Energy Management And Optimal Design Of Grid-Independent Hybrid Systems: An Economic And Environmental Assessment Using Intelligent Techniques

Abstract

The increasing demand for renewable energy calls for solutions that must address economic, reliable, and environmental aspects simultaneously. The concept of hybrid renewable microgrids provides a viable solution to provide clean and reliable power to meet the demands of remote and off-grid locations. In this paper, an effective economic, reliable, and environmental methodology is developed for optimizing and sizing hybrid microgrids of photovoltaic (PV) panels, wind turbines (WT), battery (B) storage, hydrogen (H₂)‑based storage, and diesel generators (DG) systems using the Walrus Optimization Algorithm (WOA) and the Gorilla Troops Optimizer (GTO) metaheuristic search techniques to reduce total cost and enhance reliability simultaneously. Seven different hybrid solutions are tested based on six distinct performance metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), Excess Energy Proportion (EXP), Renewable Fraction (RF), and Lifecycle Emissions (LCE). The simulation runs are repeated 20 times to explore their robustness and sensitivity to optimization iterations. It is observed that WOA provides better solutions compared to GTO, with reduced COE and NPC, improved reliability, and better system configurations. For both economic and environmental viability, The PV–WT–DG–Battery configuration achieves the lowest COE and NPC, with values of 0.0853 $/kWh and approximately 2.06 MUSD, respectively, while the PV–WT–Fuel Cell (FC)–DG configuration yields the lowest LCE, approximately 156,700 kg CO₂.