Electric vehicle (EV) fast charging stations often experience high peak charging loads and strong fluctuations. Therefore, developing an energy management strategy for the energy storage system is crucial to improve load characteristics and reduce peak power. Addressing these challenges, this paper proposes a fast-charging station energy management strategy based on deep reinforcement learning. Firstly, the load characteristics of the charging station are analyzed. Subsequently, a mathematical optimization model is established, aiming to minimize the daily electricity purchase cost of the fast charging station while considering peak power reduction as a constraint. The control strategy is designed using a deep deterministic policy gradient algorithm. To validate the efficacy of the proposed deep reinforcement learning control strategy, a case study is conducted. The results demonstrate the significant reduction in peak load power, which validates the efficacy of the strategy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep reinforcement learning method for energy management in fast charging station


    Contributors:
    Chen, Shihao (author) / Yin, Pengcheng (author) / Bao, Yan (author) / Wang, Zhihao (author) / Shi, Jinkai (author)


    Publication date :

    2023-11-28


    Size :

    1915055 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Electric vehicle charging station charging power distribution method based on deep reinforcement learning

    XIA YONGXIANG / CHENG ZHONGYI / ZHANG JIAQI | European Patent Office | 2025

    Free access

    A Reinforcement Learning-Augmented Lyapunov Optimization Approach to DC Fast Charging Station Management

    Abbasi, Mohammad Hossein / Arjmandzadeh, Ziba / Mishra, Dillip Kumar et al. | IEEE | 2024


    Electric vehicle charging station pricing optimization method based on deep reinforcement learning

    YUAN QUAN / LI PENG / MAO MINGXUAN et al. | European Patent Office | 2024

    Free access

    Optimal EV Fast Charging Station Deployment Based on a Reinforcement Learning Framework

    Zhao, Zhonghao / Lee, Carman K. M. / Ren, Jingzheng et al. | IEEE | 2023