A Data-Driven Approach for Real-Time Residential EV Charging Management

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	A Data-Driven Approach for Real-Time Residential EV Charging Management
	He HB(何海波)4; Prokhorov, Danil 2; Wan, Zhiqiang 4; Li HP(李鹤鹏)3
	2018
会议日期	August 5-10, 2018
会议地点	Portland, OR, United states
关键词	Data-driven reinforcement learning EV charging management
页码	1-5
英文摘要	When electric vehicle (EV) participates in demand response with real-time electricity price, the EV charging cost can be significantly reduced by properly managing the charging schedules according to these pricing data. However, due to the existence of randomness in the pricing process of the utility and the user's commuting behavior, determining a cost-efficient charging strategy becomes challenging. Traditional model-based solutions need a model to predict the uncertainty. Constructing a model-based controller is difficult when the heterogeneity of EV users is taken into consideration. In this paper, the EV charging management problem is formulated as an Markov Decision Process (MDP) which has unknown transition probability. A data-driven approach based on deep reinforcement learning is developed to determine the optimal EV charging strategy. The proposed approach does not need any system model information. Experimental results verify the effectiveness of our proposed approach.
产权排序	2
会议录	2018 IEEE Power and Energy Society General Meeting, PESGM 2018
会议录出版者	IEEE Computer Society
会议录出版地	New York
语种	英语
ISSN号	1944-9925
ISBN号	978-1-5386-7703-2
WOS记录号	WOS:000457893900191
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/24164]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Wan, Zhiqiang
作者单位	1.MI 48105, United States 2.Toyota Research Institute North America, Ann Arbor 3.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Department of Electrical, University of Rhode Island, RI 02881, United States
推荐引用方式 GB/T 7714	He HB,Prokhorov, Danil,Wan, Zhiqiang,et al. A Data-Driven Approach for Real-Time Residential EV Charging Management[C]. 见:. Portland, OR, United states. August 5-10, 2018.