Optimisation of Electric Vehicle Charging Stations Planning Based on Macro and Micro Perspectives

The growing ownership of electric vehicles in urban areas leads to increasing demand for public charging spaces. With existing charging facilities failing to match the constantly increasing demand for charging, it is necessary to plan for new charging infrastructure. A two-stage approach is proposed for public charging infrastructure planning from both macro and micro perspectives. Firstly, a linear regression model with an exponential elasticity function is used to estimate charging demand, considering comprehensive charging demand factors. Secondly, effective served charging demand (ESCD) is proposed to accurately calculate the charging demand of effective service areas, considering the distance impact factor and competition among neighbouring charging stations. A capacitated maximal service location model (CMSLM) is proposed to optimise the spatial layout of public charging stations by maximizing their ESCD while considering investment budget and charging station capacity limits. CMSLM is solved using sparrow search algorithm from both macro and micro perspectives. The proposed approach is applied to Guangzhou, China, as a case study. Results show that when the investment budget is increased to 5 million CNY, the ESCD of all districts under the macro and micro optimisation perspectives increases by an average of 41.0% and 34.1%, respectively. Optimised charging stations can remedy the spatial imbalance between charging demand and existing charging station distribution, laying the foundation for further construction implementation.