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Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
文献类型：期刊论文
| 作者 | Wan, Zhiqiang3; Prokhorov, Danil1; Li HP(李鹤鹏)2 ; He HB(何海波)3
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| 刊名 | IEEE TRANSACTIONS ON SMART GRID
 |
| 出版日期 | 2019 |
| 卷号 | 10期号:5页码:5246-5257 |
| 关键词 | Deep reinforcement learning model-free EV charging scheduling |
| ISSN号 | 1949-3053 |
| 产权排序 | 2 |
| 通讯作者 | He, Haibo(he@ele.uri.edu) |
| 英文摘要 | Driven 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. |
| WOS关键词 | DEMAND RESPONSE ; SERVICES ; AGGREGATOR |
| 资助项目 | Office of Naval Research[N00014-18-1-2396] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000482623500049 |
| 资助机构 | Office of Naval Research [N00014-18-1-2396] |
| 源URL | [http://ir.sia.cn/handle/173321/25613]  |
| 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 | He HB(何海波) |
| 作者单位 | 1.Mobility Research Department, Toyota Research Institute, North America, Ann Arbor, MI 48105 USA 2.Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, South Kingstown, RI 02881 USA |
| 推荐引用方式 GB/T 7714 | Wan, Zhiqiang,Prokhorov, Danil,Li HP,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,Prokhorov, Danil,Li HP,&He HB.(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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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