Uncertainty-based strategic planning of charging network expansion in large urban areas

Abstract

The technological advancement of electric vehicles and charging stations emphasizes the requirements for more effective deployment strategies. To this end, a two-stage stochastic programming model is proposed to determine the location and capacity of charging stations, aiming to minimize the cost investment while maximizing expected revenues under uncertainty. The proposed methodology integrates spatial clustering techniques to identify candidate sites and incorporate demand patterns across the urban network. The outcomes of the model, applied to a case study of the city of Milan, supports a more balanced deployment by highlighting the importance of underserved areas to improve charging network efficiency, and encourage EV adoption. These findings support allocation decisions as preventing excessive concentration of charging stations in specific areas, reducing the number of chargers with inefficient utilization, and lowering operational costs for service providers in large-scale urban environment.