Optimization scheduling of photovoltaic-hydrogen-storage for new energy vehicle charging station based on deep reinforcement learning

杨 莹; 刘启航; 赵为光; 苏勋文; 李学军; 张 帅; 聂兴鹏

doi:10.3969/j.issn.1009-1831.2025.04.014

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Optimization scheduling of photovoltaic-hydrogen-storage for new energy vehicle charging station based on deep reinforcement learning

更新时间：2026-03-05

- Optimization scheduling of photovoltaic-hydrogen-storage for new energy vehicle charging station based on deep reinforcement learning
- Vol. 27, Issue 4, (2025)
- 作者机构：
  
  1. 黑龙江科技大学电气与控制工程学院,哈尔滨,15002
  2. 中国能源建设集团有限公司湖南省电力设计院,长沙,410007
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.04.014
  CLC：
- Published：2025
- 稿件说明：
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杨莹, 刘启航, 赵为光, et al. Optimization scheduling of photovoltaic-hydrogen-storage for new energy vehicle charging station based on deep reinforcement learning[J]. 2025, 27(4).
DOI：

杨莹, 刘启航, 赵为光, et al. Optimization scheduling of photovoltaic-hydrogen-storage for new energy vehicle charging station based on deep reinforcement learning[J]. 2025, 27(4). DOI： 10.3969/j.issn.1009-1831.2025.04.014.

摘要

针对新能源汽车充能时间不确定性、光伏出力随机性下充能站运营成本高的问题

同时应对大规模电动汽车（electric ve?hicle

EV）充电过程中动作变量过多的挑战

提出了一种基于深度强化学习（deep reinforcement learning

DRL）算法的光氢储充能站双层顺序优化调度模型。考虑光伏出力、分时电价、负荷不确定性和系统各设备运行效率等因素

以满足用户需求和降低充能站运营成本为目标

采用双延迟深度确定性策略梯度算法（twin delay deep deterministic policy gradient

TD3）对双层顺序调度模型进行求解。仿真结果表明：在满足用户充能需求前提下

该模型能大幅减少充能站运营成本；此外

充电桩数量增加时

模型实时调度时间不受影响且能有效减少弃光量。

Abstract

To address the high operating costs of charging stations due to the uncertainty of charging times for new energy vehicles and the randomness of photovoltaic（PV）output

as well as to tackle the challenge of excessive action variables in large-scale electric vehicle （EV）charging processes

a two-layer sequential optimization scheduling model for a PV-hydrogen-storage charging stationis is proposed based on a deep reinforcement learning（DRL）algorithm. This model considers factors such as PV output

time-of-use pricing

load uncertainty

and the operational efficiency of each piece of equipment in the system

aiming to meet user demands while reducing the operating costs of the charging station. The twin delayed deep deterministic policy gradient（TD3）algorithm is employed to solve the two-layer sequential scheduling model. The simulation results show that the model can greatly reduce the operating cost of charging station under the premise of meeting the charging demand of users. In addition

when the number of charging piles increases

the real-time scheduling time of the model is not affected and solar curtailmentcan be effectively reduced.

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