Robust and Congestion-Aware Planning of Electric Vehicle Charging Stations Under Algorithmic and Parametric Uncertainty

Abstract

Planning public electric vehicle charging stations (EVCSs) in emerging urban environments requires balancing operator profitability and user accessibility while accounting for traffic congestion and uncertainty in future demand and economic conditions. However, most existing EVCS planning studies focus primarily on identifying nominal optimal solutions and provide limited insight into the reliability of deployment decisions under uncertain conditions. To address this gap, this study develops a congestion-aware bi-objective framework for EVCS siting and sizing that simultaneously maximizes annualized operator profit and minimizes aggregate additional user cost, including travel, queueing, and energy-consumption components. The study integrates metaheuristics with congestion due to peak period travel, queueing-based service assessment, Monte Carlo uncertainty analysis, reliability evaluation, and elasticity-based sensitivity assessment within a unified decision-support methodology. The approach is demonstrated for the city of Guwahati, India represented by a 53-node, 76-link road network. Results highlight that robust EVCS deployment solutions consistently converge to a narrow infrastructure envelope comprising approximately 16–18 charging stations, 366–390 chargers, and a peak demand of 22–23 MW despite uncertainty in key techno-economic parameters. Reliability analysis shows that selected compromise solutions maintain profitability and affordability targets with high probability under stochastic perturbations, while sensitivity analysis identifies charging demand, retail tariffs, and fleet battery composition as the principal drivers of outcome variability. The proposed framework provides a reproducible and reliable methodology for congestion-aware EVCS planning under uncertainty.