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A new formulation and solution of probabilistic constrained load flow (PCLF) problem suitable for modern power systems with wind power generation and electric vehicles (EV) demand or supply is represented. The developed stochastic model of EV demand/supply and the wind power generation model are incorporated into load flow studies. In the resulted PCLF formulation, discrete and continuous control parameters are engaged. Therefore, a hybrid learning automata system (HLAS) is developed to find the optimal offline control settings over a whole planning period of power system. The process of HLAS is applied to a new introduced 14-busbar test system which comprises two wind turbine (WT) generators, one small power plant, and two EV-plug-in stations connected at two PQ buses. The results demonstrate the excellent performance of the HLAS for PCLF problem. New formulae to facilitate the optimal integration of WT generation in correlation with EV demand/supply into the electricity grids are also introduced, resulting in the first benchmark. Novel conclusions for EV portfolio management are drawn.