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REVIEW 3 major objections 5 minor 40 references

Evaluating Undergrounding Decisions for Wildfire Ignition Risk Mitigation across Multiple Hazards

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Transmission lines chosen for undergrounding to mitigate wildfire ignition risk were outaged in none of the hurricane or active wildfire scenarios and in only 4% of wind scenarios, so the investment produced meaningful load-shed…

desk verdict Useful cross-hazard case study with a robust geographic message, but the wind-overlap numbers rest on an uncalibrated scenario generator and need sensitivity analysis before they are taken quantitatively. read the letter →

arxiv 2506.06575 v1 pith:ZN4D3U5M submitted 2025-06-06 eess.SY cs.SY

Ryan Piansky , Daniel K. Molzahn , Nicole D. Jackson , J. Kyle Skolfield This is my paper
classification eess.SYcs.SY
keywords powersystemresiliencewildfireignitionrisklineundergroundingmulti-hazardoutagesloadsheddingDCoptimalflowhurricanewindevent
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether investments chosen to protect a power grid from one natural hazard also improve resilience to other natural hazards. Using a large-scale synthetic grid representing Texas and real 2021 outage and weather data, it tests a $1 billion line-undergrounding plan designed to reduce load shed from wildfire-related shutoffs against hurricane, active wildfire, and wind outage scenarios. The paper finds that the plan yields essentially no benefit for hurricanes or active wildfires—none of the undergrounded lines appear in those outage sets—and only small, occasional benefits for wind events, with meaningful load-shed reduction on just three days. The authors conclude that single-hazard optimized resilience investments do not necessarily transfer to broader resilience and that planning should co-optimize across hazards.

What carries the argument

The argument runs through a scenario-generation rule, an event-overlap count, and an optimal power flow comparison. For each day and each hazard, 100 outage scenarios are built by drawing a $\beta$-prime random value per line (mean 0.01) and declaring the line outaged when that draw falls below the county's customer-outage fraction; the pre-resilience case removes the resulting line set, and the post-resilience case re-energizes the subset that was undergrounded. Load shedding is then minimized with a linearized DC optimal power flow ($B_\theta$ DC-OPF) across the full day. The event-overlap count—how often an undergrounded line falls in the outaged set—is the object that carries the conclusion.

What would settle it

A direct check would compare actual 2021 transmission line outage locations during Hurricane Nicholas and Texas wind events against the undergrounding plan; any observed outage on a planned undergrounded line would overturn the zero-overlap claim for that hazard. Alternatively, re-running the scenario procedure with a different beta-prime mean (e.g., 0.1 instead of 0.01) and observing whether the wind-overlap fraction changes materially would test the stability of the 4% figure.

Watch

Extended reading notes

Core claim

The central discovery is an evaluation result for the 2021 Texas case: the set of transmission lines chosen for undergrounding to mitigate wildfire ignition risk has zero overlap with the lines outaged in hurricane and active wildfire scenarios, and only 4% of wind-related outage scenarios (252 of 6,239) involve an undergrounded line. Consequently, the investment reduces wind-event load shed on only three days (January 30, June 25, and June 26), with average per-scenario reductions of 25 MWh, 1,250 MWh, and 135 MWh. The paper therefore establishes that a plan that reduces shutoff-related load shed by over 70% in its design context can have negligible cross-hazard resilience value, supporting the case for co-optimizing investments across multiple hazards.

Load-bearing premise

The results depend on an uncalibrated rule that converts county-level customer outage counts into transmission line outages: each line is declared outaged when a random draw with mean 0.01 falls below the county outage fraction, and the paper does not validate or vary this mapping.

