| 引用本文: | 张延宇,饶新朋,周书奎,周 毅.基于深度强化学习的电动汽车充电调度算法研究进展[J].电力系统保护与控制,2022,50(16):179-187.[点击复制] |
| ZHANG Yanyu,RAO Xinpeng,ZHOU Shukui,ZHOU Yi.Research progress of electric vehicle charging scheduling algorithmsbased on deep reinforcement learning[J].Power System Protection and Control,2022,50(16):179-187[点击复制] |
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| 摘要: |
| 对电动汽车的充电过程进行优化调度有利于电网安全稳定运行，提升道路通行效率，提高可再生能源利用率，减少用户充电时间和充电费用。深度强化学习可以有效解决电动汽车充电优化调度面临的随机性和不确定性因素的影响。首先，概述了深度强化学习的工作原理，对比分析了不同种类强化学习的特点和应用场合。然后，从静态充电调度和动态充电调度两方面综述了基于深度强化学习的电动汽车充电调度算法研究成果，分析了现有研究的不足。最后，展望了该领域未来的研究方向。 |
| 关键词: 智能电网 电动汽车 深度强化学习 充电调度 |
| DOI：DOI: 10.19783/j.cnki.pspc.211454 |
| 投稿时间：2021-10-28修订日期：2022-03-12 |
| 基金项目:国家自然科学基金项目资助(62176088)；河南省科技攻关项目资助(212102210412) |
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| Research progress of electric vehicle charging scheduling algorithmsbased on deep reinforcement learning |
| ZHANG Yanyu,RAO Xinpeng,ZHOU Shukui,ZHOU Yi |
| (1. College of Artificial Intelligence, Henan University, Zhengzhou 450046, China; 2. International Joint Laboratory of
Collaborative Technology for Internet of Vehicles of Henan Province (Henan University), Zhengzhou 450046, China) |
| Abstract: |
| Optimal scheduling of the electric vehicle charging process is beneficial to the safe and stable operation of power grids. It improves road traffic efficiency, facilitates renewable energy utilization, and reduces the charging time and costs for users. Deep reinforcement learning can effectively solve the problems caused by different randomness and uncertainty in the optimal charging scheduling. This paper summarizes the working principle of deep reinforcement learning first, and makes the comparison of the characteristics and applications among different types of reinforcement learning. Then, the research results of deep reinforcement learning for EV charging scheduling are summarized in terms of both static and dynamic charging scheduling, and the shortcomings of existing research are analyzed. Finally, future research directions are discussed.
This work is supported by the National Natural Science Foundation of China (No. 62176088). |
| Key words: smart grid electric vehicles deep reinforcement learning charging scheduling |
