### Research Contribution  
The contributions of this study can be listed as follows:  
* The proposed hybrid NLMPC framework, integrated with data-driven methods, introduces a novel approach to AGC in multimachine power systems. This method offers significant advancements by effectively balancing technical and economic objectives, addressing real-world constraints, and providing robust, scalable, and adaptive control under dynamic conditions. The primary contribution of this paper lies in its comprehensive integration of technical and economic considerations into a unified control framework. Unlike prior studies that often prioritize either system stability or economic efficiency, the proposed approach incorporates a multi-objective cost function that simultaneously minimizes frequency deviations, voltage deviations, generation costs, and pollution penalties. This dual focus ensures that the system \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Previous Research** & **Proposed Distributed NL MPC Framework** \\ \hline \multirow{4}{*}{Optimization Focus} & Primarily local optimization & \multirow{4}{*}{Achieves system-wide optimization by integrating both control performance and economic objectives.} \\  & (ACE-based), leading to local optima & \\  & similar to traditional PI control & \\  & [10; 11; 12; 19]. & \\ \hline \multirow{4}{*}{Handling Nonlinear Dynamics} & Relies on linearized models, which & Utilizes a nonlinear MPC framework that is better suited for the inherent nonlinear behavior of modern power systems. \\  & complex nonlinear dynamics [10; 11; 12]. & \\ \hline \multirow{4}{*}{Scalability and Flexibility} & Centralized MPC faces scalability & \multirow{4}{*}{Offers a scalable and flexible control strategy that adapts to real-time operational conditions.} \\  & issues due to high & \\ \cline{1-1}  & computational/communication & \\ \cline{1-1}  & demands; DMPC lacks flexibility & \\ \cline{1-1}  & [24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. & \\ \hline \multirow{4}{*}{Integration of Economic and Technical Objectives} & Often separates economic and & \multirow{4}{*}{Integrates economic efficiency directly into the control framework, balancing} \\  & technical goals, leading to & \\ \cline{1-1}  & suboptimal performance [33; 34]. & \\ \hline \multirow{4}{*}{Response to Renewable Energy} & Struggles to manage the variability & \multirow{4}{*}{Designed to adapt to real-time challenges posed by RESs, enhancing grid stability and reliability.} \\ \cline{1-1}  & [10; 14; 15; 16; 18; 20]. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief comparison of previous research and proposed framework. achieves optimal performance not only in terms of operational stability but also with respect to economic and environmental sustainability. * A notable innovation of the present method is its advanced dynamic modeling, which integrates detailed thermo-mechanical dynamics of synchronous generators, including turbine and governor behaviors. In contrast to recent state-of-the-art methods that rely on simplified or linearized models, this approach combines physical modeling with data-driven techniques to improve the accuracy of system predictions and adapt to real-time operational changes. This integration enables the framework to address unmodeled dynamics and disturbances, such as cyber-physical attacks, load fluctuations, and renewable energy variability, which are significant limitations in existing methods. * Another critical contribution of the paper is its approach to managing computational complexity, a known challenge for NLMPC methods. By leveraging data-driven techniques to approximate unmodeled dynamics, the framework reduces the computational burden associated with solving nonlinear equations. Furthermore, the hierarchical control structure distributes computational effort across primary, secondary, and tertiary levels, ensuring scalability and real-time feasibility for large-scale systems.