A planning method for urban centralized charging stations based on improved k-means algorithm

Abstract

An urban centralized charging station planning method based on the improved K-means algorithm. It includes establishing a centralized charging station site selection map based on the geographical location information and charging demand of each charging demand point in the area to be planned, and based on the graph theory central point and K-means clustering algorithm for the centralized charging station site selection map The combined method is used to obtain the charging station planning scheme; the charging station construction cost, user charging cost and charging station operation annual income are used as evaluation indicators to optimize the above charging station planning scheme. The beneficial effects of the urban centralized charging station planning method based on the improved K-means algorithm provided by the present invention: the fast site selection of the charging station is realized based on the clustering idea, and at the same time, the optimal location planning of the charging station is realized by using quantitative evaluation indicators, which can Provide a scientific basis for the site selection planning and planning scheme evaluation of charging stations.

Description

technical field [0001] The invention belongs to the technical field of planning and optimization of centralized charging stations for electric vehicles, and in particular relates to a method for planning urban centralized charging stations based on an improved K-means algorithm. Background technique [0002] The current imperfect charging infrastructure has become an important factor restricting the rapid development of electric vehicles, especially the insufficient number of centralized charging stations that undertake fast charging of electric vehicles in a large area. As a bridge connecting electric vehicle users and the power grid, the reasonable layout of the charging station can not only reduce the impact on the power grid, but also provide users with fast and convenient charging services, realize the friendly interaction between users, charging stations and the power grid, and further promote the development of electric vehicles. The rapid development of the automobil...

AI Technical Summary

Problems solved by technology

However, there are still some problems. For example, due to the general use of intelligent algorithms such as genetic algorithm and particle swarm algorithm, the parameter setting often depends on experience. As a result, it is difficult to guarantee the optimality of the charging station site selection results; Basic facts such as limited customer source and customer carrying capacity of the website

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