## 1 Introduction  
To reach the goals of carbon peaking and carbon neutrality, an ever-growing penetration level of renewable energy, including wind and solar, is being integrated with modern power systems. Due to the uncertainty and stochastic nature of renewable energy, power electronic devices, natural disasters, and malfunctions in control devices, significant challenges are being observed in operating the power grid to meet various security constraints at all times [1]. In case of severe disturbances, rapidly restoring the fluctuating voltage and frequency profiles and the overloaded line flows back to normal is of great importance to ensure the secure and economical operation of power systems. Traditionally, voltage control is performed locally to maintain bus voltage profiles within secure ranges via adjusting generator terminal voltage settings and switching shunt VAr devices. Line flow control strategies are designed similarly to mitigate overloading issues before and after system disturbances. Their settings and coordination strategies are typically determined via large-scale offline simulation studies to prepare for the projectedworst scenarios before being used in a real-time environment. Full and accurate system models need to be built and large-scale sensitivity analyses conducted to screen for effective controllers in dealing with voltage violation and line flow violations. Then, optimization approaches such as the inner point algorithm are adopted to adjust the selected controllers to provide voltage and line flow controls. Such methods work well for traditional power systems. However, given the trend of the grid's ever-increasing complexity and stochastic characteristics, those offline-determined operational rules and study assumptions can be violated, thus limiting the effectiveness of such offline-determined control decisions. Optimizing control strategies in real time remains a difficult task that requires sufficiently accurate full-system models and fast simulation techniques to quickly adapt to changing operating conditions, causing the majority of today's controller settings to be adjusted manually. Therefore, deriving prompt and effective coordinated control strategies of active and reactive power for real-time applications becomes critical to optimize control performance [2]. Hierarchical automatic control systems, including automatic voltage control (AVC) and automatic generation control (AGC) with multiple-level coordination, have been developed and are widely deployed in today's power systems. An AVC system typically consists of three levels of control (primary, secondary, and tertiary), e.g., in France [3], Italy [4; 5], and China [6; 7; 8; 9; 10]. At the primary level, automatic voltage regulators maintain local voltage levels using excitation systems with a response time of several seconds. At the secondary level, the bus voltages of selected pilot buses in different control zones are regulated using available reactive power resources, with a response time of several minutes. At the tertiary level, the setpoints of zonal pilot buses are optimized to minimize inter-zonal transmission losses while respecting security constraints, with a response time of 15 min to several hours. An AGC system is designed to provide line flow control and secondary frequency regulation by adjusting the active power outputs of AGC generators. Due to the abovementioned issues, the control performance of today's AVC and AGC systems can be affected for the following reasons: (1) They require accurate real-time full-system models to optimize and achieve the desired control performance, which depend upon high-quality EMS snapshots running every few minutes. (2) Sensitivity-based methods for selecting the most effective control devices can be affected significantly by different operating conditions, significant topology changes, and severe contingencies. (3) Real-time coordination and optimization of all controllers in a high-dimensional space for a large-scale power network remains a challenging task.