| Summary: | Renewable energy systems offer a sustainable alternative for addressing energy needs, especially in rural settings. This study investigates the optimization of a hybrid photovoltaic (PV) pumping system by minimizing the Cost of Energy (COE), maximizing the Renewable Fraction (RF), and reducing Carbon Dioxide Emissions (ECO₂). The optimization process was carried out using Python to implement the Queen Honeybee Migration (QHBM) algorithm, which evaluated three distinct scenarios to determine the most efficient system configuration. The optimal setup comprised 48 units of 600 Wp PV panels, 17 units of 250 Ah batteries, and 3 units of 8,000 W inverters. This configuration achieved a COE of $0.041 per kWh, an RF of 43.96 %, and an ECO₂ reduction of 118.49 kgCO₂e, with a cumulative objective value of -146.76. A comparative analysis with the Particle Swarm Optimization (PSO) algorithm indicated that QHBM excels in optimizing RF and ECO₂, whereas PSO demonstrates marginally superior performance in reducing COE. The economic evaluation reveals a Net Present Value (NPV) of $40,206.11 over a 35-year period, a Return on Investment (ROI) of 398 %, a Break-Even Point (BEP) of 6.45 years, and a Payback Period (PP) of 13.25 years. The system is projected to yield daily and annual cost savings of $6.33 and $2,310.90, respectively. These results underscore the efficacy of QHBM in achieving superior techno-economic and environmental performance in hybrid PV pumping systems. © 2024 The Author(s)
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