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Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks

Author

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  • Xianhao Shen

    (College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China)

  • Yexin Zhang

    (College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China)

  • Decheng Wang

    (College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China)

Abstract

The electric vehicle (EV) cluster charging strategy is a key factor affecting the grid load shifting in vehicle-to-grid (V2G) mode. The conflict between variable tariffs and electric-powered energy demand at different times of the day directly affects the charging cost, and in the worst case, can even lead to the collapse of the whole grid. In this paper, we propose a multi-agent reinforcement learning and long-short memory network (LSTM)-based online charging strategy for community home EV clusters to solve the grid load problem and minimize the charging cost while ensuring benign EV cluster charging loads. In this paper, the accurate prediction of grid prices is achieved through LSTM networks, and the optimal charging strategy is derived from the MADDPG multi-agent reinforcement learning algorithm. The simulation results show that, compared with the DNQ algorithm, the EV cluster online charging strategy algorithm can effectively reduce the overall charging cost by about 5.8% by dynamically adjusting the charging power at each time period while maintaining the grid load balance.

Suggested Citation

  • Xianhao Shen & Yexin Zhang & Decheng Wang, 2022. "Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks," Energies, MDPI, vol. 15(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4582-:d:845742
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    References listed on IDEAS

    as
    1. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    2. Tuchnitz, Felix & Ebell, Niklas & Schlund, Jonas & Pruckner, Marco, 2021. "Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning," Applied Energy, Elsevier, vol. 285(C).
    3. Frank Schneider & Ulrich W. Thonemann & Diego Klabjan, 2018. "Optimization of Battery Charging and Purchasing at Electric Vehicle Battery Swap Stations," Transportation Science, INFORMS, vol. 52(5), pages 1211-1234, October.
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