Game-Theoretic Dynamic Pricing for EV Charging Stations Using Graph-based Multi-agent Reinforcement Learning

With the rise of electric vehicles (EVs), transportation electrification has become vital for sustainable mobility and low-carbon development. This study proposes a two-stage optimization framework for EV charging stations (CSs), combining user equilibrium (UE) modeling with multiagent reinforcement learning (MARL). A behavior-aware demand allocation model is first constructed based on real-world road networks and points of interest, capturing users' adaptive routing and charging responses under spatially heterogeneous pricing. Building upon this, a graph-enhanced MARL approach is employed to enable decentralized and competitive pricing decisions. By leveraging local graph structures, each CS agent perceives neighboring pricing and demand conditions to adapt strategies accordingly. Experimental results indicate that the proposed method increases revenue by 17.5% and effectively alleviates congestion and spatial imbalance, demonstrating its potential for coordinated optimization of transportation and power distribution systems.