Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability  

作　　者：Reza Bayani Saeed D.Manshadi Guangyi Liu Yawei Wang Renchang Dai 

机构地区：[1]San Diego State University,San Diego,CA,92182,USA [2]GEIRI North America,San Jose,CA,95134,USA

出　　处：《CSEE Journal of Power and Energy Systems》2022年第3期669-681,共13页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported by the Technology Project of State Grid Corporation of China(5100-201958522A-0-0-00).

摘　　要：A total of 19%of generation capacity in California is offered by PV units and over some months,more than 10%of this energy is curtailed.In this research,a novel approach to reducing renewable generation curtailment and increasing system flexibility by means of electric vehicles'charging coordination is presented.The presented problem is a sequential decision making process,and is solved by a fitted Q-iteration algorithm which unlike other reinforcement learning methods,needs fewer episodes of learning.Three case studies are presented to validate the effectiveness of the proposed approach.These cases include aggregator load following,ramp service and utilization of non-deterministic PV generation.The results suggest that through this framework,EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times,as well as solar power generation are unknown beforehand.

关 键 词：Reinforcement learning electric vehicle curtailment reduction dispatchability SCHEDULING 

分 类 号：TM910.6[电气工程—电力电子与电力传动]