Coordinated Optimization of Orderly Charging and Grid Interaction at Electric Vehicle Charging Stations Based on Multi-Agent Reinforcement Learning

Abstract

Uncoordinated charging of large-scale electric vehicles exacerbates peak-valley differences and voltage exceedance risks in the power grid, while existing scheduling methods still have limitations in distributed decision-making, dynamic pricing, and multiobjective balancing. These problems become more significant in charging station clusters where power-electronic converters, communication links, and complex electromagnetic operating environments jointly affect grid interaction stability. In this paper, a collaborative optimization framework based on multi-agent reinforcement learning is proposed for orderly charging at electric vehicle charging stations and coordinated interaction with the power grid. First, each charging station is modeled as an autonomous agent, and distributed environment modeling is realized based on local observation information and Markov decision processes. Second, a proximal policy optimization algorithm is used to generate a dynamic service fee multiplier in a continuous action space, which is combined with a demand elasticity module to form an adaptive pricing mechanism. Finally, a composite reward system integrating grid stability, operational revenue, and user satisfaction is developed, and multi-agent convergence training is achieved through parameter sharing and generalized advantage estimation. The results confirm the overall benefits of joint optimization in load shaping, economic performance, and robustness, providing a technical reference for intelligent charging coordination under grid interaction and electromagnetic compatibility constraints.