Abstract
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 behavior 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 demonstrate the approach. The importance of using these realistic profiles is illustrated by comparing three studies with those adopting data based on travel surveys or small-scale trials. Results demonstrate that using realistic profiles avoids under or overestimations; thus, ensuring better planning and operation of electricity networks.
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