Electric vehicle charging station location determination with consideration of routing selection policies and driver’s risk preference

Abstract

Nowadays, petroleum resources reduction along with increased fossil fuels price and severe pollution in large cities have invoked the consumption of clean energy. The advancement of modern electricity generation infrastructures and substantial improvement in automobile industry make electric vehicles become the promising alternative in the common transportation fleet. Motivating people to use electric vehicles and removing its development obstacles, require to identify appropriate locations for the installation of public charging stations. Having one reserve battery as the complement is one of the primary options to reduce the electric driving range limitation for inter-city travels. This paper proposes a three-stage model consisting of: (1) regional traffic load network development using different route policies and Monte Carlo simulation, (2) vehicle battery capacity/driving range determination with consideration of drivers’ risk preference towards reserve battery, and (3) mixed integer linear programming model for charging station location selection and socket count determination. A novel metaheuristic algorithm based on scatter search and variable neighborhood search algorithm is proposed to identify non-dominated solutions. A numerical example and charging stations in the state of California are used as the case studies to validate the proposed model and algorithm performance. The results confirm that the low traffic regions near large cities would satisfy the charge demands and reduce the installation cost simultaneously.