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Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
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
Corresponding AuthorHe, Haibo(he@ele.uri.edu)
Source PublicationIEEE TRANSACTIONS ON SMART GRID
ISSN1949-3053
2019
Volume10Issue:5Pages:5246-5257
Indexed BySCI
WOS IDWOS:000482623500049
Contribution Rank2
Funding OrganizationOffice of Naval Research [N00014-18-1-2396]
KeywordDeep reinforcement learning model-free EV charging scheduling
AbstractDriven by the recent advances in electric vehicle (EV) technologies, EVs have become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user's commuting behavior, and the pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.
Language英语
WOS SubjectEngineering, Electrical & Electronic
WOS KeywordDEMAND RESPONSE ; SERVICES ; AGGREGATOR
WOS Research AreaEngineering
Funding ProjectOffice of Naval Research[N00014-18-1-2396]
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25613
Collection工业控制网络与系统研究室
Corresponding AuthorHe HB(何海波)
Affiliation1.Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, South Kingstown, RI 02881 USA
2.Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Mobility Research Department, Toyota Research Institute, North America, Ann Arbor, MI 48105 USA
Recommended Citation
GB/T 7714
Wan, Zhiqiang,Li HP,He HB,et al. Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning[J]. IEEE TRANSACTIONS ON SMART GRID,2019,10(5):5246-5257.
APA Wan, Zhiqiang,Li HP,He HB,&Prokhorov, Danil.(2019).Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning.IEEE TRANSACTIONS ON SMART GRID,10(5),5246-5257.
MLA Wan, Zhiqiang,et al."Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning".IEEE TRANSACTIONS ON SMART GRID 10.5(2019):5246-5257.
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