A robust Pareto model for electric vehicle charging station deployment in urban areas considering psychology effects of drivers
提出一个综合框架，考虑驾驶员里程焦虑和充电需求的不确定性，以及燃油车占用充电站带来的负面心理效应，通过鲁棒帕累托模型优化城市电动汽车充电站的连续和离散选址。
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.