Optimization Scheduling of Virtual Power Plants with Electric Vehicles Based on Moth-Flame Optimization Algorithm

In recent years, distributed renewable energy generation has been vigorously developed to address energy crises, environmental pollution, and other issues. However, distributed renewable energy generation exhibits strong randomness and volatility. This paper constructs a dual-layer optimization scheduling model for virtual power plants (VPP) with electric vehicles (EVs), designs operational strategies for VPP integrating EV, and optimizes the output of each component using the moth-flame optimization algorithm. The moth-flame optimization algorithm demonstrates excellent search performance and robustness, suitable for solving complex real-world engineering problems. Simulation results indicate that coordinating output between EVs and controllable power sources can compensate for deviations in renewable energy output, reduce the grid impact caused by standalone EV grid integration, and enhance the supply reliability of virtual power plants.