Multi-Objective Optimization of Charging Dispatching for Electric Vehicle Battery Swapping Station Based on Adaptive Mutation Particle Swarm Optimization

The out-of-order charging behavior of largescale electric vehicle(EV) users will make the peak load condition of power grid more severe,so it is urgently to implement the guidance and dispatching of the charging behavior under large-scale utilization of EV.Due to the feature that the battery swapping station of EV is less impacted by the scheduling time constraint,it is easier to implement the charging dispatching by battery swapping station of EV than the scheduling of individual EVs.According to the characteristics of battery swapping station,taking the charging power of battery swapping station in different time intervals as controlled object a multi-objective dispatching strategy model is built and solved by adaptive mutation particle swarm optimization to reduce the influence of premature of standard particle swarm algorithm on optimization result to achieve optimized charging scheme for the next day.Based on the load curve of a certain region,the proposed method is simulated to verify the effectiveness of the proposed algorithm and the influences of single-objective optimization and multi-objective optimization on load curve are compared.Simulation results show that when multi-objective optimization is applied in charging strategy of battery swapping station the poor effect of filling the lowest valley portion in the load curve by singleobjective optimization can be remedied and the peak-valley difference in the load curve can be effectively reduced.