Multi-Agent Deep Reinforcement Learning Algorithms for Distributed Charging Station Management

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 chargin...

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Vydáno v:International journal of advanced computer science & applications Ročník 16; číslo 7
Hlavní autoři: Junda, Li, Tianan, Wang, Dingyi, Zhang, Quancai, Wu, Jian, Liu
Médium: Journal Article
Jazyk:angličtina
Vydáno: West Yorkshire Science and Information (SAI) Organization Limited 2025
Témata:
Algorithms
Collaboration
Computer science
Critics
Decision making
Decomposition
Deep learning
Efficiency
Electric vehicle charging
Electric vehicles
Energy management
Infrastructure
Machine learning
Markov analysis
Markov processes
Methods
Multiagent systems
Optimization
Power supply
Resource allocation
Resource utilization
Scheduling
Simulation
Smart grid
Traffic
ISSN:2158-107X, 2156-5570
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Shrnutí: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.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160753