| 總結: | With the expanding demand for electrical energy across the world and the growing concern about environmental matters resulting from the widespread use of fossil fuels in the power system, it is necessary to find suitable alternatives to resolve the problem. In this regard, Renewable Energy Sources (RES) that produce almost no pollution have become the preferred means of supplying the global energy demand. In this paper, a novel approach is proposed for formulating the problem and reducing reliability costs to achieve the minimum total cost of the grid. Concurrently, the transportation system has been replacing conventional vehicles with Electric Vehicles (EVs), where Plug-in Electric Vehicles (PEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) have got the public's attention. Thanks to the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies, it is technically possible and economically justified to plug in such vehicles to receive/inject power from/to the grid. Alternatively, an emerging concept in electric power systems, i.e. Microgrid (MG), was developed and employed to level up the RESs’ penetration rate and maximize the capabilities of EVs using the smart infrastructure. Indeed, the V2G option is utilized for mitigating operating costs in order to host PEVs in the network. Therefore, optimally scheduling the MG would be of utmost importance. Thus, an efficient day-ahead stochastic operation framework has been developed in this research work for an MG equipped with renewable energy-powered Distributed Generation (DG) units and EVs. The mentioned stochastic programming framework is based on the Unscented Transform (UT). It is worth mentioning that the problem has been formulated as a stochastic programming problem with the objective of optimizing the total operating cost. The studied problem is then tackled using a biomimicry well-known approach called the “Converged Barnacles Mating Optimizer (CBMO) algorithm” and the results derived from simulating the problem would be compared to the ones achieved by some prominent algorithms. © 2022 Elsevier Ltd
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