###### Abstract  
Real-world power systems face challenges from demand fluctuations, system constraints, communication delays, and unmeasurable disturbances. This paper presents a real-time hybrid approach integrating Nonlinear Model Predictive Control (NLMPC) and data-driven methods for automatic generation control (AGC) of synchronous generators, particularly under cyber-physical attacks. Unlike previous studies, this work considers both technical and economic aspects of power system management. A key innovation is the incorporation of a detailed thermo-mechanical model of turbine and governor dynamics, enabling optimized control and effective management of power oscillations. The proposed NLMPC-based AGC strategy addresses governor saturation and generation rate constraints, ensuring stability. Extensive simulations in MATLAB/Simulink, including IEEE 11-bus and 9-bus test systems, validate the controller's effectiveness in enhancing power system performance under various challenging conditions.  
automatic generation control; nonlinear model predictive control; data-driven model; synchronous generators +
Footnote †: journal: Applied Sciences  
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Footnote 1: email: {[[https://doi.org/10.3390/app15041956](https://doi.org/10.3390/app15041956)]([https://doi.org/10.3390/app15041956](https://doi.org/10.3390/app15041956))}  
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: {[[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))  
(MPC) has emerged as a promising approach for optimal generation control, offering the flexibility to incorporate a wide range of constraints and enabling real-time adjustments [7].  
However, existing MPC-based AGC strategies have primarily focused on optimizing control performance without fully addressing the simultaneous achievement of technical and economic objectives at the system level. This gap underscores the need for a novel approach that integrates both technical and economic considerations into a cohesive control strategy [8]. The research presented in this paper addresses this critical need by proposing a distributed nonlinear model predictive control (NLMPC) framework with state constraints [9]. This advanced approach not only optimizes control performance but also ensures economic efficiency at the system level, representing a significant advancement in the field. As the global integration of RESs into power grids accelerates, the challenges posed by these variable energy sources demand innovative control strategies that can adapt to real-time operational conditions and the dynamic nature of power systems. The proposed methodology offers a comprehensive solution that enhances the stability, reliability, and economic efficiency of power grids, positioning AGC systems to remain resilient and cost-effective even as dependence on renewable energy sources grows. Ultimately, this research contributes to the broader objective of transitioning to a more sustainable and resilient energy infrastructure, addressing the complex interplay between technical and economic challenges in the integration of renewable energy into the power grid. In the following section, a comprehensive literature review is conducted to assess the recent state-of-the-art research in this area. This review aims to provide clarity on the current advancements and identify gaps that the proposed methodology seeks to address. By examining the latest studies and developments, it will be better understood how recent approaches have tackled the challenges associated with the integration of renewable energy sources into power grids and the optimization of control strategies under stochastic conditions.