Federated Multi-Agent Deep Reinforcement Learning-Based Competitive Pricing Strategy for Charging Station Operators

With the rapid advancements in transportation electrification, the proliferation of electric vehicles (EVs) has interconnected power and transportation networks, forming the vehicle-traffic-power nexus. By setting charging prices, charging station operators (CSOs) can effectively guide the charging behavior of EVs, alleviate grid stress, and enhance profitability. This paper proposes a Nash-Stackelberg-Nash (N-S-N) game model to investigate the competitive charging pricing strategy for CSOs. We establish the stochastic user equilibrium with the elastic demand traffic assignment problem (SUE-ED-TAP) model to account for users' incomplete rationality and perception errors regarding trip costs. Furthermore, to protect the privacy of both CSOs and EV users, a federated multi-agent deep reinforcement learning-based solution method is proposed to solve this problem. In this method, a non-profit aggregator is introduced to exchange neural network parameters among agents, enabling privacy-preserving and collaborative learning without sharing CSOs' data. Case studies on two test systems show that the proposed method achieves higher profits compared to existing algorithms.