph.D. degree, he went to Nanyang Technical University, Singapore, and was a research fellow in School of Electrical and Electronics Engineering from June 2016 to December 2017. Currently, Dr. Li is an associate professor in School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He is also a core member of the Future Power Grid Research Institute, which is supported by STATE GRID Corporation of China. His research interests include artificial intelligence and its application in smart grid, deeplearning, reinforcement learning, optimal power system/microgrid dispatch and decision making, stochastic optimization considering large-scale integration of renewable energy into the power system and multi-objective optimization. He has authored or coauthored several peer-reviewed papers in international journals, including more than 40 IEEE Transactions papers. Some of the papers have been selected as Feature Article, ESI Highly Cited Paper, Best Conference Award, Highly Cited Journal Paper, etc. He is the associate editor of IEEE Transactions on Intelligent Vehicles and IET Renewable Power Generation. Yong Zhao received the M.S. degree and Ph.D. degree in system engineering from the Huazhong University of Science and Technology, Wuhan, China, in 1992 and 1996, respectively. He was then a postdoc in mechanical engineering during 1996 to 1998. He was promoted as an associate professor and a full professor in School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, in 1998 and 2000, respectively. He was a visiting scholar in the Department of System Engineering, University of Oxford, UK, during 2005 to 2006. Currently, he is the vice director of Future Power Grid Research Institute, which is supported by STATE GRID Corporation of China. His research interests include decision-making theory, operation research, power system markets, integrated energy systems, and smart grids. He has authored or coauthored several peer-reviewed papers in international journals and supervised more than 20 Ph.D. students. Lei Wu received the B.S. degree in electrical engineering and the M.S. degree in systems engineering from Xi'an Jiaotong University, Xi'an, China, in 2001 and 2004, respectively, and the Ph.D. degree in electrical engineering from Illinois Institute of Technology (IIT), Chicago, IL, USA, in 2008. From 2008 to 2010, he was a senior research associate with the Robert W. Galvin Center for Electricity Innovation, IIT. He was a professor with the Electrical and Computer Engineering Department, Clarkson University, Potsdam, NY, USA, till 2018. He is currently a professor with the Electrical and Computer Engineering Department, Stevens Institute of Technology, Hoboken, NJ. His primary research and teaching areas are focused on power and energy system optimization and control, with specific interests in the modeling of large-scale power systems with a high penetration of demand response and renewable energy, and community resilience microgrid. He is the recipient of Transactions Prize Paper Award from the IEEE Power and Energy Society (PES) in 2009 and the IEEE PES Student Prize Paper Award in Honor of T. Burke Hayes as an adviser in 2014. He is an IEEE fellow. Zhigang Zeng received the Ph.D. degree in systems analysis and integration from Huazhong University of Scienceand Technology, Wuhan, China, in 2003. He is currently a professor with the School of Artific

Inhalt

Chapter 1: Introduction for Smart Grid Forecast and Dispatch.- Chapter 2: Review for Smart Grid Forecast.- Chapter 3: Review for Smart Grid Dispatch.- Chapter 4: Deep Learning Based Densely Connected Network for Load Forecast.- Chapter 5: Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecast of Electric Vehicles.- Chapter 6: Dense Skip Attention based Deep Learning for Day-Ahead Electricity Price Forecast with a Drop-Connected Structure.- Chapter 7: Dirichlet Process Mixture Model Based on Relevant Data for Uncertainty Characterization of Net Load.- Chapter 8: Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power.- Chapter 9: Data-driven Bayesian Assisted Optimization Algorithm for Dispatch of Highly Renewable Energy Power Systems.- Chapter 10: Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems.- Chapter 11: Deep Reinforcement Learning Assisted OptimizationAlgorithm for Many-Objective Distribution Network Reconfiguration.- Chapter 12: Federated Multi-Agent Deep Reinforcement Learning Approach via Physic-Informed Reward for Multi-Microgrid Energy Management.- Chapter 13: Supply Function Game Based Energy Management Between Electric Vehicle Charging Stations and Electricity Distribution System.