Deep Reinforcement Learning Based Optimization for Charging of Aggregated Electric Vehicles

被引：0

作者：

Zhao X. ^{[1
]}

Hu J. ^{[1
]}

机构：

[1] State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Changping District, Beijing

来源：

Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Aggregated electric vehicles; Deep reinforcement learning; Distributed deployment; Optimization of charging strategy; TOU tariff;

D O I：

10.13335/j.1000-3673.pst.2020.1418

中图分类号：

学科分类号：

摘要：

With the popularization of electricity data acquisition systems, the application of data-driven machine learning methods has played a significant role on optimal decision-making in demand response. In this paper, based on the real-time feedback data from the charging monitoring system and TOU tariff, a deep reinforcement learning (DRL) method is proposed to optimize the charging strategy of the electric vehicles (EVs) from the perspective of aggregator. A twin delay deep deterministic policy gradient (TD3) algorithm is implemented to model the charging process of a single vehicle. By adding randomly the noises in the states of the trained agent, our model attained generalized abilities to control EV charging strategies under divergent states. With the distributed deployment of the well-trained agents, this method realizes the real-time optimization of aggregated EVs' charging strategy, which is proved with examples. © 2021, Power System Technology Press. All right reserved.

引用

页码：2319 / 2327

页数：8

共 19 条

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