Work

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

Year: 2022

Type: article

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.

Source: Energies

DOI: 10.3390/en15134582

Authors: Xianhao Shen, Yexin Zhang, Decheng Wang

Institution: Guilin University of Technology

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,303