Location And Capacity Of Electric Vehicle Charging Station Based On Voronoi Diagram And Improved Gravity Search Algorithm

With the rapid development of economy,fossil energy problems and environmental pollution problems have become increasingly prominent.People urgently need to change the development mode and develop green low-carbon economy.Electric vehicles can reduce environmental pollution and alleviate energy shortage,which has attracted worldwide attention.In recent years,China has also introduced a number of policies to stimulate the development of related industries.It is expected that the number of electric vehicles in China will increase rapidly in the future,and the number of electric vehicle charging stations as the support facilities of electric vehicles will also soar.If the charging stations are not scientifically planned,it will inevitably lead to some charging stations ’ one difficulty ’ and some charging stations unconcerned.Therefore,it is necessary to predict the electric vehicle charging load and locate the charging station.Firstly,the development status of electric vehicles and charging stations,the disorderly and orderly charging behavior of electric vehicle users and the charging habits of four types of electric vehicles were analyzed.Considering subjective weights and objective weights comprehensively,and using AHP weight method,a multi-objective decision-making model for location and capacity of EV charging stations is established,which takes construction and operation costs,user charging time costs and distribution network loss costs as objectives.Secondly,an improved gravitational search algorithm(IGSA)based on chaos theory and adaptive theory is proposed.In order to reduce the dependence on initial values and increase the diversity of population,a chaotic mapping composed of Logistic mapping and Tent mapping is introduced to solve the problems such as slow convergence speed of traditional gravity search algorithm and insufficient precision of solving high-dimensional problems.At the same time,the global optimal advantage adaptive guiding speed updating formula is introduced to improve the ability of the algorithm to jump out of the local optimal.Through the simulation experiment of the standard function,compared with the particle swarm optimization algorithm(PSO)and the traditional gravity search algorithm(GSA),it is proved that the improved algorithm in this paper has better optimization ability and improved optimization efficiency to some extent.Thirdly,using the Monte Carlo method,the charging load forecasting method of electric vehicles is proposed based on the disorder mode guided by order and time of use electricity price.Taking a city planning district as an example,the charging habits of four types of electric vehicles under two charging modes were counted,and then the charging loads under the two charging modes were predicted respectively,and the impact of charging loads on the power grid was analyzed.Finally,the Voronoi diagram is used to divide the service area of the charging station,and the joint solution process of the improved gravity search algorithm and the Voronoi diagram is proposed.The simulation experiment is carried out on the planned area,and the optimal configuration scheme of the charging station is obtained.The experimental results show that the model established in this paper is feasible and effective,and the proposed algorithm can effectively solve the problem of charging station location and capacity,which provides a new idea and method for EV charging station planning.