Statistical modeling of Electric Vehicle electricity consumption in the Victorian EV Trial,Australia |
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| Affiliation: | 1. CSIRO Ecosystem Sciences and CSIRO Energy Transformed Flagship, 37 Graham Road, Highett, Victoria 3190, Australia;2. CSIRO Ecosystem Sciences and CSIRO Energy Transformed Flagship, 41 Boggo Road, Dutton Park, Queensland 4102, Australia;1. Urban Planning Group, Department of Urban Science and Systems, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands;2. Information Systems in the Built Environment, Department of Urban Science and Systems, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands;1. School of Electrical, Electronic and Computer Engineering, The University of Western Australia (M018), 35 Stirling Highway, Crawley, Western Australia 6009, Australia;2. School of Engineering and Information Technology, Department of Electrical Engineering, Energy & Physics, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia;1. Ghent University – imec, IDLab, Dept. of Information Technology, Technologiepark Zwijnaarde 15, 9052 Ghent, Belgium;2. ElaadNL, Utrechtseweg 310, Building B42, 6812 AR Arnhem, The Netherlands;1. Transport Operations Research Group, Newcastle University, United Kingdom;2. School of Engineering and Computing Sciences, Durham University, United Kingdom;3. School of Electrical and Electronic Engineering, Newcastle University, United Kingdom;1. Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, United States;2. Civil and Environmental Engineering, Carnegie Mellon University, United States;3. Electrical and Computer Engineering, Carnegie Mellon University, United States |
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| Abstract: | The market share of Electric Vehicles (EVs), an attractive alternative to conventional vehicles, is expected to exceed 30% of all vehicles by 2033 in Australia. Although the expected EV uptake may place greater burdens on electricity networks, the potential impacts contributed by different EV user categories and vehicle models to peak loads at different times during the day are not well understood. This paper addresses the issue through statistical analysis of the charge events in the Victorian EV Trial in Australia as well as modeling the charging behaviors according to participant categories and vehicle models. The analysis was performed on 4933 charge events that were recorded by both private and public Electric Vehicle Supply Equipment. In total, these events consumed over 33 MW h of energy over 12,170 h by the 178 trial participants, out of which about 70% were household participants while the others were fleet participants. Based on a range of EV uptake scenarios and modeled charging behaviors from the trial, the power demand in the summer of 2032/33 was estimated for all of Victoria. The results of the simulations show that the broad scale uptake of EVs produces a relatively small increase in overall power demand (estimated to be between 5.72% and 9.79% in 2032/33). |
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| Keywords: | Electric vehicle Charge event Statistical modeling |
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