DOI:10.35833/MPCE.2020.000552 Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review Page view: 0        Net amount: 255 Author: Di Cao1,Weihao Hu1,Junbo Zhao2,Guozhou Zhang1,Bin Zhang1,Zhou Liu3,Zhe Chen3,Frede Blaabjerg3 Author Affiliation: 1.Wide-area Measurement and Control Sichuan Provincial Key Laboratory, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China;2.Department of Electrical and Computer Engineering, Mississippi State University, Starkville, Mississippi, USA;3.Department of Energy Technology, Aalborg University, Aalborg, Denmark Foundation: This work was supported by the Sichuan Science and Technology Program (Sichuan Distinguished Young Scholars) (No. 2020JDJQ0037). Abstract: With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed. Keywords: Reinforcement learning ; deep reinforcement learning ; power system operation and control ; optimization Received:July 31, 2020               Online Time:2020/12/03 View Full Text       Download reader