Recovering Plausible Transmission Outage Scenarios
Abstract
Expansion planning for extreme weather needs outage scenarios that specify which transmission branches fail. However, public outage records often only report customers without power by county, making it unclear how to exploit this information to create outage scenarios. We propose a method to recover sets of outaged branches that most closely match regional load shed data. This gives us sets of plausible outages for a given hazard event. We formulate a bilevel problem with a load shedding DC optimal power flow in the lower level. We relax the binary branch states to differentiate the lower level problem, replace it with its sensitivity matrix, and use gradient descent to update the upper level. We treat the relaxed branch states as probabilities and perform randomized rounding to restore integrality. On three test systems with synthetic outages, the recovered sets reduce the load shed error to 0.02−4.5%. On the California Test System with storm data from December 2025, the error falls to 23%. These scenarios give expansion planning a reliable source of outage data tied to observed hazards.
Related
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BibTeX
@misc{ji2026recovering,
title = {{Recovering Plausible Transmission Outage Scenarios}},
author = {Karen Ji and Cameron Khanpour and Samuel Talkington and J. Kyle Skolfield and Daniel K. Molzahn},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.29872},
eprint = {2610.29872},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.29872},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Recovering Plausible Transmission Outage Scenarios
AU - Karen Ji
AU - Cameron Khanpour
AU - Samuel Talkington
AU - J. Kyle Skolfield
AU - Daniel K. Molzahn
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.29872
UR - https://arxiv.org/abs/2610.29872
AB - Expansion planning for extreme weather needs outage scenarios that specify which transmission branches fail. However, public outage records often only report customers without power by county, making it unclear how to exploit this information to create outage scenarios. We propose a method to recover sets of outaged branches that most closely match regional load shed data. This gives us sets of plausible outages for a given hazard event. We formulate a bilevel problem with a load shedding DC optimal power flow in the lower level. We relax the binary branch states to differentiate the lower level problem, replace it with its sensitivity matrix, and use gradient descent to update the upper level. We treat the relaxed branch states as probabilities and perform randomized rounding to restore integrality. On three test systems with synthetic outages, the recovered sets reduce the load shed error to 0.02−4.5%. On the California Test System with storm data from December 2025, the error falls to 23%. These scenarios give expansion planning a reliable source of outage data tied to observed hazards.
ER -