Enhancing Microgrid Energy Management Incorporating Renewable Energy and Energy Storage Systems via Geometric Mean Optimizer

Integrating renewable energy sources (RES) and energy storage technologies into grid-connected microgrids is a significant solution for energy management, improving power system sustainability, dependability, and economic efficiency. This paper presents an approach to microgrid energy management optimization using the Geometric Mean Optimizer (GMO) algorithm. The study evaluates the performance of GMO against the Genetic Algorithm (GA) under various scenarios, including two different load profiles, with and without RES integration. MATLAB M-files were utilized for all simulations and analysis, guaranteeing reproducibility and promoting additional research in this domain. Based on the simulation results, GMO is more efficient and cost-effective than GA in every situation. Without RES, GMO significantly reduced daily operating costs for both loads, exceeding GA. The integration of RES further amplified GMO's advantages, leading to even lower operational costs in both load scenarios. Finally, GMO's improved performance significantly reduced microgrid expenditures and significantly shortened payback periods compared to GA.