Strategic scheduling of the electric vehicle-based microgrids under the enhanced particle swarm optimization algorithm - Saeed Abdollahi Khou; Javad olamaei; Mohammad Hassan Hosseini (Scientific Reports)
Abstract With increasing worldwide attention on environmental sustainability, microgrids that harness renewable sources have become more prominent. The changing characteristics of renewable energy sources and energy demand’s unpredictable patterns might cause disruptions in the sustainable working of microgrids. Moreover, EVs (electric vehicles), being dynamic loads, might significantly affect the security administration of the microgrid. However, the persistent problem of PSOAs (particle swarm optimization algorithms) being affected by local optima emphasizes the need for more improvements to these algorithms. In order to tackle these difficulties, a framework for dual-objective optimization was developed with the aim of improving both economic efficiency and environmental sustainability in microgrids that incorporate electric vehicles. This model employs a linear weighting strategy under a TPZSG (two-person zero-sum game) to maximize the utilization of renewables options and provide support for the load. The ultimate objective is to achieve a more efficient balance between these two goals. In addition, a more advanced approach called enhanced ASA-PSOA (adaptive simulated annealing-PSOA) is employed to find the best solutions in this context. The simulation outcomes indicate that the multi-function weighting strategy can reduce the impact of uncertainties, hence optimizing the use of renewable resources and load management. Furthermore, implementing systematic charging and discharging procedures for electric vehicles has the potential to decline both operational and environmental expenses in microgrids. The total expense of the system under the proposed algorithm (ASA-PSOA) can be reduced by 11.1%, 10.1%, 6.5%, and 4.5% compared to the PSOA, standard-PSOA, adaptive-PSOA, and simulated annealing-PSOA, respectively. Therefore, the improved optimization technique greatly enhances the economic and ecological efficiency of the microgrid.