Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch

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.

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