Abstract
This paper uses a modern stochastic framework for studying the storage devices, PHEVs (plug-in hybrid electric vehicles), RESs (renewable energy sources) and such MGs (micro grids) for optimal management of energy while applying the popular Monte Carlo simulation technique for modelling the uncertainties of RESs and PHEVs. Prominent charging patterns namely uncontrolled, smart and controlled charging schemes are implemented for analysis of response in MGs for varied PHEV charging behaviours. Simultaneously, we also apply a powerful and robust Symbiotic Organisms Search (SOS) algorithm for analysing the uncertain parameter behaviours in natural stochastic form and the optimal MG operation. The interactions observed in natural organisms that depend on other organisms for survival are simulated by SOS. The total search ability in global and local searches is improved effectively using the modified SOS algorithm. Multiple MG test systems with varied scheduling time limits are used for examination of the proposed technique and its performance. In the presence as well as absence of PHEV charging effects, comparison of proposed technique and other algorithms are conducted with case studies under diverse conditions.
Published Version
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