IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924024231.html
   My bibliography  Save this article

Community shared ES-PV system for managing electric vehicle loads via multi-agent reinforcement learning

Author

Listed:
  • Talihati, Baligen
  • Fu, Shiyi
  • Zhang, Bowen
  • Zhao, Yuqing
  • Wang, Yu
  • Sun, Yaojie

Abstract

The rapid growth of electric vehicles (EVs) is an unavoidable trend within the global energy transition. However, the substantial integration of EVs poses significant challenges to the stability and reliability of power systems. This study proposes mitigating EV load through community-shared energy storage and photovoltaic (ES-PV) systems. Within the framework of multi-agent reinforcement learning (MARL), multiple decision-making agents collaborate to manage various variables and systems in community, including energy storage charging and discharging strategies, intelligent EV charging strategies, and ES-PV system electricity pricing strategies. The coordination and optimization achieved through MARL enable these strategies to address the interdependencies and dynamic changes of the variables, thereby enhancing overall performance. Case studies in real-world scenarios demonstrate that ES-PV systems can support up to 38.68 % of EV load, increase photovoltaic self-consumption rates by 66.41 %, and significantly reduce community reliance on the distribution grid. In terms of economic performance, implementing the ES-PV system reduced community electricity expenses by up to 7.73 %, resulting in a net profit of €51,924.65 for the ES-PV system in summer. This indicates a win-win solution for both community residents and ES-PV system operators. Therefore, this framework can support a more efficient and resilient community energy utilization paradigm, accommodating the increasing prevalence of EVs and the rapid development of smart communities.

Suggested Citation

  • Talihati, Baligen & Fu, Shiyi & Zhang, Bowen & Zhao, Yuqing & Wang, Yu & Sun, Yaojie, 2025. "Community shared ES-PV system for managing electric vehicle loads via multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024231
    DOI: 10.1016/j.apenergy.2024.125039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924024231
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125039?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. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    2. Shengyu Tao & Haizhou Liu & Chongbo Sun & Haocheng Ji & Guanjun Ji & Zhiyuan Han & Runhua Gao & Jun Ma & Ruifei Ma & Yuou Chen & Shiyi Fu & Yu Wang & Yaojie Sun & Yu Rong & Xuan Zhang & Guangmin Zhou , 2023. "Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    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. Liu, Xutao & Tao, Shengyu & Fu, Shiyi & Ma, Ruifei & Cao, Tingwei & Fan, Hongtao & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Binary multi-frequency signal for accurate and rapid electrochemical impedance spectroscopy acquisition in lithium-ion batteries," Applied Energy, Elsevier, vol. 364(C).
    2. Cao, Tingwei & Xu, Yinliang & Liu, Guowei & Tao, Shengyu & Tang, Wenjun & Sun, Hongbin, 2024. "Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station," Applied Energy, Elsevier, vol. 371(C).
    3. Lei, Hongxuan & Liu, Pan & Cheng, Qian & Xu, Huan & Liu, Weibo & Zheng, Yalian & Chen, Xiangding & Zhou, Yong, 2024. "Frequency, duration, severity of energy drought and its propagation in hydro-wind-photovoltaic complementary systems," Renewable Energy, Elsevier, vol. 230(C).
    4. Zhang, Jiarui & Mao, Lei & Liu, Zhongyong & Yu, Kun & Hu, Zhiyong, 2025. "A Bayesian transfer learning framework for assessing health status of Lithium-ion batteries considering individual battery operating states," Applied Energy, Elsevier, vol. 382(C).
    5. Shengyu Tao & Ruifei Ma & Zixi Zhao & Guangyuan Ma & Lin Su & Heng Chang & Yuou Chen & Haizhou Liu & Zheng Liang & Tingwei Cao & Haocheng Ji & Zhiyuan Han & Minyan Lu & Huixiong Yang & Zongguo Wen & J, 2024. "Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Ji, Shanling & Zhang, Zhisheng & Stein, Helge S. & Zhu, Jianxiong, 2025. "Flexible health prognosis of battery nonlinear aging using temporal transfer learning," Applied Energy, Elsevier, vol. 377(PD).
    7. Tao, Junjie & Wang, Shunli & Cao, Wen & Fernandez, Carlos & Blaabjerg, Frede & Cheng, Liangwei, 2025. "An innovative multitask learning - Long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current condi," Energy, Elsevier, vol. 314(C).
    8. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).
    9. Ma, Tian & Zhang, Qi & Tang, Yanyan & Liu, Boyu & Li, Yan & Wang, Lu, 2024. "A review on the industrial chain of recycling critical metals from electric vehicle batteries: Current status, challenges, and policy recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
    10. Talihati, Baligen & Tao, Shengyu & Fu, Shiyi & Zhang, Bowen & Fan, Hongtao & Li, Qifen & Lv, Xiaodong & Sun, Yaojie & Wang, Yu, 2024. "Energy storage sharing in residential communities with controllable loads for enhanced operational efficiency and profitability," Applied Energy, Elsevier, vol. 373(C).
    11. Li, Qingbo & Zhong, Jun & Du, Jinqiao & Yi, Yong & Tian, Jie & Li, Yan & Lai, Chunyan & Lu, Taolin & Xie, Jingying, 2024. "Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits," Energy, Elsevier, vol. 294(C).
    12. Shengyu Tao & Haizhou Liu & Chongbo Sun & Haocheng Ji & Guanjun Ji & Zhiyuan Han & Runhua Gao & Jun Ma & Ruifei Ma & Yuou Chen & Shiyi Fu & Yu Wang & Yaojie Sun & Yu Rong & Xuan Zhang & Guangmin Zhou , 2023. "Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2024. "An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2," Energy, Elsevier, vol. 304(C).

    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:appene:v:380:y:2025:i:c:s0306261924024231. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.