GOODNESS OF FIT OF PROBABILISTIC MODELS FOR ELECTRIC VEHICLE CHARGING BEHAVIOUR
Letetia M. Addison1, Govinda A. Hosein2 and Sanjay Bahadoorsingh3*
1Faculty of Science and Technology, The University of the West Indies, Trinidad
2,3Faculty of Engineering, The University of the West Indies, Trinidad
1Email:letetia.addison@sta.uwi.edu
2Email: govinda.hosein@gmail.com
3Email: sanjay.bahadoorsingh@sta.uwi.edu *(Corresponding author)
Abstract:
Electric vehicles (EVs) have a number of environmental benefits in an era where fossil fuels have dominated. As such, the upgrade of electricity distribution grids to suit the needs of the modern world where the use of EVs can be accommodated is essential. Management of EV penetration is necessary, since uncoordinated charging can produce load imbalances and sharp variations in current, voltages and power. In order to assess the needs of such a system, estimates of random variables reflecting charging behaviour are necessary, particularly in cases where real data is insufficient. An attempt is made to assess some probabilistic models based on weekday load curves derived from the charging process. Level 1 EV charging profiles for uncoordinated charging schemes over one year for a data set consisting of 348 vehicles corresponding to 200 households are analysed and compared. Charging characteristics are reviewed and probability models are validated by goodness of fit statistics. Probability distribution functions (PDFs) which provide the best fit for these weekday load profiles are identified among the Johnson SB, Generalised Gamma and Dagum functions. This can provide an insight into estimation of PDFs based on EV charging behaviours, in order to build and assess models associated with transportation mobility data in other regions.
Keywords: Electric Vehicles, Probability distribution function, Goodness of fit test, Uncoordinated charging
https://doi.org/10.47412/QZWP9167
