Optimization of grid-connected photovoltaic/ wind/battery/supercapacitor systems using a hybrid artificial gorilla troops optimizer with a quadratic interpolation algorithm

A global transition toward renewable energy is essential for mitigating the environmental and economic challenges associated with fossil fuels. However, optimizing hybrid renewable energy systems (HRES) presents significant challenges, particularly in achieving a balance between efficiency and cost-effectiveness. This study introduces a novel optimization approach called the Quadratic Interpolation-enhanced Artificial Gorilla Troops Optimizer (QIGTO), which is specifically designed to address these challenges. Unlike existing methods, QIGTO improves convergence speed and solution accuracy, which are crucial for optimizing grid-connected HRES. The QIGTO algorithm was applied to a real-world scenario involving a grid-connected system comprising photovoltaic panels, wind turbines, batteries, and supercapacitors. QIGTO provides significant improvements over existing methods by increasing the renewable energy fraction to 78.54%, reducing the annual cost to $572369.93, and lowering the cost of energy to $0.23996/kWh. The results indicate significant improvements in the system’s renewable energy fraction, cost savings, and overall performance. These findings establish QIGTO as an effective tool for advancing sustainable energy solutions and tackling the complexities associated with hybrid energy systems. The results of this study underscore the importance of advanced optimization techniques in renewable energy research and provide a robust foundation for future studies aimed at optimizing HRES across various contexts.