Urban centralized charging station planning method based on improved K-means algorithm

Abstract

The invention provides an urban centralized charging station planning method based on an improved K-means algorithm. The method comprises the steps that a centralized charging station location map is established according to the geographical location information and the charging demand of each charging demand point in an area to be planned; for the centralized charging station location map, a charging station planning scheme is acquired based on a method combining a graph theory central point and a K-means clustering algorithm; and the construction cost of a charging station, the charging cost of a user and the operation annual income of the charging station are used as evaluation indexes to optimize the charging station planning scheme. The urban centralized charging station planning method based on the improved K-means algorithm has the advantages that fast location of the charging station is realized based on the clustering idea; the quantitative evaluation indexes are used to realize the optimal location planning of the charging station; and a scientific basis is provided for location planning and planning scheme evaluation of the charging station.

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

Images