As governments pursue decarbonization, electric vehicle (EV) adoption is accelerating rapidly in California and elsewhere. This transition poses new challenges for electricity distribution networks, which were not originally designed to handle such concentrated and growing demand. There is a growing expectation that EVs will drive distribution capacity investments in the future.

The existing literature primarily relies on engineering simulation exercises to quantify the impact of EV growth on infrastructure needs. This project aims to leverage publicly available data on planned distribution grid investments from Pacific Gas and Electric (PG&E) to identify where these investments are taking place and evaluate whether they are concentrated in regions experiencing more rapid EV growth. This has important equity implications, as the costs of building infrastructure to meet EV-related load growth are spread across all customers, including non-EV owners who tend to be lower-income households.

To address these questions, the project will employ geo-spatial mapping software (QGIS), statistical programming, and machine learning techniques to quantify the relationship between many correlated covariates and planned distribution network investments.