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

TL;DR

This work addresses the rising uncertainty in distribution-network net load due to increasing PV and EV adoption across residential, industrial, and office buildings. It systematically compares three UQ metric classes—baseline-free, baseline-based, and error-based—under varied DER penetration, assessing their usefulness for different consumer types. Key findings indicate that EV heightens high-end variability while PV offsets daytime load, with Wasserstein distance, MAE, and RMSE effectively capturing net-load changes; KLD and TVD often underperform due to distribution shifts and bimodality. Importantly, simultaneous PV and EV operation can partially offset uncertainty, especially in office settings where temporal overlap is strongest, offering practical guidance for DSOs and stakeholders to implement uncertainty-aware planning and operations.

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.

Figures (27)

• Figure 1: Overview of UQ metrics based on three classifications: (i) without baseline, (ii) with baseline, (iii) error-based metrics.
• Figure 2: Proposed UQ framework with per unit change in EV and/or PV penetration for different load types
• Figure 3: Annual energy injected by PV only, charged by EV only, and impact on net load
• Figure 4: Comparison of PDFs for different levels of PV and EV penetration for a small consumer
• Figure 5: Trends of Shannon entropy for residential consumers