Genetic Algorithm-Driven Optimization for Standalone PV/Wind Hybrid Energy Systems Design

Due to their abundance and cleanliness, renewable energy sources like solar and wind energy offer many advantages over conventional power sources. However, the primary drawback is that their outputs are weather-dependent. Rising energy costs and declining turbine and PV panel costs are driving uptake of Wind-Photovoltaic Hybrid Systems. However, figuring out the best combination of wind turbines and photovoltaic panels at the lowest possible cost is essential before building a renewable energy plant. This paper's goal is to identify the best hybrid wind-solar power system design for stand-alone use. The Genetic Algorithm (GA) optimization technique was employed in this work to meet the load requirements in a dependable manner while minimizing costs. This method provides an accurate way to determine the ideal number of wind turbines and photovoltaic modules. The cost of the hybrid systems is significantly lower, according to the results.