Editorial extensions

If this is right

  • A resilience investment optimized for one hazard should be assumed to have little or no benefit for other hazards until demonstrated otherwise.
  • Sequential single-hazard planning is a risky use of limited resilience budgets; co-optimizing across hazard types would spend the same dollar where it reduces expected load shed over the whole event set.
  • Hazard geography drives the result: wildfire ignition risk concentrates in one part of the state while hurricane damage concentrates in another, so plans tuned to one geography will miss the other.
  • Wind events are the only hazard class examined that intersect the undergrounding plan, yet the benefit is concentrated on three days; planners should weigh such partial overlaps against the full distribution of events.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The scenario-generation rule's 0.01 mean is a free parameter; varying it would likely change the overlap statistics, so the paper's quantitative claims are not yet checked for sensitivity to that parameter.
  • County-level customer outage data conflate distribution and transmission outages, so the synthetic line-outage sets may misrepresent which specific transmission lines failed; line-level outage records would be needed to confirm the zero-overlap findings.
  • A natural next step, not taken in the paper, is to solve a co-optimization problem that allocates undergrounding across wildfire, hurricane, and wind risks and compare its load-shed performance with the single-hazard plan.
  • Because the study uses only 2021 events, the conclusion reflects that year's event geography; multi-year or climate-projected event sets could produce overlaps where 2021 did not.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper evaluates whether a transmission-line undergrounding plan designed in prior work [18] to mitigate wildfire ignition risk during Public Safety Power Shutoffs (PSPS) also reduces outage-driven load shedding during other natural hazards. Using the Texas7k synthetic grid, 2021 county-level customer outage data from EAGLE-I, and NOAA storm event records, the authors generate 100 daily outage scenarios for hurricane, wind, and active-wildfire events. They map county outage fractions to line outages via a beta-prime random threshold, run a B-theta DC-OPF to compute load shed with and without the undergrounded lines, and report overlap statistics. The main empirical findings are: no undergrounded line is outaged in any hurricane or active-wildfire scenario; only 252 of 6,239 wind scenarios (4%) involve undergrounded lines; and load-shed reductions occur on only three days (January 30, June 25, June 26). The paper concludes that single-hazard resilience investments do not necessarily improve outcomes for other hazards, motivating co-optimization.

Significance. If the quantitative results are reliable, the paper makes a useful contribution to the resilience-planning literature: it is, to my knowledge, the first large-scale synthetic-grid evaluation of how a specific line-hardening plan designed for one hazard (wildfire ignition risk) performs under other hazard types. The geographic explanation for the hurricane and wildfire non-overlap is plausible and robust, since the undergrounded lines lie in northwest Texas while the hurricane damage is in southeast Texas and the active-wildfire outages are in central-north Texas. The wind-overlap analysis is the only part that could support a quantitative multi-hazard transferability claim, but it depends on an uncalibrated scenario-generation rule. The manuscript is well-structured and the power-flow formulation is standard.

