Multi-Objective Optimization for Hybrid Microgrid Integration Using a Modified Firefly Algorithm

This paper presents a multi-objective optimization method utilizing a modified Firefly Algorithm (MFA) to optimize a hybrid gridconnected microgrid that integrates wind and diesel power sources, validated through a case study of such a microgrid. The MFA aims to enhance Net Present Cost (NPC), Levelized Energy Cost (LCOE), reliability, and greenhouse gas (GHG) emissions. The beta parameter in the MFA was dynamically adjusted based on the alpha, theta, and gamma parameters, influencing the attractiveness and movement of fireflies in the optimization process. The MATLAB simulation results demonstrate that the MFA significantly outperforms the original Firefly Algorithm (FA), with an overall improvement of 16%. These findings highlight the MFA's superior performance in enhancing cost-efficiency and reducing environmental impact compared to the original FA. The MFA results were categorized into economic metrics, including NPC, LCOE, and environmental greenhouse gas emissions (GHG) measured as CO2.