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Statistical Representation of EV Charging: Real Data Analysis and Applications

The electrification of the transport sector is posed to create challenges but also opportunities for the electricity system. In this transition, it is crucial to understand the charging behav-ior of electric vehicles (EVs) so detailed studies can be carried out. However, to date, EV data is scarce. This paper proposes the use of probability density functions based on Gaussian Mixture Models (GMMs) to represent key charging metrics of EVs. These GMMs are then combined to produce realistic EV profiles needed in diverse studies. Real data from 221 EVs part of the largest trial in the UK and Europe (My Electric Avenue) is used to demon-strate the approach. The importance of using these realistic pro-files is illustrated by comparing three studies with those adopting data based on travel surveys or small-scale trials. Results demon-strate that using realistic profiles avoids under or overestima-tions; thus, ensuring better planning and operation of electricity networks.


Jairo Quirós-Tortós    
University of Costa Rica
Costa Rica

Alejandro Navarro-Espinosa    
University of Chile

Luis F. Ochoa    
The University of Melbourne

Timothy Butler    
EA Technology
United Kingdom


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