An Electric Vehicles Migration Framework for Public Institutions in Developing Countries

TL;DR

This paper presents a mathematical model to optimize the placement of electric vehicle charging stations for public institutions in developing countries, aiming to reduce costs and facilitate EV adoption.

Contribution

It introduces a mixed integer linear programming model tailored for developing countries, using real data from Morocco to optimize EV network deployment.

Findings

Minimized total costs for EV network setup in Moroccan public institutions.

Demonstrated the effectiveness of clustering for zone formation.

Provided a decision-making framework for policymakers.

Abstract

Electric vehicles, noted EV, with smaller environmental footprint than traditional gasoline vehicles or hybrids, are growing rapidly worldwide. Several countries such as Norway and Canada have successfully established their EV networks and achieved a significant progress towards their EV deployment. While the new EV technology is becoming popular in developed countries, emerging countries are lacking behind mainly because of the huge investment hurdle to establish EV networks. This paper provides an efficient mathematical model aiming to minimize the total costs involved in establishing an EV network, using real work data from Morocco. A given set of public institutions having a fleet of EVs are first grouped into zones based on clustering algorithms. Mixed integer linear programming model are developed to optimally select charging station locations within these organizations, with an objective to minimize total cost. This paper support the minimization of the investment needed to establish EV networks. The transition towards EV networks can first take place in cities, especially for public institutions fleet that have a fixed and known operating itinerary and schedule, followed by locations among cities. The mathematical models provided through this paper aim to enhance and foster policy makers' ability in making decisions related to the migration towards EV.