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.