Clustering methodologies for flexibility characterization of electric vehicles supply equipment

The continuous growth of electric vehicles (EVs) poses new challenges to power systems planning and operation due to the need to meet society's decarbonization goals. In this context, clustering has emerged as a powerful tool to help understand and categorize the uncertain behavior of EV users and the electric vehicle supply equipment (EVSE) needs. However, previous studies lack empirical European EV charging data and relevance for practical applications.Therefore, to address such issues, this study evaluates different clustering techniques to identify typical EV charging profiles and, mainly, usage flexibility. The defined methodology comprises three major stages: data preprocessing, clustering application, and validation of results. We conduct benchmarking based on EV energy consumption, arrival, and sojourn times, using K-means, Gaussian mixture model, and Hierarchical clustering. This method allows greater applicability to various datasets from different regions, producing more comprehensive profiles that can provide empirical flexibility data in a visual, intuitive, and relevant approach.A use case considering EV charging data from Caltech University and Greece is utilized to test the proposed methods, demonstrating the versatility of our methodology.Specifically, Caltech features highly flexible prolonged charging sessions, while Greece exhibits quick-stay sessions with less flexibility potential. Both contexts offer opportunities to use the available flexibility for coordination with renewable energy sources and help balance the grid. This information unlocks the potential for future studies, enabling distribution system operators and charge point operators to intelligently and successfully integrate EVs into the energy system. • Quantification of EV charging process’s flexibility. • Benchmarking of clustering methods for comprehensive EV charging profiles. • Evaluation of power flexibility with real datasets. • Definition of flexibility data to be use din distribution grids planning.

Authors

Institutions

Publication Details

Journal
Green Energy and Intelligent Transportation
Published
2025-03-29
DOI
https://doi.org/10.1016/j.geits.2025.100304
Citations
4
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
2.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clustering methodologies for flexibility characterization of electric vehicles supply equipment

Hugo Morais, Cindy P. Guzmán, Marcelo Forte, Alexios Lekidis
4 citations
Green Energy and Intelligent Transportation
Advanced Battery Technologies Research
2.73
article

Clustering methodologies for flexibility characterization of electric vehicles supply equipment

Hugo Morais, Cindy P. Guzmán, Marcelo Forte, Alexios Lekidis
article en
4 citations

Abstract

The continuous growth of electric vehicles (EVs) poses new challenges to power systems planning and operation due to the need to meet society's decarbonization goals. In this context, clustering has emerged as a powerful tool to help understand and categorize the uncertain behavior of EV users and the electric vehicle supply equipment (EVSE) needs. However, previous studies lack empirical European EV charging data and relevance for practical applications.Therefore, to address such issues, this study evaluates different clustering techniques to identify typical EV charging profiles and, mainly, usage flexibility. The defined methodology comprises three major stages: data preprocessing, clustering application, and validation of results. We conduct benchmarking based on EV energy consumption, arrival, and sojourn times, using K-means, Gaussian mixture model, and Hierarchical clustering. This method allows greater applicability to various datasets from different regions, producing more comprehensive profiles that can provide empirical flexibility data in a visual, intuitive, and relevant approach.A use case considering EV charging data from Caltech University and Greece is utilized to test the proposed methods, demonstrating the versatility of our methodology.Specifically, Caltech features highly flexible prolonged charging sessions, while Greece exhibits quick-stay sessions with less flexibility potential. Both contexts offer opportunities to use the available flexibility for coordination with renewable energy sources and help balance the grid. This information unlocks the potential for future studies, enabling distribution system operators and charge point operators to intelligently and successfully integrate EVs into the energy system. • Quantification of EV charging process’s flexibility. • Benchmarking of clustering methods for comprehensive EV charging profiles. • Evaluation of power flexibility with real datasets. • Definition of flexibility data to be use din distribution grids planning.

Green Energy and Intelligent TransportationVol. 5(1)
Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT), Public Power Corporation (Greece) (GR), University of Thessaly (GR)
HORIZON EUROPE Framework Programme, Fundação para a Ciência e a Tecnologia
Affordable and clean energy
Openalex Percentile: Top 11%
Advanced Battery Technologies Research
2.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.