Vehicle–Grid Interaction Pricing Optimization Considering Travel Probability and Battery Degradation to Minimize Community Peak–Valley Load

Vehicle-to-Grid (V2G) technology has been widely applied in recent years. Under the time-of-use pricing, users independently decide the charging and discharging behavior to maximize economic benefits, charging during low-price periods, discharging during high-electricity periods, and avoiding battery degradation. However, such behavior under inappropriate electricity prices can deviate from the grid’s goal of minimizing peak–valley load difference. Based on the basic electricity data of a community in Beijing and electricity vehicle (EV) random travel behavior obtained through Monte Carlo simulation, this study establishes a user optimal decision model that is influenced by battery degradation and electricity costs considering depth of discharge, charging rate, and charging energy loss. A mixed-integer linear programming algorithm with the objective of minimizing the cost of EV users is constructed to offer the participation power of V2G. By analyzing grid load fluctuations under different electricity pricing strategies, the study derives the formulation and adjustment rules for optimal electricity pricing that achieve ideal load stabilization. Under 30% V2G participation, the relative fluctuation of grid load is reduced from 31.81% to 5.19%. This study addresses the challenge of obtaining optimal electricity prices to guide users to participate in V2G to minimize the peak–valley load fluctuation.