Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks

Samuel Talkington, Cameron Khanpour, Rahul K. Gupta, Sergio A. Dorado-Rojas, Daniel Turizo, Hyeongon Park, Dmitrii M. Ostrovskii, and Daniel K. Molzahn

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bibtex Entry

@inproceedings{talkington2026,
  title = {Admittance {{Matrix Concentration Inequalities}} for {{Understanding Uncertain Power Networks}}},
  author = {Talkington, Samuel and Khanpour, Cameron and Gupta, Rahul K. and {Dorado-Rojas}, Sergio A. and Turizo, Daniel and Park, Hyeongon and Ostrovskii, Dmitrii M. and Molzahn, Daniel K.},
  booktitle = {Proceedings of the 1st {{PowerUp Conference}}},
  year = 2026,
  address = {Boulder, CO},
}

Abstract

This paper presents conservative probabilistic bounds for the spectrum of the admittance matrix and classical linear power flow models under uncertain network parameters; for example, probabilistic line contingencies. Our proposed approach imports tools from probability theory, such as concentration inequalities for random matrices. This provides a theoretical framework for understanding error bounds of common approximations of the AC power flow equations under parameter uncertainty, including the DC and LinDistFlow approximations. Additionally, we show that the upper bounds scale as functions of nodal criticality. This network-theoretic quantity captures how uncertainty concentrates at critical nodes for use in contingency analysis. We validate these bounds on IEEE test networks, demonstrating that they correctly capture the scaling behavior of spectral perturbations up to conservative constants.

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