major comments (3)
  1. The line-outage scenario generation is the load-bearing step for the wind-overlap statistic, yet the beta-prime threshold is both underspecified and unvalidated. Step 2 states only that o_l,d,k is drawn from a beta-prime distribution with mean 0.01; the shape parameters are not given, so the distribution is not fully defined. More importantly, the rule that line l is outaged when o_l,d,k < rho_d,c has no empirical calibration against actual transmission line outage records, and no sensitivity analysis is presented. The scale of o directly controls the number and location of outaged lines: with rho values on the order of 0.01 common on wind days, the 252-of-6,239 overlap count and the three-day load-shed reductions in Sections IV and V-B would change substantially if the threshold mean were, say, 0.001 or 0.1. The authors should either validate the mapping against observed line-outage data co-occurring with wind events or systematically vary the beta-prime parameters and show that the qualitative conclusions are unchanged.
  2. The reported load-shed reductions (25 MWh, 1250 MWh, and 135 MWh on January 30, June 25, and June 26) are scalar averages over 100 stochastic scenarios per day, but no measures of uncertainty (e.g., confidence intervals, quantiles, or the full distribution of reductions) are given. Because the scenario generation is stochastic and uncalibrated, the claim that only these three days see 'meaningful' reductions is not supported without distributional evidence; the violin plots in Figure 7 show the spread of absolute load shed but not the spread of the difference. Please provide uncertainty quantification for the reductions, or at least report the range and interquartile intervals.
  3. The conclusion in Section VI states that the undergrounding plan 'yield[s] very small improvements' for wind events, but this inference is based on a single realization of the threshold parameter and on a small number of days with large reductions. If the threshold were recalibrated, the set of days with meaningful reductions could change, and the load-shed improvements per scenario could be larger or smaller. The central claim of the abstract—that 'investment decisions made to address one type of natural disaster do not necessarily improve broader resilience outcomes'—is phrased as a general statement, but the evidence for it is conditional on the unvalidated scenario generator. A sensitivity analysis or validation against independent outage data is necessary to make the quantitative claim robust.
minor comments (5)
  1. The notation rho_d,c = sum_{t in T} rho_{d,c,t} is ambiguous because the subscript t is omitted on the left-hand side while the right-hand side defines a sum over hourly fractions. Please use a distinct symbol (e.g., R_{d,c}) or explicitly state that rho_{d,c} is the daily aggregate.
  2. Reference [37] is titled 'Utilizing beta distribution...' and appears to be about the beta distribution, not the beta-prime distribution; the citation in Section III-A step 2 may be mismatched.
  3. The caption mentions 'pre-contingency' and 'post-contingency' load shed, while the text uses 'pre-resilience' and 'post-resilience.' Please use consistent terminology throughout.
  4. The undergrounding plan is described as coming from [18], but the paper does not summarize the optimization objective, constraints, or the exact set of lines beyond Figure 1; a brief description of the selection criteria would help readers assess transferability.
  5. The claim 'to the best of our knowledge, this is the first power systems paper...' is difficult to verify; consider softening to 'to our knowledge' and citing any related multi-hazard resilience evaluation work that may have appeared since the authors' search.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the undergrounding plan is an external input and the multi-hazard evaluation is an independent test.

full rationale

The derivation chain is not circular. The wildfire-undergrounding plan is taken as an input from the authors' prior optimization study [18]; this paper does not re-derive the plan from the hazard-outage data it evaluates. The evaluation uses independent external sources (EAGLE-I outage data, NOAA NCEI storm events, and the Texas7k synthetic grid) and an independently specified Bθ DC-OPF model. The central quantitative claim—that the plan rarely intersects wind outages and never intersects hurricane or active-wildfire outages—is a measurable outcome, not an identity: the authors do not fit any parameter to reproduce it. The beta-prime draw with mean 0.01 in Section III-A is an uncalibrated modeling assumption whose sensitivity is not analyzed, but that is a correctness and validation concern, not circularity: no fitted value is renamed as a prediction, and no equation reduces to its own input. The only self-citation is [18], which supplies the plan itself and is a legitimate external input; that cited result is not the conclusion of this paper, and the outcome could in principle have shown broad benefits. Under the review rules, a self-citation is not load-bearing here because the evaluation is externally falsifiable and parameter-free with respect to the tested plan's derivation.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the mapping from real outage data to synthetic grid outages, the validity of the Btheta DC-OPF approximation, and the representativeness of the prior undergrounding plan. The most fragile item is the beta-prime threshold, which is a hand-chosen free parameter.

