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