Abstract

The uncertainties and intermittency associated with renewable generation sources, such as solar and wind, can pose significant overloading risks to power systems under N − k contingencies, potentially leading to cascading outages. Accurately quantifying these risks in independent system operator scale power systems, which may include tens of thousands of buses, remains a grand challenge. This paper proposes a computationally efficient, tail-distribution-aware approach for accurate overloading risk quantification in large-scale power systems. Specifically, a deep-kernel sparse vector-valued Gaussian process is developed and serves as a surrogate model. This model incorporates generation dispatch, predefined contingencies, and uncertain inputs, such as photovoltaic power and load demand, to predict their impacts on branch power flows, which are treated as the model outputs. To improve the fidelity of overloading risk assessment, we introduce an adaptive resampling mechanism based on power flow solver, which corrects biases in surrogate model predictions near the overloading threshold. Extensive results obtained on the realistic 21k+ bus New England power system demonstrate that the proposed method accelerates the risk assessment process by 22 times compared to the benchmark Monte Carlo sampling method, while maintaining high accuracy. Additionally, we validate the robustness of the approach across a wide range of distribution types and correlation scenarios between renewable generation and load demands.

Data availability

The figure/table data generated in this study is provided in the Source Data file. Source Data file has been deposited in Figshare under accession code https://doi.org/10.6084/m9.figshare.30656771.v137. The raw ISO New England power system network data are protected and are not available due to the commercial restrictions of ISO New England Inc. Source data are provided with this paper.

Code availability

All realizations were performed using Python. The code has been deposited in Figshare under accession code https://doi.org/10.6084/m9.figshare.3036611838.

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Acknowledgements

This material is partially supported by U.S. Department of Energy Office of Electricity under award number DOE OE00095 (B.T. and J.Z.). Part of this material is also based upon work supported by ISO New England (B.T., K.Y., J.Z., M.H., S.M., and X.L.). The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Office of Electricity.

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Authors and Affiliations

Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA

Bendong Tan, Ketian Ye & Junbo Zhao 1.

Thayer School of Engineering, Dartmouth College, Hanover, NH, USA

Junbo Zhao 1.

ISO New England, Holyoke, MA, USA

Mingguo Hong, Slava Maslennikov & Xiaochuan Luo

Authors

1. Bendong Tan
2. Ketian Ye
3. Junbo Zhao
4. Mingguo Hong
5. Slava Maslennikov
6. Xiaochuan Luo

Contributions

B.T. contributed to the conceptualization, methodology, and writing of the initial draft. K.Y. contributed to data generation and the writing of the initial draft. J.Z. contributed to the conceptualization, review, editing, supervision, and guidance. M.H., S.M., and X.L. contributed to data, review, and editing.

Corresponding author

Correspondence to Junbo Zhao.

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Nature Communications thanks Michael Chertkov, Meng Yue and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Tan, B., Ye, K., Zhao, J. et al. Computationally efficient tail distribution-aware large-scale power system overloading risk assessment. Nat Commun (2026). https://doi.org/10.1038/s41467-025-68241-y

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Received: 02 May 2025

Accepted: 22 December 2025

Published: 05 January 2026

DOI: https://doi.org/10.1038/s41467-025-68241-y