Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control (2310.05021v1)
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.
- Z. Yan and Y. Xu, “Data-driven load frequency control for stochastic power systems: A deep reinforcement learning method with continuous action search,” IEEE Transactions on Power Systems, vol. 34, no. 2, pp. 1653–1656, 2019.
- Q. Huang, R. Huang, W. Hao, J. Tan, R. Fan, and Z. Huang, “Adaptive power system emergency control using deep reinforcement learning,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1171–1182, 2020.
- C. Chen, M. Cui, F. F. Li, S. Yin, and X. Wang, “Model-free emergency frequency control based on reinforcement learning,” IEEE Transactions on Industrial Informatics, 2020.
- M. Glavic, “(deep) reinforcement learning for electric power system control and related problems: A short review and perspectives,” Annual Reviews in Control, 2019.
- X. Chen, G. Qu, Y. Tang, S. Low, and N. Li, “Reinforcement learning for selective key applications in power systems: Recent advances and future challenges,” IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2935–2958, 2022.
- Y. Li, C. Yu, M. Shahidehpour, T. Yang, Z. Zeng, and T. Chai, “Deep reinforcement learning for smart grid operations: Algorithms, applications, and prospects,” Proceedings of the IEEE, vol. 111, no. 9, pp. 1055–1096, 2023.
- R. Huang, Y. Chen, T. Yin, X. Li, A. Li, J. Tan, W. Yu, Y. Liu, and Q. Huang, “Accelerated derivative-free deep reinforcement learning for large-scale grid emergency voltage control,” IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 14–25, 2022.
- J. Weng, M. Lin, S. Huang, B. Liu, D. Makoviichuk, V. Makoviychuk, Z. Liu, Y. Song, T. Luo, Y. Jiang, Z. Xu, and S. Yan, “Envpool: A highly parallel reinforcement learning environment execution engine,” 2022.
- Anyscale, “Ray: An open-source unified framework for scaling ai and python applications,” https://www.ray.io/, accessed: 2023-9-27.
- B. Palmer, W. Perkins, Y. Chen, S. Jin, D. Callahan, K. Glass, R. Diao, M. Rice, S. Elbert, M. Vallem, and Z. Huang, “Gridpack: A framework for developing power grid simulations on high performance computing platforms,” in 2014 Fourth International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing, 2014, pp. 68–77.
- PNNL, “GridPACK.” [Online]. Available: https://github.com/GridOPTICS/GridPACK
- R. Huang, Y. Chen, T. Yin, Q. Huang, J. Tan, W. Yu, X. Li, A. Li, and Y. Du, “Learning and fast adaptation for grid emergency control via deep meta reinforcement learning,” IEEE Transactions on Power Systems, vol. 37, no. 6, pp. 4168–4178, 2022.
- Huang, Renke and Huang, Qiuhua and Yin, Tianzhixi, and Palmer, Bruce, and Li, Ang, “GridPACK.” [Online]. Available: https://github.com/pnnl/hadrec
- WECC, “Tpl-001-wecc-crt-4—transmission system planning performance,” https://www.wecc.org/Reliability/TPL-001-WECC-CRT-4.pdf, accessed: 2023-9-28.
- Y. Du, Q. Huang, R. Huang, T. Yin, J. Tan, W. Yu, and X. Li, “Physics-informed evolutionary strategy based control for mitigating delayed voltage recovery,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3516–3527, 2022.
- T. Xu, A. B. Birchfield, K. S. Shetye, and T. J. Overbye, “Creation of synthetic electric grid models for transient stability studies,” in The 10th Bulk Power Systems Dynamics and Control Symposium (IREP 2017), 2017, pp. 1–6.
- X. Wang, Y. Chen, and W. Zhu, “A survey on curriculum learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4555–4576, 2021.
- X. Sun, X. Li, S. Datta, X. Ke, Q. Huang, R. Huang, and Z. J. Hou, “Smart sampling for reduced and representative power system scenario selection,” IEEE Open Access Journal of Power and Energy, vol. 8, pp. 293–302, 2021.