In this paper, the optimal energy management of MGs (micro grids) including RESs (renewable energy sources), PHEVs (plug-in hybrid electric vehicles) and storage devices is studied by a new stochastic framework that considers the uncertainties in modelling of PHEVs and RESs using the well-known Monte Carlo simulation. In order to see the influence of different charging behaviours of PHEVs in the MG, three different charging patterns including uncontrolled, controlled and smart charging schemes are investigated. To study the optimal operation of the MG including the natural stochastic behaviour of the uncertain parameters, a new robust and powerful SOS (Symbiotic Organisms Search) algorithm is applied too. SOS simulates the interactions observed among natural organisms relying on other organisms to survive. In addition, a new modified version of the SOS algorithm is suggested to increase its total search ability in the local and global searches successfully. The performance of the proposed method is examined on two typical MG test systems with different scheduling time horizons. The results of applying the proposed method on the case studies are compared to other algorithms in different conditions with and without the PHEV charging effects.
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