  Plug-in Electric Vehicles (PEV) exert an increasingly disruptive influence on
power delivery systems with penetration surge in the past decade. Therefore,
accurately assessing their impact plays a crucial role in managing grid assets
and maintaining power grids reliability. However, PEV loads are stochastic and
impulsive, which means they are of high power density and vary in a fast and
discrete manner. These load characteristics make conventional assessment
methods unsuitable. This paper proposes an algorithm, which captures the
inter-temporal response of grid assets and allows fast assessment through an
integrated interface. To realize these advantageous features, we establish
analytical models for two generic classes of grid assets (continuous and
discrete operating assets) and recast their cost functions in the statistical
settings of PEV charging. Distinct from simulation-based methods, the proposed
method is analytical, and thus greatly reduce the computation resources and
data required for accurate assessment. The effectiveness of the proposed
algorithm has been demonstrated on a set of power distribution networks in
Columbus metropolitan area, in comparison with the conventional assessment
methods.
