Demand Aware Deployment and Expansion Method for an Electric Vehicles Fast Charging Network

Abstract

The rising awareness for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies and the remarkable initiatives taken by many countries are nurturing the enormous potential of Electric Vehicles (EV) of being our principal mode of transportation. EVs acceptance, however, is hindered by several challenges, among them is their shorter driving range, slower charging rate, and the lack of ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. Meanwhile, the expected immense EV load onto the power distribution sector may compromise the power quality. In this paper, we present a two stage solution to provision and dimension a DC fast charging network that minimizes the deployment cost while ensuring a certain quality of experience for charging (e.g., acceptable waiting time, shorter travel distance to charge, etc.). Further, we pay particular attention to maintain the voltage stability by adding a minimum number of voltage stabilizers upon the need to the power distribution network. We propose, evaluate and compare two CS (charging station) network expansion models to determine a cost effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future charging demands. Finally, a custom built PYTHON-based discrete event simulator is developed to test our outcomes.