Research On Orderly Charging And Discharging Strategy Of Electric Vehicles Considering The Influence Of Temperature And Traffic

The advent of electric vehicles has injected fresh"blood"into the traditional automobile industry.In the future,when large-scale electric vehicles are connected to the system for disordered charging,it is bound to bring great challenges to the safety and stability of the system.Therefore,how to develop a reasonable and efficient orderly charging and discharging strategy for electric vehicles and guide the owners charging and discharging behavior is a matter worth studying.Firstly,the factors affecting the charging load of electric vehicles are studied.Under the influence of temperature,the battery capacity and air conditioning power model of electric vehicles are established.Under the influence of traffic factors,combined with traffic index,driving speed and other factors,the energy consumption per unit mileage model of electric vehicles is established.Secondly,according to the above model and combined with the travel characteristics of electric vehicles,the disordered charging load model of electric vehicles is established.Taking an electric vehicles in a community as an example,the Monte Carlo method is used to simulate the charging load of electric vehicles.The results show that temperature and traffic factors have significant effects on the charging load of electric vehicles.After the system is connected to large-scale electric vehicles,the peak-valley difference and variance of the total load curve of the system will be further aggravated.Moreover,with the increase of the number of electric vehicles connected to the system,the influence on the system will be more significant.Finally,the orderly charge and discharge strategy of electric vehicles is studied.According to the relationship between the electricity price and the load in each period,the time-of-use electricity price model in multiple periods is established.The advantages and disadvantages of genetic algorithm(GA)and quantum particle swarm optimization(QPSO)are analyzed,and a GA-QPSO algorithm is proposed by combining GA algorithm with QPSO algorithm.Combined with the above model,the possible charging and discharging modes of electric vehicles during parking are analyzed,and the charging and discharging load model of electric vehicles is established.The minimum variance of the total load of the system and the minimum total charging cost of all electric vehicles are selected as the multi-objective function,and the GA-QPSO algorithm is used to simulate and analyze the situation that the vehicle owner executes different strategies and the vehicle owner executes the orderly charging and discharging strategy under different responsiveness,The peak valley difference of the total load curve of the system is reduced from2275k W to 649k W,the variance is reduced from 540870k W~2 to 28105k W~2,and the total charging cost of all electric vehicles is reduced from 3670 yuan to 1112yuan,which can maximize the benefits between the system and the vehicle owner.With the increase of the vehicle owner’s responsiveness,the optimization effect of the multi-objective function is more and more significant.