Technical and Economic Optimization of a 348 kWp PV/Wind/Battery in Cinkassé, Togo: Comparative Analysis of Genetic Algorithm and Homer Pro Configurations

Hybrid PV/Wind/Battery microgrid systems provide sustainable energy solutions for remote areas with limited grid access, yet their optimization requires advanced methodologies to ensure reliability and cost-effectiveness. This study investigates and compares the performance of genetic algorithm and Homer Pro for optimizing a 348 kWp hybrid microgrid system in Cinkassé, Togo. Using local meteorological and load demand data from NASA, both optimization techniques were evaluated based on key metrics, including loss of power supply probability, net present cost, levelized cost of energy, renewable penetration, and battery state of charge. The GA-optimized system achieved superior performance, with an LPSP of 0.0986%, an LCOE of $0.2233/kWh, and an NPC of $2,522,909.78, alongside higher renewable penetration and efficient battery utilization. In contrast, the Homer Pro-optimized system exhibited an LPSP of 1.44%, an LCOE of $0.3486/kWh, and an NPC of $7,599,467, reflecting higher costs and lower efficiency. The GA approach dynamically balanced renewable energy generation and storage, minimizing unmet load and enhancing system reliability, whereas Homer Pro struggled with resource variability and resulted in oversized components. The study demonstrates that advanced optimization techniques like GA are essential for maximizing renewable energy utilization, minimizing costs, and ensuring sustainable electrification in underserved areas.