Multi-Objective Optimization of Hybrid Energy Systems

Renewable energy generation can help countries achieve the Sustainable Development Goals by providing clean, safe, reliable and affordable energy. Conventional energy production is impractical due to shortages, high fuel prices and harmful emissions from fossil fuels. Convergence is the best way to address renewable energy issues, because it combines multiple renewable sources simultaneously. This research proposed a hybrid renewable energy system that combines energy from two or more locally accessible sources to meet demand in remote locations. A hybrid solar, wind and biogas grid system has been proposed, evaluated using the homer Pro software and an artificial intelligence technique called the genetic algorithm. The proposed strategy provided low cost, optimal volume, reduced emissions, high reliability. Comparing the results of GA-based optimization and Homer Pro, a clear trend emerged: GA-based optimization showed better performance stability and lower energy costs, which indicates a more economically efficient system. The GA-based optimization also proposed smaller sizes for Homer components, which reduced the net current cost and proved that a system with the same efficiency and reliability could effectively meet the energy requirements of the site.