Research On Capacity Ratio Of Wind-Solar Hybrid Electric Vehicle Charging Station Based On Improved Particle Swarm Algorithm

In order to alleviate environmental pollution and solve the problem of the decline of power grid stability caused by a large number of electric vehicles connected to the large power grid,at the same time,combined with the characteristics of long inter-provincial channels and far grid connection points in Inner Mongolia,this thesis proposes a combination of wind-solar hybrid power generation system and electric vehicle charging station.Combined to form an off-grid wind-solar hybrid electric vehicle charging station.In order to improve the reliability and economy of its power supply,this thesis studies the capacity configuration of its fans,photovoltaic panels and energy storage devices.The specific research contents are as follows:(1)This thesis takes the off-grid wind-solar hybrid electric vehicle charging station as the research object,and establishes the mathematical model of wind turbine output and photovoltaic output based on the wind and light resource data in Inner Mongolia,considering that the energy storage battery can be efficiently utilized.The mathematical model of the charging and discharging output of the energy storage battery lays the foundation for the subsequent research on capacity allocation.(2)In view of the factors that affect the load demand of electric vehicles,this thesis proposes to use the Monte Carlo method to predict the load demand of electric vehicles.By analyzing the user’s behavior and habits,the functional relationship of battery charging with time and the mathematical relationship between battery charging time and battery state of charge,charging efficiency,and charging power are obtained.Finally,the load demand of electric vehicles within 24 hours a day is obtained through Monte Carlo simulation.(3)For the multi-objective optimization problem,the working principle of the multi-objective particle swarm optimization algorithm is introduced in detail,and the inertia weight of the multi-objective particle swarm optimization algorithm,the external archiving mechanism and the selection of the global optimal solution are improved respectively;using the test function ZDT1 and ZDT2 compare and analyze the multi-objective particle swarm optimization algorithm and the improved multi-objective particle swarm optimization algorithm in terms of convergence,standard deviation and error ratio,respectively.The results of ZDT1 show that the convergence and standard deviation of the improved multi-objective particle swarm optimization algorithm are improved respectively.9.2%,26%,and the error ratio is reduced by 12%;ZDT2 results show that the convergence and standard deviation of the improved multi-objective particle swarm algorithm are increased by 11.2% and 23.4%,respectively,and the error ratio is reduced by 17%,which verifies the improved multi-objective particle swarm.the effectiveness of the algorithm.(4)For the optimization of the capacity configuration of the off-grid wind-solar hybrid electric vehicle charging station,determine the objective function with the lowest cost and the minimum power shortage rate as the capacity configuration and the minimum power,equipped device capacity,and floor space as the capacity configuration.Constraints,combined with the wind and wind resource data in Inner Mongolia and the forecast results of electric vehicle load demand,the multi-objective particle swarm algorithm and the improved multi-objective particle swarm algorithm were used to solve the capacity configuration of wind turbines,photovoltaic panels and energy storage devices.The results are compared and analyzed,and the results show that the load power shortage rate obtained by the improved algorithm is smaller than the load power shortage rate obtained by the algorithm before the improvement under the same cost condition;the cost obtained by the improved algorithm under the same load power shortage rate It is less than the cost obtained by the algorithm before the improvement;it verifies the effectiveness of the improved multi-objective particle swarm algorithm.