Location optimization of EV charging stations: A custom K-means cluster algorithm approach

The strategic deployment of EV (EV) charging stations is crucial for promoting sustainable transportation and facilitating the widespread adoption of EVs. However, the lack of readily accessible charging station continues to be a significant barrier to the mainstream adoption of EVs. This study presents a comprehensive approach to optimizing the location of charging stations using a custom K-means clustering algorithm. The algorithm incorporates various factors including charging demand, energy consumption, population density, and existing stations, to identify optimal locations for the charging stations. The proposed methodology aims to minimize the distance between charging stations and areas with high EV demand while considering energy efficiency and avoiding redundancy near low-weighted point locations. The algorithm iteratively assigns data points to clusters and updates the centroids, converging to an optimal solution that balances coverage, accessibility, and sustainability. Folium map in Python and Python code was used for case study analysis. The innovation of the finding shows the effectiveness of the custom K-means clustering algorithm in optimizing charging station placement, by introducing a penalty term to avoid repositioning charging stations close to a low-weighted (charging demand) point charging station, also ensuring convenient access for EV owners, and promoting efficient energy utilization. The study emphasizes on strategic planning and data-driven approaches in developing a comprehensive and sustainable EV charging station network to address the challenge of limited charging station and support the growth of electric mobility.