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Optimal self-consumption scheduling of highway electric vehicle charging station based on multi-agent deep reinforcement learning

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  • Zhou, Jianshu
  • Xiang, Yue
  • Zhang, Xin
  • Sun, Zhou
  • Liu, Xuefei
  • Liu, Junyong

Abstract

Due to the randomness of renewable energy and electric vehicles (EVs) in highway charging stations, it is difficult to ensure the consistency of renewable energy supply and EVs demand. Considering the randomness of EVs charging and renewable energy power generation, an optimal self-consumption scheduling of a highway EV charging station based on multi-agent deep reinforcement learning (MADRL) is proposed to realize the economy, self-consumption, low-carbon operation and ensure reliability of power supply. In day-ahead, the traffic flow prediction model based on the CNN-BiLSTM and the queuing model based on user psychology are built to predict the charging load. The 24-h optimal charging price is obtained by solving the incentive price optimization model and guides the orderly charging of EVs. In intra-day, considering the prediction errors of day-ahead and the diversity of charging levels, an optimal scheduling based on the MADRL is proposed. Regarding the multi-objective scheduling of the highway charging station, the multi-objective nonlinear and non-convex problem is transformed into multi-agent Markov game model. Finally, the effectiveness and optimality of the proposed method are verified on a highway charging station The results show that the proposed method can realize the economy, self-consumption and low-carbon operation of the charging station.

Suggested Citation

  • Zhou, Jianshu & Xiang, Yue & Zhang, Xin & Sun, Zhou & Liu, Xuefei & Liu, Junyong, 2025. "Optimal self-consumption scheduling of highway electric vehicle charging station based on multi-agent deep reinforcement learning," Renewable Energy, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:renene:v:238:y:2025:i:c:s0960148124020500
    DOI: 10.1016/j.renene.2024.121982
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    References listed on IDEAS

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    1. Xie, Hongbin & Song, Ge & Shi, Zhuoran & Peng, Likun & Feng, Defan & Song, Xuan, 2025. "Stable energy management for highway electric vehicle charging based on reinforcement learning," Applied Energy, Elsevier, vol. 389(C).
    2. Wang, Wenwei & Zhao, Wentao & Zhou, Xingyu & Zhang, Xinyong & Wu, Wentao & Liu, Manyu, 2025. "Deep learning-aided stochastic integrated optimization of highway service area renewable energy systems adopting a novel topology," Energy, Elsevier, vol. 338(C).
    3. Arumugam, Rajapandiyan & Subbaiyan, Thangavel, 2025. "A synergistic EV charging framework for smart cities with commitment-driven penalty mechanism and preference-based optimal charging source selection," Applied Energy, Elsevier, vol. 401(PB).
    4. Ye, Yujiang & Zhang, Tengxi & Shi, Ruifeng & Liu, Zhuangzhuang & Jia, Limin, 2025. "A multi-stage stochastic-robust planning approach for highway service area self-contained energy system considering multiple uncertainties," Energy, Elsevier, vol. 340(C).
    5. Ni, Fangyuan & Xiang, Yue & Wang, Shiqian & Hu, Zechun & Liu, Fang & Xu, Xiao & Jiang, Yi & Wang, Yang, 2025. "Charging management of electric vehicles with consumption of renewable energy," Energy, Elsevier, vol. 321(C).
    6. Song, Ge & Xie, Hongbin & Zhang, Jingyuan & Fu, Hongdi & Shi, Zhuoran & Feng, Defan & Song, Xuan & Zhang, Haoran, 2025. "Long-term efficient energy management for multi-station collaborative electric vehicle charging: A transformer-based multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 397(C).
    7. Ekaterina Dudkina & Claudio Scarpelli & Valerio Apicella & Massimo Ceraolo & Emanuele Crisostomi, 2025. "Optimised Centralised Charging of Electric Vehicles Along Motorways," Sustainability, MDPI, vol. 17(12), pages 1-15, June.

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