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.
