Translating the Spatial-temporal Electric Vehicle Charging Loads onto Distribution Assets

In the changing context of electricity generation and supply, brought about by pressures from decarbonization, and decentralization, rapid adoption of electric vehicles represents both an opportunity and a challenge for utilities. Electric vehicle growth yield benefits such as reduction in CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, and other air pollutants. Nevertheless, electric vehicles charging will also result in a rise in electricity consumption. Consequently, utilities will need to prepare for the impact of these significant loads on their system, through various mitigation strategies including infrastructure upgrades. In this paper, we outline a methodology for determining the feeder capacity under varying EV growth scenarios by allocating geospatially derived (in this case at the census tract level) electric vehicle charging loads to utility feeders. Thereby enabling utilities to identify the feeders that will be most impacted by electric vehicle load growth in their system by considering the change in feeder peak load that may occur as a result of EV charging load.