Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation

TL;DR

This paper introduces two data-driven methods to estimate residential EV charging profiles from real-world meter data, producing typical daily load patterns to aid utilities in grid planning amid increasing EV adoption.

Contribution

It presents novel least-squares and kernel density estimation methods for extracting and modeling EV charging profiles from customer meter data, addressing data scarcity and stochastic charging behaviors.

Findings

Both methods produce a distinct 'u-shaped' overnight charging profile.

Validated profiles enable utilities to better anticipate EV demand increases.

The approaches are scalable and applicable to real-world utility data.

Abstract

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.