Abstract
Conference Title: 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) Conference Start Date: 2018, May 13 Conference End Date: 2018, May 16 Conference Location: Quebec, QC, Canada Modelling plug-in electric vehicles (PEVs) charging load for use in many power system applications requires reliable estimates of a number of random variables that characterize the PEV charging process. Among these variables are the variables relevant to the driver's behaviour (e.g., arrival and departure times and daily mileage). Determining reliable estimates of these variables is challenging, since no currently sufficient real data can be relied upon for precise descriptions of these variables. The alternative is to use sample data for each variable from the available transportation mobility data, and to estimate a proper probability distribution function (PDF) that can preserve the random characteristics of each variable and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the random characteristics of each driver behaviour variable. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed sample data based on consideration of a well-known goodness of fit statistical test.