Abstract: With the continued growth of the electric vehicle (EV) fleet, the issue of cross-regional coordinated scheduling for charging infrastructure has become increasingly prominent, facing challenges such as uneven resource allocation and delayed responses. Considering the complex coupling between charging stations and the power system in a smart grid environment, this paper proposes a distributed scheduling strategy based on multi-agent deep reinforcement learning (MADRL) to achieve efficient, coordinated management of charging infrastructure and power resources. The proposed approach constructs a hierarchical decision-making architecture to jointly optimize intra-regional resource allocation and cross-regional power support, modeling the scheduling process as a Markov Decision Process (MDP) and treating regional charging stations, power nodes, and material units as independent agents. Through the multi-agent deep reinforcement learning mechanism, each agent autonomously learns optimal scheduling policies in the presence of uncertain demand and supply fluctuations, thus enabling rapid response and enhancing system robustness. Simulation results demonstrate that the proposed method effectively reduces scheduling costs and improves resource utilization and service quality. This study provides both theoretical support and practical pathways for building intelligent, efficient, and sustainable charging infrastructure.

Li Junda, Wang Tianan, Zhang Dingyi, Wu Quancai and Liu Jian. “Multi-Agent Deep Reinforcement Learning Algorithms for Distributed Charging Station Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.7 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160753

@article{Junda2025,
title = {Multi-Agent Deep Reinforcement Learning Algorithms for Distributed Charging Station Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160753},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160753},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {7},
author = {Li Junda and Wang Tianan and Zhang Dingyi and Wu Quancai and Liu Jian}
}