Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

Abstract

This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, considering these uncertainties to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimizing the cost of microgrids by 4.31%, 3.82%, and 1.87% to the genetic algorithm (GA), Particle swarm optimization (PSO) algorithm, and Teaching learning-based optimization (TLBO) algorithm, respectively.