典型文献
Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Reza Bayani;Saeed D.Manshadi;Guangyi Liu;Yawei Wang;Renchang Dai
作者机构：
San Diego State University,San Diego,CA,92182 USA;GEIRI North America,San Jose,CA,95134 USA
文献出处：
引用格式：
[1]Reza Bayani;Saeed D.Manshadi;Guangyi Liu;Yawei Wang;Renchang Dai-.Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(03):669-681
A类：
Fleets,Dispatchability,curtailed,curtailment,beforehand
B类：
Autonomous,Charging,Electric,Vehicle,Enhance,Renewable,Generation,total,generation,capacity,California,offered,by,PV,units,over,some,months,more,than,this,energy,In,research,novel,approach,reducing,renewable,increasing,system,flexibility,means,electric,vehicles,charging,coordination,presented,problem,sequential,decision,making,process,solved,fitted,iteration,algorithm,which,unlike,other,reinforcement,learning,methods,needs,fewer,episodes,Three,studies,are,validate,effectiveness,proposed,These,cases,include,aggregator,load,following,ramp,service,utilization,deterministic,results,suggest,that,through,framework,EVs,successfully,how,adjust,their,schedule,stochastic,scenarios,where,trip,times,well,solar,power,unknown
AB值：
0.696541
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
