Empowering Transportation Electrification and Grid Planning: A Bottom-Up Predictive Modeling Framework

Abstract

Transportation electrification plays a crucial role in the transition to green energy. As households, businesses, and public entities increasingly shift from gas vehicles to electric vehicles (EVs), the demand for charging infrastructure leads to a significant rise in energy consumption. To effectively plan grid buildout, utilities need to rely on granular geographic data to pinpoint when and where EVs will start to emerge or grow in number on the grid. This process involves utilizing multiple models, including detection models to identify existing EVs that are currently unknown to utilities; propensity models to predict which customers are more likely to adopt electric vehicles in the near future; and forecasting models to anticipate which service areas will experience a greater rise in energy demand due to increasing EV adoption, thus requiring more immediate attention. While there is overlap in data sources and preliminary work on this topic, this paper outlines a blueprint for a bottom-up approach that leverages diverse data to create multiple predictive models tailored to different business needs.

Keywords: Transportation Electrification, Forecasting, Predictive Modeling, Machine Learning, Artificial Intelligence, Grid Planning, Electric Vehicle

DOI: 10.54941/ahfe1006398