Work

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

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Power System Technology

DOI: 10.1109/powercon60995.2024.10870536

Authors: Md Umar Hashmi, Domenico Gioffrè, Simon Nagels, Dirk Van Hertem

Institutions: KU Leuven, Politecnico di Milano

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: green

Funders: Agentschap Innoveren en Ondernemen, Vlaamse regering