The paper's reliance on simulated data for training and validation raises concerns about its generalizability to real-world scenarios. The complexity and unpredictability of real-world power systems may not be fully captured by the simulated environment, leading to suboptimal or even unsafe control actions in practice. A more robust approach would involve testing and validation on real-world data, potentially in a controlled experimental setting.

While the paper demonstrates improved computational speed compared to traditional methods, it doesn't adequately address the scalability of the proposed approach to much larger power systems. The computational complexity of deep reinforcement learning can grow significantly with the size of the system, potentially negating the speed advantages observed in the 200-bus system. Further analysis and experimentation on larger systems are needed to establish the scalability of the approach.

The paper focuses primarily on optimizing economic dispatch while considering N-1 contingencies, but it doesn't fully address other important aspects of power system security, such as voltage stability and transient stability. A comprehensive approach to real-time control should consider all relevant security constraints to ensure reliable and stable system operation.