A data-driven approach for characterising the charging demand of electric vehicles: A UK case study. (15th January 2016) - Xydas, Erotokritos; Marmaras, Charalampos; Cipcigan, Liana M.; Jenkins, Nick; Carroll, Steve; Barker, Myles
Highlights: 21, 918 charging events from 255 different charging stations in UK were analysed. A data pre-processing methodology for dealing with EVs charging data was presented. A data mining model was developed to analyse the EVs charging data. A fuzzy logic decision model was developed to characterise the EVs charging demand. Abstract: As the number of electric vehicles increases, the impact of their charging on distribution networks is being investigated using different load profiles. Due to the lack of real charging data, the majority of these load impact studies are making assumptions for the electric vehicle charging demand profiles. In this paper a two-step modelling framework was developed to extract the useful information hidden in real EVs charging event data. Real EVs charging demand data were obtained from Plugged-in Midlands (PiM) project, one of the eight 'Plugged-in Places' projects supported by the UK Office for Low Emission Vehicles (OLEV). A data mining model was developed to investigate the characteristics of electric vehicle charging demand in a geographical area. A Fuzzy-Based model aggregates these characteristics and estimates the potential relative risk level of EVs charging demand among different geographical areas independently to their actual corresponding distribution networks. A case study with real charging and weather data from three counties in UK is presented to demonstrate the modelling framework.