Abstract
The capable incorporation of Plug-in Hybrid Electric Vehicles (PHEVs) in the upcoming transportation area presents several technical challenges to electrical distribution networks for example voltage drop and loss increase. The energy demand of these movable loads is stochastic naturally owing to t he uncertainties accompanied by their location and amount of required energy. Accordingly, a new optimal stochastic reconfiguration procedure based on Gravitational Search Algorithm (GSA) is suggested that diminishes resistive loss and costs of radial distribution grids. The proposed technique is equipped with mutative operators to surpass the optimization process. Furthermore, a novel local smart charging pattern is recommended which lessens the congestion influence of PHEVs on system load curve successfully. The uncertainties of PHEV loads are modeled with Monte Carlo Simulation (MCS) and the proposed methodology is examined on Tai-power distribution network to validate its performance and robustness.
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