A Framework for Wildfire Risk Assessment to Electric Grid

Wildfires can cause catastrophic damage to the surrounding environment and pose substantial challenges to the reliability and resiliency of the electric grid. Recently, wildfire incidents have increased, and electric utilities are facing numerous challenges in maintaining seamless operation of the power system. In this paper, a data-driven framework is proposed to assess the spatio-temporal aspects of wildfires on the power system. To illustrate this approach, we utilize data from two existing power networks in the southwestern-US, including corresponding fire risk values for these geographical locations. This methodology aims to establish a correlation between the wildfire risks to the power grid models, leveraging the geographical attributes of transmission lines. Subsequently, power flow is solved using dynamic power grid models in Positive Sequence Load Flow (PSLF) to demonstrate the damages caused by wildfires using probabilities representing wildfire risks and fire arrival times based on single fire growth, respectively. Additionally, grid hardening techniques and component-induced ignition probabilities are incorporated in the analysis to provide a comparative evaluation of the effects on power grids with grid hardening included. The findings demonstrate that both N-k and N-1-1 contingencies evaluations are required, and the reliability of a system in adverse fire-related conditions can be exacerbated without grid-hardening measures, and the cascading failure probabilities can be significantly increased while component failures are incorporated.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3583964
Citations
1
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
1.11

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article

A Framework for Wildfire Risk Assessment to Electric Grid

Holly Eagleston, Brian Pierre, Jubair Yusuf, Michelle Bester
1 citations
IEEE Access
Fire effects on ecosystems
1.11
article

A Framework for Wildfire Risk Assessment to Electric Grid

Holly Eagleston, Brian Pierre, Jubair Yusuf, Michelle Bester
article en
1 citations

Abstract

Wildfires can cause catastrophic damage to the surrounding environment and pose substantial challenges to the reliability and resiliency of the electric grid. Recently, wildfire incidents have increased, and electric utilities are facing numerous challenges in maintaining seamless operation of the power system. In this paper, a data-driven framework is proposed to assess the spatio-temporal aspects of wildfires on the power system. To illustrate this approach, we utilize data from two existing power networks in the southwestern-US, including corresponding fire risk values for these geographical locations. This methodology aims to establish a correlation between the wildfire risks to the power grid models, leveraging the geographical attributes of transmission lines. Subsequently, power flow is solved using dynamic power grid models in Positive Sequence Load Flow (PSLF) to demonstrate the damages caused by wildfires using probabilities representing wildfire risks and fire arrival times based on single fire growth, respectively. Additionally, grid hardening techniques and component-induced ignition probabilities are incorporated in the analysis to provide a comparative evaluation of the effects on power grids with grid hardening included. The findings demonstrate that both N-k and N-1-1 contingencies evaluations are required, and the reliability of a system in adverse fire-related conditions can be exacerbated without grid-hardening measures, and the cascading failure probabilities can be significantly increased while component failures are incorporated.

IEEE AccessVol. 13
Sandia National Laboratories California (US), Sandia National Laboratories (US)
U.S. Department of Energy
Climate action
Openalex Percentile: Top 23%
Fire effects on ecosystems
1.11
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A Framework for Wildfire Risk Assessment to Electric Grid — Holly Eagleston, Brian Pierre, et al. · IEEE Access (2025) | TGRS Research Map | TGRS