As renewable energy sources continue to gain prominence, the need for reliable energy storage solutions becomes increasingly evident, posing a significant challenge to modern electrical grids. Energy storage systems emerge as indispensable components in facilitating the successful transition towards sustainable energy by virtue of their capability to capture, store, and distribute electricity efficiently. With renewable energy integration comes the pressing technical challenge of managing their inherent variability, especially concerning solar irradiation fluctuations and their implications for grid stability. Control algorithms govern the operation of energy storage systems, which are extensively deployed to counteract these fluctuations. Despite their widespread adoption, there exists a notable gap in comprehensive comparative evaluations of their performance across various sampling intervals, a critical factor influencing the longevity and effectiveness of battery systems. This study seeks to address this gap by undertaking a rigorous assessment of two widely used techniques, Exponential Moving Average (EMA) and Exponential Linear Estimation Smoothing (ELES), within a standardized irradiation profile framework. By analyzing their respective abilities to regulate ramp rates and maintain battery state of charge, this research aims to provide valuable insights into optimizing energy storage system deployment strategies, thus fostering greater grid stability and resilience in the face of renewable energy integration challenges.