  This paper has delved into the pressing need for intelligent emergency
control in large-scale power systems, which are experiencing significant
transformations and are operating closer to their limits with more
uncertainties. Learning-based control methods are promising and have shown
effectiveness for intelligent power system control. However, when they are
applied to large-scale power systems, there are multifaceted challenges such as
scalability, adaptiveness, and security posed by the complex power system
landscape, which demand comprehensive solutions. The paper first proposes and
instantiates a convergence framework for integrating power systems physics,
machine learning, advanced computing, and grid control to realize intelligent
grid control at a large scale. Our developed methods and platform based on the
convergence framework have been applied to a large (more than 3000 buses) Texas
power system, and tested with 56000 scenarios. Our work achieved a 26%
reduction in load shedding on average and outperformed existing rule-based
control in 99.7% of the test scenarios. The results demonstrated the potential
of the proposed convergence framework and DRL-based intelligent control for the
future grid.
