Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

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. •Multifaceted challenges for large-scale power system emergency control such as scalability, adaptiveness and security require convergence: merging ideas and approaches from a wide range of areas.•A convergence framework is proposed for integrating physics, machine learning, advanced computing, and control to realize intelligent grid emergency control.•Open-source high performance platform GridPACK-Gym for developing and testing DRL-based power system dynamic control algorithms.•The proposed solution has been tested on a large-scale Texas-size power system with more than 3000 buses and 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.