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This article presents travel datasets of privately used vehicles for the determination of the daily charging demand of electric vehicles (EV) at a university campus and to analyse strategies to minimise the annual energy cost. The datasets have been used in the primary research article published in the Renewable Energy journal 1. The original raw data of vehicle usage is sourced from the Victorian Integrated Survey of Travel & Activity (VISTA) 2, which is an ongoing survey led by the Department of Transport and Planning of the Victorian State Government. Since 2012, data collection has been evenly distributed across each year, with 32,000 households and 82,000 individuals participating in the ongoing survey. The raw dataset is filtered and processed to obtain the daily travel distance and workplace arrival–departure times of privately used vehicles. Probability distributions and cumulative distributions of the daily travel distance and workplace arrival–departure times respectively are extracted. Using these distributions, the year-round travel data (daily travel distance and workplace arrival–departure times) is created for the desired number of EVs individually. These are used to generate the daily EV charging demand profile at the workplace so that appropriate charging strategies and cost optimisation methods can be tested. The experimental methods used to obtain the required data, from downloading the raw dataset to creating the individual EV's travel data are described in this paper.
Wu et al. (Sat,) studied this question.