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In the given research, a new multicriterion (multiobjective) optimization algorithm has been considered to solve the problem of electric vehicles (EV) scheduling in a smart network in view of sustainable energy sources based on the cost and pollution minimization. By considering the environmental and economic problems, the application of EVs as a proper charging/discharging scheduling model and green energy sources plays an important role in the power system. This study focuses on multicriteria scheduling through uncertainty factors via inexhaustible assets and EVs, presenting a battery storing framework and reducing both the operation costs and the amount of the framework's pollution while improving the procedures. For this purpose, a new optimization algorithm has been considered to address the mentioned problem taking into account some clean energy sources and emission. The proposed model is examined in smart grid environment based on real-life model by Demand Response Program (DRP) evaluation and the uncertainties in sustainable energies. The proposed optimization algorithm shows more desirable results in comparison with other models, while the efficiency of the suggested approach is studied and evaluated in two power systems, i.e., a 33-bus standard power system and a 94-bus Portugal network. The obtained results validate the proposed method.

An Improved Multicriteria Optimization Method for Solving the Electric Vehicles Planning Issue in Smart Grids via Green Energy Sources