Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets

TL;DR

This paper evaluates three publicly available datasets to quantify seasonal and annual uncertainties in electric vehicle charging, solar generation, and load profiles, providing frameworks for scenario creation and linking extreme load events to weather data.

Contribution

It introduces a methodology for analyzing publicly available datasets to quantify uncertainties and creates frameworks for scenario generation in energy systems.

Findings

Identified extreme load weeks linked to weather conditions.

Analyzed seasonal variations in load and generation profiles.

Provided an online repository for dataset analysis and scenario creation.

Abstract

This paper aims to analyze three publicly available datasets for quantifying seasonal and annual uncertainty for efficient scenario creation. The datasets from Elaad, Elia and Fluvius are utilized to statistically analyze electric vehicle charging, normalized solar generation and low-voltage consumer load profiles, respectively. Frameworks for scenario generation are also provided for these datasets. The datasets for load profiles and solar generation analyzed are for the year 2022, thus embedding seasonal information. An online repository is created for the wider applicability of this work. Finally, the extreme load week(s) are identified and linked to the weather data measured at EnergyVille in Belgium.