Open Access

ARTICLE

An Optimisation Method for the Siting and Capacity of Electric Vehicle Charging Stations Considering the User’s Charging Accessibility

Bai Xiao^1,*, Jingjun Bu¹, Binbin Du², Yulin Ge², Jian Gao²
1 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Ministry of Education, Jilin, China
2 State Grid Jilin Electric Power Co., Ltd., Changchun Power Supply Company, Changchun, China
* Corresponding Author: Bai Xiao. Email:
(This article belongs to the Special Issue: Integration of Renewable Energies with the Grid: An Integrated Study of Solar, Wind, Storage, Electric Vehicles, PV and Wind Materials and AI-Driven Technologies)

Energy Engineering https://doi.org/10.32604/ee.2026.081408

Received 01 March 2026; Accepted 18 March 2026; Published online 04 August 2026

Abstract

In order to solve the problem of the mismatch between the supply of electric vehicle charging stations and charging demand of electric vehicle charging stations caused by the rapid growth of electric vehicles, and the difficulty in determining the optimal site and capacity of electric vehicle charging stations, an optimisation method for the siting and sizing of electric vehicle charging stations considering the accessibility of user charging was proposed. Firstly, the road network topology structure is established in the GIS environment, and the shortest time path of the user is planned by establishing the road impedance model and combining with the improved Dijkstra algorithm. Secondly, Latin hypercube sampling is used to stratify the factors related to EV charging demand, and the spatiotemporal distribution of this demand at each road network node is obtained through Monte Carlo simulation. Based on these spatiotemporal distribution results, the Gaussian Two-step Floating Catchment Area method is adopted to calculate the charging accessibility of electric vehicle users. Subsequently, a siting and sizing model for electric vehicle charging stations is constructed based on the spatiotemporal forecasting of charging demand, and the site selection and capacity optimisation were carried out with the goal of minimising the annual total cost and maximising the charging accessibility evaluation index, so as to balance the interests of both EV charging station investors and electric vehicle users. Finally, a non-dominated sorting genetic algorithm is used to solve the established model, and the entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution are used to rank the obtained Pareto solution set, and the optimal site selection and capacity determination scheme is selected. An analysis of the results from the proposed method verify the correctness and effectiveness of the proposed optimisation method.

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Keywords

Electric vehicle charging stations; Latin hypercubic sampling; spatiotemporal forecasting of charging demand; charging accessibility; optimisation of site selection and capacity; non-dominated sorting genetic algorithm

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