Coordinated Multi-Objective Optimization Scheduling for Electric Vehicle Swapping Station Cluster and Grid

This paper takes into account factors such as the demand of electric vehicle users, the output of wind and solar energy, and the load of the power grid. It introduces a wind-solar-storage system into the integrated energy station and establishes a joint optimization scheduling model for the wind-solar-storage integrated energy station cluster and the power grid. An inter-station electricity mutual aid strategy is introduced to improve energy utilization efficiency. The aim is to maximize the group's daily profit, minimize the total cost and loss rate of the energy storage system, and minimize the variance and peak-valley difference of the regional power grid load. Finally, simulation experiments were conducted under five different scenarios. The results show that the model has significant advantages compared with traditional integrated energy stations. Additionally, for high-dimensional multi-objective optimization problems, a high-dimensional multi-objective optimization algorithm based on multiple update strategies is proposed. It is compared with the NSGA-III and θ-DEA algorithms, and its superior performance in handling high-dimensional multi-objective complex problems is verified through evaluation indicators such as HV, GD, and IGD.