IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v238y2025ics0960148124020500.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124020500
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2024.121982?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    2. Xiang, Yue & Lu, Yu & Liu, Junyong, 2023. "Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage," Applied Energy, Elsevier, vol. 332(C).
    3. Woo, Hyeon & Son, Yongju & Cho, Jintae & Kim, Sung-Yul & Choi, Sungyun, 2023. "Optimal expansion planning of electric vehicle fast charging stations," Applied Energy, Elsevier, vol. 342(C).
    4. Hammam, Ahmed H. & Nayel, Mohamed A. & Mohamed, Mansour A., 2024. "Optimal design of sizing and allocations for highway electric vehicle charging stations based on a PV system," Applied Energy, Elsevier, vol. 376(PB).
    5. Wang, Kang & Wang, Haixin & Yang, Zihao & Feng, Jiawei & Li, Yanzhen & Yang, Junyou & Chen, Zhe, 2023. "A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 343(C).
    6. Feng, Jiawei & Hou, Shengya & Yu, Lijun & Dimov, Nikolay & Zheng, Pei & Wang, Chunping, 2020. "Optimization of photovoltaic battery swapping station based on weather/traffic forecasts and speed variable charging," Applied Energy, Elsevier, vol. 264(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Ikram & Daryoush Habibi & Asma Aziz, 2025. "Networked Multi-Agent Deep Reinforcement Learning Framework for the Provision of Ancillary Services in Hybrid Power Plants," Energies, MDPI, vol. 18(10), pages 1-34, May.
    2. Zhao, Zhonghao & Lee, Carman K.M. & Yan, Xiaoyuan & Wang, Haonan, 2024. "Reinforcement learning for electric vehicle charging scheduling: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
    3. Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    4. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    5. Ding, Yanyan & Jian, Sisi & Yu, Lin, 2025. "How to reduce carbon emissions in the urban transportation systems through carbon markets? Balancing the monetary and environmental benefits," Applied Energy, Elsevier, vol. 377(PB).
    6. Haiqing Gan & Wenjun Ruan & Mingshen Wang & Yi Pan & Huiyu Miu & Xiaodong Yuan, 2024. "Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments," Energies, MDPI, vol. 17(12), pages 1-30, June.
    7. Rabea Jamil Mahfoud & Nizar Faisal Alkayem & Emmanuel Fernandez-Rodriguez & Yuan Zheng & Yonghui Sun & Shida Zhang & Yuquan Zhang, 2024. "Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks," Mathematics, MDPI, vol. 12(2), pages 1-33, January.
    8. Nayak, Dhyaan Sandeep & Misra, Shamik, 2024. "An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty," Energy, Elsevier, vol. 290(C).
    9. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Dorrell, David G. & Li, Xiaohui & Zhan, Weipeng, 2023. "Operation optimization approaches of electric vehicle battery swapping and charging station: A literature review," Energy, Elsevier, vol. 263(PE).
    10. Rehman, Anis Ur & Ullah, Zia & Shafiq, Aqib & Hasanien, Hany M. & Luo, Peng & Badshah, Fazal, 2023. "Load management, energy economics, and environmental protection nexus considering PV-based EV charging stations," Energy, Elsevier, vol. 281(C).
    11. Amr A. Elshazly & Mahmoud M. Badr & Mohamed Mahmoud & William Eberle & Maazen Alsabaan & Mohamed I. Ibrahem, 2024. "Reinforcement Learning for Fair and Efficient Charging Coordination for Smart Grid," Energies, MDPI, vol. 17(18), pages 1-28, September.
    12. Truong, Van Binh & Le, Long Bao, 2024. "Electric vehicle charging design: The factored action based reinforcement learning approach," Applied Energy, Elsevier, vol. 359(C).
    13. Chen, Yongdong & Liu, Youbo & Zhao, Junbo & Qiu, Gao & Yin, Hang & Li, Zhengbo, 2023. "Physical-assisted multi-agent graph reinforcement learning enabled fast voltage regulation for PV-rich active distribution network," Applied Energy, Elsevier, vol. 351(C).
    14. Gönül, Ömer & Duman, A. Can & Güler, Önder, 2021. "Electric vehicles and charging infrastructure in Turkey: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    15. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
    16. Homod, Raad Z. & Munahi, Basil Sh. & Mohammed, Hayder Ibrahim & Albadr, Musatafa Abbas Abbood & Abderrahmane, AISSA & Mahdi, Jasim M. & Ben Hamida, Mohamed Bechir & Alhasnawi, Bilal Naji & Albahri, A., 2024. "Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings," Applied Energy, Elsevier, vol. 356(C).
    17. Jianxun Luo & Wei Zhang & Hui Wang & Wenmiao Wei & Jinpeng He, 2023. "Research on Data-Driven Optimal Scheduling of Power System," Energies, MDPI, vol. 16(6), pages 1-15, March.
    18. Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
    19. Zanvettor, Giovanni Gino & Fochesato, Marta & Casini, Marco & Lygeros, John & Vicino, Antonio, 2024. "A stochastic approach for EV charging stations in demand response programs," Applied Energy, Elsevier, vol. 373(C).
    20. Shi, Haojie & Xiong, Houbo & Gan, Wei & Lin, Yumian & Guo, Chuangxin, 2025. "Fully distributed planning method for coordinated distribution and urban transportation networks considering three-phase unbalance mitigation," Applied Energy, Elsevier, vol. 377(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:238:y:2025:i:c:s0960148124020500. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.