Research progress of electric vehicle charging scheduling algorithms based on deep reinforcement learning

被引：0
|
作者：
Zhang Y. [1 ]
Rao X. [1 ]
Zhou S. [1 ]
Zhou Y. [2 ]
机构：
[1] College of Artificial Intelligence, Henan University, Zhengzhou
[2] International Joint Laboratory of Collaborative Technology for Internet of Vehicles of Henan Province, Henan University, Zhengzhou
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 16期
基金：
中国国家自然科学基金;
关键词：
charging scheduling; deep reinforcement learning; electric vehicles; smart grid;
D O I：
10.19783/j.cnki.pspc.211454
中图分类号：
学科分类号：
摘要：
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. © 2022 Power System Protection and Control Press. All rights reserved.
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页码：179 / 187
页数：8
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