Multi-Objective Optimization of an Islanded Green Energy System Utilizing Sophisticated Hybrid Metaheuristic Approach

Responding to the global call for sustainable renewable energy sources amidst growing energy demands, exhaustion of fossil fuels, and increasing greenhouse gas emissions, this study introduces a multi-objective optimization of an islanded green energy system. The focus is on the implementation of a sophisticated hybrid metaheuristic approach in a Hybrid Renewable Energy System (HRES) specifically designed for a university campus in Turkey. The developed HRES combines an array of technologies, including Photovoltaic (PV) panels, wind turbines, batteries, diesel generators, and inverters. One of the novel aspects of our work is the deployment of a rule-based Energy Management Scheme for effectively orchestrating the power flow between different system components. We employed various algorithms, namely Genetic Algorithm (GA), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and a novel hybrid of the Firefly and PSO algorithms (HFAPSO) to ensure optimal sizing of HRES. This proves critical for achieving a cost-effective system that can meet specific load demands and adhere to techno-economic indicators. Our study employed four distinct scenarios, with the optimal scenario being met through PV/Battery components. Our approach effectively addressed the high Total Gas Emissions (TGE) observed in scenarios 3 and 4, leading to uninterrupted annual load coverage with zero TGE and 100% renewable energy, akin to scenario 1. The simulation results demonstrate the supremacy of the HFAPSO algorithm in sizing HRES. This approach proved more effective than the HOMERPPro software tool, as well as the GA, FA, and PSO algorithms. In addition, a comparative analysis of the time performances of these algorithms highlighted the superior performance and convergence of HFAPSO. The application of the HFAPSO algorithm in the most efficient system configuration resulted in 2787.341 kW PV and 3153.940 kW Battery. This led to an annual system cost (ACS) of 479340.57, a net present cost (NPC) of 7777668.32, and an energy cost of 0.2201 per kWh. The system, entirely covered by solar panels, achieved a Renewable Energy Fraction (REF) of 100%.This study highlights the potential of efficient utilization and management of renewable energy sources through multi-objective optimization. Our method provides a valuable solution for reliably meeting energy demands and minimizing the annual cost of energy systems. The optimization was programmed using the MATLAB simulation package.