A density-based spatial clustering and linear programming method for electric vehicle charging station location and price optimization

This article presents a linear optimization method for optimal placement of Electric Vehicle Charging Stations at optimal consumer charging cost in large urban networks. The optimization model is developed using Hefei city as a case study. Various factors such as population, population density, traffic flow, Electric vehicle penetration in transport network, road network, and electric vehicle routing are integrated to maximize the charging station utility while minimizing the consumer charging cost. To effectively handle large datasets density-based spatial clustering of applications with noise is employed to reduce data size while preserving model’s computational efficiency. The optimization model is solved using linear programming by incorporating key factors such as seasonal utility grid tariffs, current consumer charging cost, traffic flow, demand density, coverage radius, and station capacity. The optimization model is validated through simulation studies on Hefei city’s road network with 35% increase in electric vehicle charging station coverage and 5% reduction in consumer charging cost compared to current locations and tariff. This approach effectively identifies optimal locations for charging stations by combining urban data analytic and optimization technique. The model offers a novel solution to the challenge of planning electric vehicle charging stations and helps to advance the deployment of electric vehicle infrastructure in the city of Hefei with global sustainability goals.