IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924024231.html

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

    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Çakıl, Fatih & Aksoy, Necati, 2025. "Reinforcement learning-based multi-objective smart energy management for electric vehicle charging stations with priority scheduling," Energy, Elsevier, vol. 322(C).
    2. Zhang, Hao & Yang, Guixiang & Lei, Nuo & Chen, Chaoyi & Chen, Boli & Qiu, Lin, 2025. "Scenario-aware electric vehicle energy control with enhanced vehicle-to-grid capability: A multi-task reinforcement learning approach," Energy, Elsevier, vol. 335(C).
    3. Pang, Xinfu & Wang, Xinyuan & Yu, Yang & Jiang, He & Zheng, Zedong, 2025. "Optimal scheduling for electric vehicle charging and discharging via double Q-learning-based multi-objective particle swarm optimization," Energy, Elsevier, vol. 339(C).
    4. Jin, Zhaorui & Fu, Shiyi & Fan, Hongtao & Tao, Yulin & Dong, Yachao & Wang, Yu & Sun, Yaojie, 2025. "Edge-cloud collaborative method for state of charge estimation of lithium-ion batteries by combining Kalman filter and deep learning," Energy, Elsevier, vol. 332(C).
    5. Soliman, Ismail A. & Tulsky, Vladimir & Abd el-Ghany, Hossam A. & Azmy, Ahmed M., 2025. "Optimizing hosting capacity and spatial deployment of EV charging stations for V2G integration in zonal distribution networks," Applied Energy, Elsevier, vol. 401(PA).

    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. Fu, Shiyi & Fan, Hongtao & Jin, Zhaorui & Ji, Fan & Tao, Yulin & Dong, Yachao & Chen, Xunyuan & Shao, Minghao & Yuan, Shuyu & Wang, Yu & Sun, Yaojie, 2026. "Recent progress in state of health estimation for lithium-ion batteries: From laboratory to practical application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PB).
    4. Fan, Yunsheng & Huang, Zhiwu & Li, Heng & Kaleem, Muaaz Bin & Wu, Yue, 2025. "State-of-health estimation for battery packs of real-world electric vehicles with cell-to-pack transfer learning," Energy, Elsevier, vol. 336(C).
    5. 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).
    6. Khosravi, Nima & Oubelaid, Adel, 2025. "Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance," Energy, Elsevier, vol. 320(C).
    7. Jin, Zhaorui & Fu, Shiyi & Fan, Hongtao & Tao, Yulin & Dong, Yachao & Wang, Yu & Sun, Yaojie, 2025. "Edge-cloud collaborative method for state of charge estimation of lithium-ion batteries by combining Kalman filter and deep learning," Energy, Elsevier, vol. 332(C).
    8. Dagang Lu & Yu Chen & Yan Sun & Wenxuan Wei & Shilin Ji & Hongshuo Ruan & Fengyan Yi & Chunchun Jia & Donghai Hu & Kunpeng Tang & Song Huang & Jing Wang, 2025. "Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles," Energies, MDPI, vol. 18(17), pages 1-52, August.
    9. 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).
    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. Xinghao Huang & Shengyu Tao & Chen Liang & Yining Tang & Jiawei Chen & Junzhe Shi & Yuqi Li & Bizhong Xia & Guangmin Zhou & Xuan Zhang, 2026. "iMOE: prediction of second-life battery degradation trajectory using interpretable mixture of experts," Nature Communications, Nature, vol. 17(1), pages 1-14, December.
    12. 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).
    13. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    14. 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.
    15. M., Abishek & Goyal, Jitendra Kumar & N., Amutha Prabha & Kouki, Mohamed, 2025. "Robust L2 based PI controller design for voltage regulation of auxiliary power units in electric vehicles through polytopic modeling," Energy, Elsevier, vol. 331(C).
    16. 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).
    17. Siyi Li & Mingrui Zhang & Robert Doel & Benjamin Ross & Matthew D. Piggott, 2025. "Deep learning predicts real-world electric vehicle direct current charging profiles and durations," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    18. 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).
    19. 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.
    20. 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 conditions," Energy, Elsevier, vol. 314(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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.