A Bayesian Potential-based Architecture for Mutually Beneficial EV Charging Infrastructure-DSO Coordination

This paper proposed a Weighted Bayesian Potential Game (WBPG)-based architecture framework to achieve mutual benefits for the noncooperative coordination between Electric Vehicle charging stations (EVCS) and Distribution System Operators (DSO), ensuring mutual operational benefits under demand forecast uncertainties and heterogeneous EVCS types. The proposed framework enables EVCS to dynamically optimize their charging strategies and bidding behaviors, thereby maximizing individual utilities while improving global welfare at the Bayesian Nash Equilibrium (BNE) as a win-win outcome for EVCS and DSO. To accommodate the specific information-gathering and processing constraints of each EVCS, this paper employs fictitious play and Cournot adjustment within the competitive process among EVCS as well as a dynamic pricing mechanism, enabling each EVCS to assess its expected utility and engage in self-optimizing bidding in a Bayesian context. The proposed framework also provides a systematic analysis of spot price volatility driven by EV charging demand uncertainties. Finally, the proposed approach is validated through comprehensive numerical simulations in both a small-scale scenario (15 EVCS) and a large-scale real-world case (600 EVCS). The results demonstrate that the WBPG framework outperforms traditional Mixed-Integer Linear Programming (MILP) optimization and non-cooperative Stackelberg game models, achieving a 11.4%–19.4% increase in global welfare with moderate computational time. These findings underscore the advantages of WBPG in enhancing the resilience and efficiency of EVCS-DSO coordination.