Adaptive Automatic Generation Control in Multi-Area Power Systems: A Deep Reinforcement Learning Approach for Model-Based Controller Tuning

The increasing penetration of renewable energy sources (RES) reduces power system inertia, causing severe frequency excursions following disturbances. Conventional Automatic Generation Control (AGC) using fixed-gain controllers struggles to adapt to these rapidly changing dynamics. While deep reinforcement learning (DRL) shows promise for adaptive AGC, most studies lack validation on industrial-grade platforms. This paper addresses this gap by developing a comprehensive DRL framework for optimal AGC tuning via a novel PowerFactory-Python co-simulation interface. Four DRL algorithms such as TD3, SAC, PPO, and DDPG, are benchmarked for the real-time optimization of PI, PID, and Fractional-Order PID (FOPID) controllers. Extensive simulations on a modified IEEE 14-bus system with 46% renewable penetration demonstrate that TD3 provides the most stable frequency response, preventing dangerous oscillations during critical transitions like generator restoration. The results establish a clear performance hierarchy: TD3 outperforms SAC, followed by DDPG, with PPO being the worst performing. Among the controllers, FOPID achieves the best overall damping; quantitative evaluation shows that TD3-FOPID reduces the Integral of Absolute Error (IAE) by 55.9% compared to conventionally-tuned baselines. Finally, configuration training times fall within a 24-hour window, proving compatibility with day-ahead economic dispatch scheduling for overnight retraining. This high-fidelity co-simulation framework provides a practical pathway for the industrial adoption of intelligent AGC.