Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control

TL;DR

This paper proposes a convergence framework integrating physics, machine learning, computing, and control to enable intelligent emergency control in large-scale power systems, demonstrating significant improvements in load shedding reduction and outperforming existing methods.

Contribution

It introduces a novel convergence framework for large-scale power system control, combining multiple disciplines and validated on a real-world system with extensive scenario testing.

Findings

26% average reduction in load shedding

Outperformed rule-based control in 99.7% of scenarios

Validated on a large Texas power system with 3000+ buses

Abstract

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.