SIA OpenIR  > 工业控制网络与系统研究室
A Data-Driven Approach for Real-Time Residential EV Charging Management
Wan, Zhiqiang1; Li HP(李鹤鹏)2; He HB(何海波)1; Prokhorov, Danil3
Department工业控制网络与系统研究室
Conference Name2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Conference DateAugust 5-10, 2018
Conference PlacePortland, OR, United states
Source Publication2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PublisherIEEE Computer Society
Publication PlaceNew York
2018
Pages1-5
Indexed ByEI ; CPCI(ISTP)
EI Accession number20190506458950
WOS IDWOS:000457893900191
Contribution Rank2
ISSN1944-9925
ISBN978-1-5386-7703-2
KeywordData-driven reinforcement learning EV charging management
AbstractWhen 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.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/24164
Collection工业控制网络与系统研究室
Corresponding AuthorWan, Zhiqiang
Affiliation1.Department of Electrical, University of Rhode Island, RI 02881, United States
2.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Toyota Research Institute North America, Ann Arbor
4.MI 48105, United States
Recommended Citation
GB/T 7714
Wan, Zhiqiang,Li HP,He HB,et al. A Data-Driven Approach for Real-Time Residential EV Charging Management[C]. New York:IEEE Computer Society,2018:1-5.
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