Robust parameter optimization of a hybrid wind-solar photovoltaic system with battery energy storage

The global energy landscape has shifted towards renewable sources in recent decades, driven by the need to mitigate the impacts of climate change and the finite nature of fossil fuels. Hybrid Renewable Energy Systems (HRES) have emerged as a promising solution, combining the strengths of different sources to enhance reliability and efficiency. This study aims to propose a multi-objective optimization (MOO) model for a hybrid wind-solar photovoltaic (PV) system with battery energy storage using Robust Parameter Design (RPD) and the Desirability Function to simultaneously minimize the Mean Squared Error (MSE) indicator of Net Present Value (NPV) and Levelized CO2 Emissions (LE). The methodology integrates control variables with a noise variable. It employs rigorous statistical analyses to validate the mean and variance model across various scenarios, ensuring economic viability and environmental sustainability. The study revealed that the NPV varied significantly with changes in the wind energy proportion and the demand level. The LE exhibited a negative relationship with the proportion of wind energy and a positive relationship with demand. In conclusion, the study demonstrated that optimizing the hybrid wind-solar PV system with battery energy storage is highly efficient and economically viable when considering the ideal proportion of wind energy and energy demand. Temperature, as an uncontrollable environmental variable, plays a role in the efficiency of these systems. Considering its impact during the planning and optimization stages is essential, particularly in a world increasingly affected by climate change.