Uncertainty quantification in load profiles with rising EV and PV adoption: the case of residential, industrial, and office buildings

TL;DR

This study evaluates various statistical metrics for quantifying uncertainty in load profiles of different building types amid rising EV and PV adoption, revealing how joint EV and PV integration can reduce overall uncertainty.

Contribution

It introduces a comparative framework for selecting effective uncertainty quantification metrics tailored to different building types under increased DER penetration.

Findings

Certain metrics effectively quantify uncertainty for specific building types.

Joint EV and PV adoption can reduce net load uncertainty.

Uncertainty reduction is most significant in office buildings.

Abstract

The integration of photovoltaic (PV) generation and electric vehicle (EV) charging introduces significant uncertainty in electricity consumption patterns, particularly at the distribution level. This paper presents a comparative study for selecting metrics for uncertainty quantification (UQ) for net load profiles of residential, industrial, and office buildings under increased DER penetration. A variety of statistical metrics is evaluated for their usefulness in quantifying uncertainty, including, but not limited to, standard deviation, entropy, ramps, and distance metrics. The proposed metrics are classified into baseline-free, with baseline and error-based. These UQ metrics are evaluated for increased penetration of EV and PV. The results highlight suitable metrics to quantify uncertainty per consumer type and demonstrate how net load uncertainty is affected by EV and PV adoption. Additionally, it is observed that joint consideration of EV and PV can reduce overall uncertainty due to compensatory effects of EV charging and PV generation due to temporal alignment during the day. Uncertainty reduction is observed across all datasets and is most pronounced for the office building dataset.