A robust Pareto model for electric vehicle charging station deployment in urban areas considering psychology effects of drivers

A successful transition from gas-powered to electric vehicles (EVs) depends on identifying the most convenient locations for electric vehicle charging stations (EVCS), particularly in urban areas. While EVCS location problems have been addressed in the literature, this study considers the ambiguity of EV drivers' range anxiety and charging demand to explore the EVCS deployment in continuous and discrete solution spaces, representing roads and parking facilities in the real-world. Additionally, our paper highlights the novelty of including the negative psychology effects experienced by both electric vehicle (EV) and fuel vehicle (FV) drivers due to the ICEing problem, where fuel vehicles (FVs) block EVCS access. This paper proposes a comprehensive framework that includes a spatio-temporal Gaussian process model for predicting charging demand, a multi-objective EVCS location model for an EVCS deployment, and a Scenario-based Multi-Objective min-max Robust Pareto (SMORP) model with ambiguous charging demand and drivers' range anxiety for a robust Pareto EVCS deployment. The proposed algorithms identify the optimal and robust Pareto fronts for EVCS deployments. We validate the models using a case study of an urban area. The resulting EVCS deployment enables the selection of optimal EVCS locations among discrete parking facilities and identifies continuous coordinates for curb parking space for EV charging.