The integration of renewable power into electrical grids brings about uncertainty in grid operations due to its inherent variability. This requires the use of strong economic dispatch models that consider the expenses linked to underestimating or overestimating renewable generation. This paper suggests a simplified approach for modeling renewable controllable generation by using uncertainty cost functions instead of the complex probability distributions commonly used in traditional methods, through Montecarlo simulations. This simplification reduces the computational complexity of optimization problems associated with solar irradiance and wind speed modeling. The benefits of this approach include decreased computational burden, enhanced precision in approximating the real solar irradiance and wind speed distribution, and adaptability to various situations. The simulations confirm that the proposed approach is effective in accurately estimating the costs associated with uncertainty while also being computationally efficient. This makes it a promising tool for enhancing the economic dispatch and facilitating better decision-making and grid operation.