Consideration of Wind-Solar Uncertainty and V2G Mode of Electric Vehicles in Bi-Level Optimization Scheduling of Microgrids

In recent years, the global electric vehicle (EV) sector has experienced rapid growth, resulting in major load variations in microgrids due to uncontrolled charging behaviors. Simultaneously, the unpredictable nature of distributed energy output complicates effective integration, leading to frequent limitations on wind and solar energy utilization. The combined integration of distributed energy sources with electric vehicles introduces both opportunities and challenges for microgrid scheduling; however, relevant research to inform practical applications is currently insufficient. This paper tackles these issues by first introducing a method for generating typical wind-solar output scenarios through kernel density estimation and a combination strategy using Frank copula functions, accounting for the complementary traits and uncertainties of wind and solar energy. Building on these typical scenarios, a two-level optimization model for a microgrid is created, integrating demand response and vehicle-to-grid (V2G) interactions of electric vehicles. The model's upper level aims to minimize operational and environmental costs, while the lower level seeks to reduce the total energy expenses of electric vehicles. Simulation results demonstrate that this optimization model improves the economic efficiency of the microgrid system, fosters regulated EV electricity consumption, and mitigates load variations, thus ensuring stable microgrid operation.