### Literature Review
#### Limitations of ACE-Based MPC Schemes  
Despite advancements, ACE-based MPC strategies are not without their shortcomings. These approaches often converge to local optima, similar to traditional PI-based AGC, as their primary focus is on maintaining predefined tie-line flows and minimizing local control costs. This narrow focus can lead to biased control, particularly during significant disturbances, and fails to optimize the system-wide economic dispatch (ED), as noted in [19]. Additionally, ACE-based MPC schemes lose their capacity to smooth wind fluctuations, a crucial function in grids with high levels of renewable energy integration. To address these issues, a state-constrained DMPC (SCDMPC) approach was proposed in [20], which introduces constraints on tie-line flows while simultaneously regulating frequency states. This method aims to achieve a cost-effective AGC by integrating ED and AGC objectives, allowing tie-line flows to adapt during the AGC cycle. However, this approach is based on the assumption of instantaneous communication between distributed controllers and the wide area control (WAC) system, and minimal demand disturbances--conditions that are often impractical in real-world applications. In large-scale power systems, communication delays are inevitable, and significant demand disturbances can activate input constraints, potentially leading to the infeasibility of the SCDMPC optimizer. Nonlinear Dynamics and Advanced Control StrategiesThe complexity of AGC is further compounded by the nonlinear dynamics of synchronous generators, especially in systems powered by thermal plants. Research conducted in [21] focused on the speed regulation of turbo generator systems, emphasizing the critical role of the excitation system in optimizing performance and maintaining stability. The study highlights the challenges posed by managing the intricate dynamics of high-order models, such as the thirteenth-order steam turbine model discussed in [22; 23]. Traditional control approaches, like linear optimal control theory, often fall short in these scenarios due to their sensitivity to operating condition changes and the frequent need to recalibrate controller gains. To mitigate these challenges, various studies have explored intelligent control techniques, such as fuzzy logic, neural networks, and hybrid control systems [24; 25; 26]. These methods offer the flexibility and robustness needed to handle nonlinear dynamics and varying operational conditions. Hybrid controllers, which combine traditional PID controllers with intelligent methods like neuro-fuzzy systems, have shown promise in managing the complex dynamics of turbo generators and other critical power system components [27]. Integration of Renewable Energy SourcesThe growing integration of RESs into power grids further complicates the control landscape. Despite the rapid expansion of renewable energy infrastructure, a significant portion of electricity generation continues to rely on conventional thermal power plants, particularly combined-cycle plants utilizing both steam and gas turbines [28; 29]. Controlling these systems is especially challenging due to their nonlinear behavior and the difficulty of directly measuring critical parameters, such as magnetic flux in generators or internal pressure in turbines. Advanced control methodologies, including MPC, adaptive control, and fuzzy logic control, have been developed to optimize the performance of these complex systems. These strategies are designed to enhance efficiency, stability, and reliability by effectively managing load variations, disturbances, and changing operating conditions [30; 31; 32]. However, the challenge of nonlinear optimal control remains a focal point of research, especially in simulated environments where the aim is to improve system performance through advanced control algorithms [33; 34]. The body of research on MPC-based AGC strategies underscores the substantial progress made in creating robust and scalable control systems for contemporary power grids.