Research On Optimal Control Strategy Of Large-Scale Electric Vehicle Charging Based On Swarm Intelligence Algorithm

Electric vehicles are an important strategic emerging industry in China and play an important role in achieving carbon peak and carbon neutral goals.With the strong support,electric vehicles have become more popular and are showing a trend of accelerated growth.With the continuous increase in sales,the large-scale electric vehicle charging will pose a series of threats to the stable operation of the grid,such as the large peak-to-valley difference of the grid and overload.Therefore,it is very necessary and urgent to study the optimization control of large-scale electric vehicle charging.During the charging,it is necessary to meet the constraints of charging state,charging time,distribution network capacity,etc.,in order to achieve high stability of the charging grid and maximize the interests of users.The charging optimization control problem is a typical multi-objective global optimization problem.Therefore,based on the analysis of the electric vehicle load model,this paper uses swarm intelligence algorithms such as artificial bee colony algorithm with global search ability to study the large-scale electric vehicle charging optimization control strategy considering the time scale and time and space scale.The main research work is as follows:(1)Research on modeling and evaluation of electric vehicle charging load.First,the factors affecting the charging demand of electric vehicle users are analyzed,the probability distribution of user travel characteristics is analyzed through statistical data.According to the user’s travel characteristics and electric vehicle charging power characteristics,the electric vehicle charging load model is established.Then,The Monte Carlo method is used to calculate the electric vehicle charging load curve,and the influence of the electric vehicle charging load curve on the power grid under different penetration rates is evaluated.The results showed that when the penetration rate continues to increase,the disordered charging method will increase.The fluctuation of the large power grid poses a threat to the safe operation of the distribution network.(2)Research on the time scale of integrated optimal control strategy for electric vehicle charging.First of all,in order to reduce the impact of disorderly charging of a large number of electric vehicles on the huge fluctuations of the power grid,a comprehensive charging strategy that weights random charging,electricity price guided charging,and mileage anxiety charging methods is proposed.Then,the charging load model of electric vehicles is set up considering the time scale,the multi-objective function is established to maximize the stability of the power grid and the interests of users,and the artificial bee colony algorithm is used for optimizing the charging strategy.The results show that,compared with the disordered charging strategy,the comprehensive optimization control strategy for charging proposed can effectively reduce the peak and peak-to-valley ratio of the grid load,save the user’s charging cost,and improve the utilization of power equipment.(3)Research on integrated optimal control strategy of electric vehicle charging considering space-time scale.First,in order to investigate the impact of electric vehicle charging on the power grid at the time and space scale,the Monte Carlo method is used to calculate the time and space charging load curve of electric vehicles.Then,a double-layer optimization charging strategy is proposed.The strategy first uses an algorithm to solve the comprehensive optimization control strategy,and then considers the time corresponding to the minimum fluctuation of the power grid for charging.The results show that compared with the disordered charging strategy,the double-layer optimized charging strategy saves15.3% more in charging costs,reduces the peak-to-valley difference rate by 10.46%,and reduces the mean square error of the grid curve by 21.44%.This charging strategy can effectively improve the stability of the power grid,and has promotion value and reference significance.