Random parameters modeling of charging-power demand for the optimal location of electric vehicle charge facilities

Nowadays, Electric Vehicles (EVs) are considered a disruptive technology that will-with time-become dominant over their predecessor. EVs range and cost are closely linked, where users can essentially trade one for the other as the battery is the single largest cost of an EV. Developing countries (e.g., Jordan) face several challenges related to the current supporting environment and infrastructure. Thus, the lack of reliable charging infra-structure is a key obstacle that would slow down the EVs' expansion. This paper aims to understand the feasi-bility of instigating EV charge facilities based on different parameters including charging technologies, charging -power demand, and potential locations of EV charge facilities. For this purpose, the Maximum Covering Location Model (MCLM) is utilized to optimize the charging demand. This optimization model is based upon the antici-pated charging demand (daytime and nighttime). Also, different charging scenarios are selected to deal with different levels of charging technologies classified based on their charging speed and power. Moreover, a random parameters approach is adopted, for the first time, to model the demand for charging power in each district. A linear mixed-integer problem with multiple constraints is also formulated to optimize the charging station location and level of coverage over a relatively large metropolitan area. Thus, the study has a novel input to the prevailing charging demand modeling from coverage and financial feasibility perspectives, as well as guides stockholders and policymakers to improve EVs' adoption.