Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications

An optimal energy mix of various renewable sources and storage devices is critical for a profitable reliable hybrid microgrid system. This work proposes optimization method to assess the wind, photovoltaic, battery system development. study considers hybridization Non-dominant Sorting Genetic Algorithm II (NSGA II) Grey Wolf Optimizer (GWO). The objective function was formulated simultaneously minimize total cost loss power supply probability. A comparative among proposed method, II, multi-objective Particle Swarm Optimization (PSO) performed examine efficiency method. analysis shows that applied performs better than other algorithms alone in terms convergence speed, reaching global minima, lower mean (for minimization objective), higher standard deviation. also reveals by relaxing probability from 0% 4.7%, an additional reduction approximately 12.12% can be achieved. provide improved flexibility stakeholders select optimum combination generation offered solutions.