Plug-in hybrid electric vehicles (PHEVs) are considered as a promising solution to resolve global warming. In order to assess the impact of PHEV’s charging on distribution systems, their demand needs to be accurately predicted, especially for planning and operation studies. In this paper, a Monte Carlobased algorithm is proposed for PHEV demand modeling. Home arrival time, traveled distance, and home departure time of light duty vehicles in Tehran is considered to extract the drivers’ behavior, then multivariate student’s t-copula is used to model the dependencies between the datasets. Two charging levels which can be implemented in residential distribution networks are considered. Moreover, linear and nonlinear charging profile of batteries are included in this study to increase accuracy of the proposed algorithm. The proposed model is applied to a typical 21- node test system, considering three PHEV penetration levels for year 2026, utilizing vehicle ownership data of Iran. The impacts of PHEV charging on loading, voltage profile, overcurrent incidents, and power loss are investigated and the results are presented.
