A Stochastic Model for Uncontrolled Charging of Electric Vehicles Using Cluster Analysis

Abstract

This paper proposes a probabilistic model for uncontrolled charging of electric vehicles (EVs). EV charging will add significant load to power systems in the coming years and, due to the convenience of charging at home, this is likely to occur in residential distribution systems. Estimating the size and shape of the load will allow necessary reinforcements to be identified. Models predicting EV charging are usually based on data from travel surveys, or from small trials. Travel surveys are recorded by hand and typically describe conventional vehicles, but represent a much larger and more diverse sample of the population. The model here utilizes both sources: trial data to parameterize the model, and survey data as the model input.Clustering is used to identify modes of vehicle use, thus reducing vehicle use to a single parameter -which can be incorporated into the model without adding significant computational burden. Two case studies are included: one investigating the aggregated charging of 50 vehicles, and one predicting the increase in after diversity maximum demand for different regions of the UK.

Large-Scale Scenarios of Electric Vehicle Charging with a Data-Driven Model of Control

“…Trip-based approaches utilize detailed travel surveys or cellphone GPS data to simulate the travel patterns of individual drivers and determine their charging behavior [5,6,7]. Statistical methods have also been used for this purpose, including to extend travel data to cover neighboring regions [8], simulate trip chains from origin-destination matrices [9,10], fit distributions of trip parameters [11,12], use clustering to identify patterns in driver travel [13,14,15] and generate new trips by modeling vehicle location and state transitions as stochastic processes [16,17]. Charging data-based models utilize charging sessions data to directly learn charging demand profiles [18,19,20,21,22,23,24,25].…”