Sizing and optimization of a hybrid photovoltaic/wind energy system with a storage system using genetic algorithms

Abstract

The increasing demand for clean and sustainable energy sources, coupled with the need to mitigate climate change, has driven the widespread adoption of renewable energy systems. Among these, hybrid photovoltaic (PV)/wind/batteries systems have emerged as a promising solution to address the intermittent nature of solar and wind energy sources. This dissertation focuses on the optimization and performance evaluation of hybrid PV/wind/batteries systems using genetic algorithms (GA). The objective is to minimize system costs while ensuring reliable power supply by integrating PV panels, wind turbines, and energy storage batteries. The study considers multiple sites and factors such as solar irradiance, wind speed, load demand, and storage capacity to determine the optimal system configurations. Through extensive simulations and analysis, the research provides insights into the cost-effectiveness, reliability, and performance of hybrid energy systems.