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

Xianhao Shen, Yexin Zhang and Decheng Wang
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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

Energies, 2022, vol. 15, issue 13, 1-14

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.

Keywords: multi-agent reinforcement learning; long–short memory network; MADDPG; smart charging (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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