Model order reduction is a crucial engineering technique used to simplify complex systems while preserving their core behavior. Describing the behavior of practical process control systems accurately is a challenging task due to the uncertainties and variations in system coefficients. To address this, order reduction techniques are necessary for simplifying the modeling process. One such technique is the gray wolf optimization (GWO) algorithm. It optimizes solutions by imitating the hunting behavior of gray wolves. In this study, we apply the GWO algorithm to the load frequency control (LFC) problem in power systems, aiming to regulate power output and keep harmony among generation and load demand. By changing the parameters of a proportional-integral-derivative (PID) controller utilizing the GWO algorithm, we enhance the performance and stability of the LFC system. We also discuss various methods for tuning PID parameters in LFC, highlighting the advantages and limitations of each approach. The results demonstrate the effectiveness of the GWO algorithm in optimizing PID parameters and improving control performance, while also emphasizing the importance of order reduction techniques, such as the balanced reduction algorithm, for managing uncertainties in practical process control systems. Overall, this research paper provides insights into utilizing the GWO algorithm and order reduction techniques to optimize control systems for the LFC.