An optimal energy mix of various renewable energy sources and storage devices is critical for beetroot birkenstock a profitable and reliable hybrid microgrid system.This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development.This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO).The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability.
A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method.The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean read more (for minimization objective), and a higher standard deviation.The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.
12% can be achieved.The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.