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Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid

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  • Park, Keonwoo
  • Moon, Ilkyeong

Abstract

As the competitive advantages of electric vehicles, both in terms of operating costs and eco-friendly characteristics have gained attention, the demand for electric vehicles has increased, and studies for efficiently charging electric vehicles are being actively conducted. Previous studies have mainly focused on scheduling one electric vehicle visiting a charging station or scheduling multiple electric vehicles in a centralized execution method. However, a decentralized execution method that can schedule multiple vehicles according to their status is more suitable in a realistic smart grid charging environment that requires quick decisions. Therefore, we propose a multi-agent deep reinforcement learning approach with a centralized training and decentralized execution method that can derive charging scheduling for each electric vehicle. Computational experiments show that the proposed approach shows desirable performance in minimizing the operating cost of electric vehicles.

Suggested Citation

  • Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s030626192201368x
    DOI: 10.1016/j.apenergy.2022.120111
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    References listed on IDEAS

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    Cited by:

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    3. Jiankai Gao & Yang Li & Bin Wang & Haibo Wu, 2023. "Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm," Energies, MDPI, vol. 16(7), pages 1-21, April.
    4. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    5. Truong, Van Binh & Le, Long Bao, 2024. "Electric vehicle charging design: The factored action based reinforcement learning approach," Applied Energy, Elsevier, vol. 359(C).
    6. Cheng, Xiu & Li, Wenbo & Yang, Jiameng & Zhang, Linling, 2023. "How convenience and informational tools shape waste separation behavior: A social network approach," Resources Policy, Elsevier, vol. 86(PB).
    7. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    8. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).
    9. Zhou, Yanting & Ma, Zhongjing & Shi, Xingyu & Zou, Suli, 2024. "Multi-agent optimal scheduling for integrated energy system considering the global carbon emission constraint," Energy, Elsevier, vol. 288(C).
    10. P, Balakumar & Ramu, Senthil Kumar & T, Vinopraba, 2024. "Optimizing electric vehicle charging in distribution networks: A dynamic pricing approach using internet of things and Bi-directional LSTM model," Energy, Elsevier, vol. 294(C).

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