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.
@article{arxiv.2310.05021,
title = {Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control},
author = {Qiuhua Huang and Renke Huang and Tianzhixi Yin and Sohom Datta and Xueqing Sun and Jason Hou and Jie Tan and Wenhao Yu and Yuan Liu and Xinya Li and Bruce Palmer and Ang Li and Xinda Ke and Marianna Vaiman and Song Wang and Yousu Chen},
journal= {arXiv preprint arXiv:2310.05021},
year = {2023}
}