Physics-Informed, Safety and Stability Certified Neural Control for Uncertain Networked Microgrids

• Published in: IEEE transactions on smart grid Vol. 15; no. 1; p. 1
• Main Authors: Wang, Lizhi, Zhang, Songyuan, Zhou, Yifan, Fan, Chuchu, Zhang, Peng, Shamash, Yacov A.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: certified control; Distributed generation; Learning; learning-based control; microgrid stability; Microgrids; Networked microgrids; Power system stability; Robust control; Safety; Stability criteria; Training; Uncertainty
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2023.3309534

This letter devises a physics-informed neural hierarchical control for uncertain networked microgrids (NMs) to provide certificated safe and stable control of NMs undergoing disturbances and uncertain perturbations. The main contributions include 1) a learning-based hierarchical control framework for inverter-based resources (IBRs) in NMs under unprecedented uncertainties of renewable energies; 2) a robust control Lyapunov barrier function (rCLBF) to provide provable safety and stability guarantees under uncertain scenarios; 3) an rCLBF-based, physics-informed learning scheme to simultaneously discover the certificates and control policy with explicit safety, stability, and robustness guarantees, enabling certified generalization beyond nominal operating scenarios. The efficacy of the rCLBF-based neural hierarchical control is thoroughly validated in different NMs cases.