Optimal Planning of Public Charging Infrastructure with Local Energy Flexibility under Electric Vehicle Adoption Uncertainty and Spatial Constraints

Abstract

The rapid adoption of electric vehicles (EVs) is accelerating the expansion of public electric vehicle charging infrastructure (EVCI). However, existing planning approaches often assume sufficient grid capacity or neglect spatial constraints and economic heterogeneity across candidate locations. In practice, private EVCI operators face uncertain EV demand, grid connection limits, land-use constraints, and spatial differences in land costs and electricity selling prices. To address these challenges, a two-stage stochastic programming model is proposed for the optimal planning of public EVCI with local energy flexibility based on photovoltaic generation and battery energy storage systems; the installation of EVCI, distributed energy resources, and land utilization under EV adoption scenarios is jointly optimized. Results demonstrate that local energy flexibility enables the expansion of effective service capacity in grid-constrained nodes, reducing congestion while simultaneously improving economic and operational performance under uncertainty. Furthermore, economically efficient locational strategies considering heterogeneous electrical, spatial, and commercial constraints are identified.