Abstract

This study addresses the limitations of Electric Vehicles Charging Stations (EVCS) in Montréal, Québec. Momentum, the growth of Electric Vehicles (EVs) is projected to accelerate substantially. However, this growth is hindered by limited EVCS, particularly in shopping center settings where high cost and spatial limitations pose significant challenges. This study addresses these gaps by proposing an optimization model to support cost-effective EVCS placement.
The problem employs Mixed-Integer Nonlinear Programming (MINLP), categorizing it as a capacitated multi-facility location allocation challenge. This approach is designed to minimize the total lifecycle costs for property owners, including costs related to equipment, electrical system equipment, operational and maintenance, fixed costs, and dismantling expenses.
The optimization process involves determining the desired number of ports in the parking, based on the available estimation of the EV growth as a first step. Afterward, determining parking blocks based on the parking size to allocate the different types of ports into them. Then, various port type combinations, are distributed into all the generated block combinations. The proposed formulation is a Life Cycle Cost (LCC)-Based optimization designed to achieve minimum costs. The solution introduces a combinatorial optimization algorithm that combines dynamic programming and brute-force search to ensure all potential configurations are considered.
In conclusion, this study provides a framework for shopping center owners to adapt their parking facilities to EVs. The proposed cost formulation provides a financially sustainable solution for EVCS placements. Through this approach, this study offers a practical method to enhance EV infrastructure within commercial indoor parking environments, balancing financial and logistical considerations