Widespread electric vehicle (EV) adoption introduces new challenges for distribution grids due to large, localized load increases, stochastic charging behavior, and limited data availability. This paper proposes two data-driven methods to estimate residential EV charging profiles using real-world customer meter data from CenterPoint Energy serving the Houston area. The first approach applies a least-squares estimation to extract average charging rates by comparing aggregated EV and non-EV meter data, enabling a statistical method for starting and ending charge times. The second method isolates EV load from meter profiles and applies a kernel density estimation (KDE) to develop a probabilistic charging model. Both methods produce a distinct "u-shaped" daily charging profile, with most charging occurring overnight. The validated profiles offer a scalable tool for utilities to better anticipate EV-driven demand increases and support proactive grid planning.
@article{arxiv.2511.13861,
title = {Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation},
author = {Linhan Fang and Jesus Silva-Rodriguez and Xingpeng Li},
journal= {arXiv preprint arXiv:2511.13861},
year = {2025}
}