Travel datasets for analysing the electric vehicle charging demand in a university campus.

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.

PV generation and load profile data of net zero energy homes in South Australia.

This paper presents the hourly Photovoltaic (PV) generation and residential load profiles of a typical South Australian Net Zero Energy (NZE) home. These data are used in the research article entitled "Energy Cost Minimization for Net Zero Energy Homes through Optimal Sizing of Battery Storage System" Sharma et al., 2019. The PV generation data is derived using the publicly accessible renewable ninja web platform by feeding information such as the region of interest, PV system capacity, losses and tilt angle. The raw load profile data is sourced from the Australian Energy Market Operator (AEMO) website, which is further processed and filtered to match the household load requirement. The processing of data has been carried out using Microsoft Excel and MATLAB software. The experimental method used to obtain the required data from the downloaded raw dataset is described in this paper. While the data is generated for the state of South Australia (SA), the method described here can be used to produce datasets for any other Australian state.