Electric Vehicle Charging Network Planning Considering Charging Driving Distance Based on Genetic Algorithm

doi:10.3969/j.issn.1008-0198.2023.03.005

Abstract

Abstract: In order to shorten the charging distance of electric vehicles and improve the charging convenience of car owners, a stochastic planning model for charging electric vehicle electrical network based on flow capturing location model(FCLM) is established. Under the premise of a given number of charging stations, by optimizing the construction address of charging stations, while ensuring that the charging distance of electric vehicles meets opportunity constraints, the average charging distance of electric vehicles in the entire transportation network is minimized. The established programming model is a 0-1 integer programming problem with opportunity constraints. The genetic algorithm based on the feasibility rule is used to solve it. In order to improve the solution performance, the crossover and mutation operators in the genetic algorithm(GA)are improved. Finally, an example based on a 25 node transportation network is used to validate the effectiveness of the proposed model and solution method. The probability distribution characteristics of charging distance under different planning boundary conditions, as well as the impact of confidence and the number of charging stations built on the planning results, are analyzed.

Key words: electric vehicle, charging network, charging driving distance, chance constraint, genetic algorithm

CLC Number:

U491
TM732

XIE Ying, ZHENG Zhong, LIU Jianfeng, GAO Xi, LI Daxiang. Electric Vehicle Charging Network Planning Considering Charging Driving Distance Based on Genetic Algorithm[J]. Hunan Electric Power, 2023, 43(3): 29-36.

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