Statistical characterisation of the real transaction data gathered from electric vehicle charging stations

Abstract

Despite the many environmental benefits that a massive diffusion of electric vehicles (EVs) could bring to the urban mobility and to society as a whole, numerous are the challenges that this could pose to the electricity distribution grid, particularly to its operation and development. While uncoordinated management of EVs can lead to load imbalances, current or voltage variation excess and steep power requests, properly designed and well-coordinated integration approaches can in contrast provide flexibility, hence value, to the whole electrical system. Such step can be achieved only if real data are available and real drivers’ behaviours are identified. This paper is based on a real dataset of 400,000 EV charging transactions. It shows and analyses an important set of key figures (charge time, idle time, connected time, power, and energy) depending on driver's behaviour in the Netherlands. From these figures, it emerges a key role of the uncertainty of the relevant variables due to the drivers’ behaviour. This requires a statistical characterisation of these variables, which generally leads to multi-modal probability distributions. Thereby, this paper develops a Beta Mixture Model to represent these multi-modal probability distributions. Based on the emerged statistical facts, a number of results and suggestions are provided, in order to contribute to the important debate on the role of EVs to move to a fully decarbonised society.