Sizing optimization of a standalone PV/wind hybrid energy system with battery storage using a genetic algorithm

Abstract

Renewable energy sources, such as wind and solar, are clean and widely available, they have significant advantages over conventional power. However, the climate has an inherent influence on their production. Due to growing energy costs and decreasing solar and wind turbine prices, the use of PV/wind hybrid energy systems has grown in popularity. Determining the ideal number of PV panels and wind turbines required is essential to minimize costs and ensure the continuous production of energy to fulfill the intended demand before building a renewable energy generating facility. The goal of this research is to identify the optimal design for a hybrid PV/wind system that includes battery storage for standalone uses. The suggested analysis uses the low power supply probability (LPSP) as a guiding metric and a genetic algorithm (GA) to optimize costs while reliably satisfying load requirements. With this technology, the ideal quantity of PV modules and wind turbines may be precisely determined at the lowest possible cost. The outcomes show that the hybrid systems have undergone effective optimization.