Construction of a candidate-site model for charging stations based on land-use attributes

HUANG Yu; LIU Jie; HUANG Jianchang; LIN Liping

doi:10.3969/j.issn.1672-0032.2025.05.001

Abstract：To improve the resource allocation efficiency of electric vehicle charging infrastructure and reduce operational costs,a charging station site selection method integrating land-use attributes and multi-objective optimization is proposed.Based on the distribution data of points of interest(POI),the K-means clustering algorithm is used as a preprocessing method to initially determine potential charging station locations.By calculating the weight and number of POI types within the coverage area of each potential site,the importance of each site is determined,and an objective function is established to maximize the importance of the charging station candidate points.At the same time,considering the construction and operational costs of the charging stations and the travel loss costs for users,an objective function to minimize the total cost of the charging stations is formulated.A bi-objective planning model for the charging station site selection is built.The model is solved using both the traditional sparrow search algorithm(SSA)and an improved sparrow search algorithm(ISSA),which integrates dynamic adaptive weights,reverse learning strategies,and Cauchy mutations.An example analysis for Jinan city is presented.The results show that the ISSA-based charging station layout effectively avoids issues of excessive concentration or dispersion,with significantly better service coverage and layout balance than SSA.Compared to SSA,the ISSA site selection plan reduces the total cost by 7.43％and increases the charging station importance by 34.34％.The service buffer coverage of the charging stations solved by ISSA outperforms SSA in all types of land-use areas,especially in commercial areas,where the coverage rate of the ISSA plan is 6.79 percentage points higher than that of the SSA plan.The charging station bi-objective planning model and the optimized solving algorithm can effectively improve the rationality of the charging station layout and spatial resource allocation efficiency.