Optimal Planning of the Microgrid Considering Optimal Sizing of the Energy Resources

This study addresses the necessity of energy storage systems in microgrids due to the uncertainties in power generation from photovoltaic (PV) systems and wind turbines (WTs). The research focuses on designing and sizing hybrid energy resources, including PV, WT, hydrogen storage, and battery systems. The main objectives of the study involve minimizing installation costs, maximizing the penetration of PV and WT systems in supply–demand, and reducing load shedding. To achieve these goals, the study utilizes combined algorithms such as particle swarm optimization (PSO) and non-dominated sorting genetic algorithm II (NSGA II) to optimize multi-objective functions. The effectiveness of the proposed method is validated through comparative experiments, demonstrating its ability to optimize the number of resources efficiently. The results obtained from the combined algorithms indicate significant improvements in installation costs, PV and WT systems penetration, and load shedding compared to the NSGA II algorithm, with savings of $325,765.3, an increase of 29.6% in PV and WT systems penetration, and a decrease of 4.3% in load shedding.