The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is a robust and widely adopted metaheuristic technique for solving complex multi-objective optimization problems. In hybrid renewable energy based microgrids, it provides an effective framework for the optimal sizing of renewable generation units and energy storage systems (ESS) by simultaneously addressing two inherently conflicting objectives: minimizing the Levelized Cost of Energy (LCOE) and reducing the Loss of Power Supply Probability (LPSP). Typically, a decrease in LCOE is accompanied by an increase in LPSP, generating a Pareto front of feasible trade-off solutions. From this set, the most suitable configuration is generally selected as the one that achieves the lowest possible LCOE while satisfying predefined reliability constraints. Although oversizing renewable generators can mitigate unmet load and reduce energy deficits, such oversizing often leads to higher capital investment and increased system losses. To overcome these limitations, this study proposes a comprehensive and integrated optimization-control framework aimed at enhancing both economic and technical performance in a renewable energy based microgrid incorporating ESS. The approach combines NSGA-II for optimal sizing of system components with a fuzzy logic based energy management strategy designed to minimize operational losses, improve energy utilization, and ensure a secure and continuous supply to the permanent load. The proposed methodology is applied to a real case study in Tajoura, a district of Tripoli, Libya. Seven different system scenarios are evaluated using a full year of meteorological data and a ۶ kW load profile. Comparative simulation results confirm that the hybrid configuration integrating photovoltaic (PV), wind energy conversion systems (WECS), and a lithium based battery energy storage system (BESS) achieves superior performance in terms of reliability, supply security, and overall cost-effectiveness.
