Research On Location And Capacity Of Electric Vehicle Charging Stations Based On Bi-level Programming

With the aggravation of the world energy crisis and the increasingly severe environmental problems,energy-saving,clean,and zero-emission electric vehicles have gradually attracted the attention of all countries.However,factors such as the unbalanced development of public charging facilities for electric vehicles and unreasonable existing layouts have hindered the development of electric vehicles to a certain extent.Blind and disorderly expansion of electric vehicle charging stations will not only fail to give full play to their effectiveness but also cause waste of land resources and funds and even affect the stable operation of the power grid;scientific and reasonable electric vehicle charging station planning can not only improve the user’s charging service satisfaction and promote the promotion of electric vehicles,but also can be combined with the orderly charging strategy to improve the spatial and temporal distribution of electric vehicle charging load through vehicle network interaction,reduce the load peak valley difference,and improve the operation efficiency of public charging stations.Therefore,scientific and reasonable planning of the location and capacity of electric vehicle charging stations is of great significance to the development of electric vehicles.Planning electric vehicle charging stations involves the interests of charging station investors,electric vehicle users,and power grids.At the same time,electric vehicle users’ travel habits and charging decisions have great randomness and autonomy.Considering the above factors,this paper constructs a bi-level planning model for the location and capacity of electric vehicle public charging stations suitable for two scenarios,aiming at whether electric vehicle users accept charging scheduling control.(1)When electric vehicle users do not accept charging scheduling,a bilevel programming model is proposed to balance the interests between investment and electric vehicle users and improve user service satisfaction while optimizing global economic costs.In the upper model,the optimal financial cost is taken as the goal,and the slow and fast charging piles are combined and planned.The lower model aims at the optimal service satisfaction of electric vehicle users and characterizes the user’s charging satisfaction by charging queuing time;driving distance,expected charging amount,and actual charging amount.Compared with the existing research,the lower layer model simulates the charging decision-making process of different users.Considering the influence of charging station layout,user parking time,location area,and subsequent travel on user charging decisions,electric vehicle charging cost and charging demand are estimated more accurately.Finally,an improved particle swarm optimization algorithm based on Cauchy mutation and chaotic mapping is proposed to solve the model.(2)A new bi-level programming model is proposed in the scenario where electric vehicle users accept charging scheduling control.In the upper model,the slow and fast charging piles are combined to optimize the economic benefits.In the lower model,the charging load of electric vehicle users is predicted,and ordered charging scheduling is carried out to optimize the charging cost of users and the peak-valley difference of setting load.Different from the existing research,the model explicitly considers the influence of the orderly charging scheduling strategy of electric vehicles on the planning of charging stations,reduces the investment cost of charging stations,improves the operating efficiency,reduces the peak-valley difference of setting the load of electric vehicles,and ensures the service satisfaction of electric vehicle users.Taking a region in Beijing as an example,two models are used to solve the region,and the planning results of various models in different scenarios are compared from the aspects of global economic benefits,charging the cost of electric vehicle users,the investment cost of charging station and charging load of the electric vehicle,which verifies the effectiveness and rationality of the proposed model.