  Replacing a portion of current light duty vehicles (LDV) with plug-in hybrid
electric vehicles (PHEVs) offers the possibility to reduce the dependence on
petroleum fuels together with environmental and economic benefits. The charging
activity of PHEVs will certainly introduce new load to the power grid. In the
framework of the development of a smarter grid, the primary focus of the
present study is to propose a model for the electrical daily demand in presence
of PHEVs charging. Expected PHEV demand is modeled by the PHEV charging time
and the starting time of charge according to real world data. A normal
distribution for starting time of charge is assumed. Several distributions for
charging time are considered: uniform distribution, Gaussian with positive
support, Rician distribution and a non-uniform distribution coming from driving
patterns in real-world data. We generate daily demand profiles by using
real-world residential profiles throughout 2014 in the presence of different
expected PHEV demand models. Support vector machines (SVMs), a set of
supervised machine learning models, are employed in order to find the best
model to fit the data. SVMs with radial basis function (RBF) and polynomial
kernels were tested. Model performances are evaluated by means of mean squared
error (MSE) and mean absolute percentage error (MAPE). Best results are
obtained with RBF kernel: maximum (worst) values for MSE and MAPE were about
2.89 10-8 and 0.023, respectively.
