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The ever increasing penetration of plug-in hybrid electric vehicles indistribution systems has triggered the need for a more accurate and at the sametime fast solution to probabilistic distribution power flow problem. In thispaper a novel algorithm is introduced based on finite sample points todetermine probabilistic density function of probabilistic distribution powerflow results. A modified probabilistic charging behavior of plug-in hybridelectric vehicles at charging stations and their overlap with residential peakload is evaluated in probabilistic distribution power flow problem. Theproposed algorithm is faster than Monte Carlo Simulation and at the same timekeeps adequate accuracy. It is applied to solve probabilistic distributionpower flow for two dimensionally different test systems and is compared withrecent probabilistic solutions. Simulation results show the accuracy andefficiency of the proposed algorithm to calculate probability density functionof uncertain outputs.

Probabilistic Distribution Power Flow Based on Finite Smoothing of Data Samples Considering Plug-in Hybrid Electric Vehicles