free parameters (1)
  • Beta-prime distribution mean for line outage sampling = 0.01
    Hand-selected mean in Section III-A (step 2). No calibration or sensitivity analysis is given; this value controls how many lines are outaged in each scenario.
assumptions (5)
  • domain assumption County-level customer outage fractions can be mapped to line outage probabilities via the described beta-prime threshold rule.
    Section III-A defines rho_d,c as the outage probability proxy and compares a beta-prime draw to it. This mapping is not validated against observed line outages.
  • domain assumption EAGLE-I and NOAA NCEI data correctly attribute outages to the three hazard types.
    Section II-A pairs outage events with storm event records, assuming the binary hazard indicator correctly identifies causality.
  • domain assumption Btheta DC-OPF (Model 1) gives an adequate estimate of load shed for the synthetic Texas7k network.
    Section III-B uses a linearized power flow; the approximation ignores voltage, reactive power, and cascading effects.
  • domain assumption The undergrounding plan from [18] is representative of wildfire ignition risk mitigation investments.
    Section II-B adopts the plan from a prior paper; the plan was optimized for PSPS load shed reduction, not for the other hazards.
  • domain assumption Texas7k synthetic network is a valid proxy for the actual ERCOT grid.
    Section II-C describes the synthetic network; its outage response may differ from the real system.

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Pith. "Pith review of Evaluating Undergrounding Decisions for Wildfire Ignition Risk Mitigation across Multiple Hazards." pith.science (2026). https://pith.science/paper/ZN4D3U5M

@misc{pith2026250606575,
  author       = {Pith},
  title        = {Pith review of: Evaluating Undergrounding Decisions for Wildfire Ignition Risk Mitigation across Multiple Hazards},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZN4D3U5M}},
  note         = {Machine review of arXiv:2506.06575}
}
read the original abstract

With electric power infrastructure increasingly susceptible to impacts from climate-driven natural disasters, there is an increasing need for optimization algorithms that determine where to harden the power grid. Prior work has primarily developed optimal hardening approaches for specific acute disaster scenarios. Given the extensive costs of hardening the grid, it is important to understand how a particular set of resilience investments will perform under multiple types of natural hazards. Using a large-scale test case representing the Texas power system, this paper aims to understand how line undergrounding investment decisions made for wildfire ignition risk mitigation perform during a range of wildfire, hurricane, and wind events. Given the varying geographical spread and damage profile of these events, we show that investment decisions made to address one type of natural disaster do not necessarily improve broader resilience outcomes, supporting the need for co-optimization across a range of hazards.

Figures

Figures reproduced from arXiv: 2506.06575 by the authors.

Figure 1
Figure 1. A map of the line undergrounding plan considered in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A map showing line outages which co-occurred with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. A map showing line outages which co-occurred with [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: A map showing line outages which co-occurred with [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Violin plots show the daily load shed across scenarios [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Violin plots show the daily load shed across scenarios [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

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Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages

  1. [18]

    Equitably allocating wildfire resilience investments for power grids – The curse of aggregation and vulnerability indices,

    M. Pollack, R. Piansky, S. Gupta, and D. K. Molzahn, “Equitably allocating wildfire resilience investments for power grids – The curse of aggregation and vulnerability indices,”Applied Energy, vol. 388, no. 125511, June 2025

    work page 2025

  2. [1]

    Blackout: Extreme weather, climate change and power outages,

    A. Kenward, U. Rajaet al., “Blackout: Extreme weather, climate change and power outages,”Climate Central, vol. 10, 2014

    work page 2014

  3. [2]

    U.S. Billion-Dollar Weather and Climate Disasters,

    NOAA National Centers for Environmental Information (NCEI), “U.S. Billion-Dollar Weather and Climate Disasters,” 2024. [Online]. Available: https://www.ncei.noaa.gov/access/billions/

    work page 2024

  4. [3]

    Weather-related Power Outages Rising,

    Climate Central, “Weather-related Power Outages Rising,”

    work page

  5. [4]

    Improving the estimated cost of sustained power interruptions to electricity cus- tomers,

    K. H. LaCommare, J. H. Eto, L. N. Dunn, and M. D. Sohn, “Improving the estimated cost of sustained power interruptions to electricity cus- tomers,”Energy, vol. 153, pp. 1038–1047, 2018

    work page 2018

  6. [5]

    Biden-Harris Administration announces additional $2 billion to protect the grid against growing threats of extreme weather, expand transmission,

    US Department of Energy, “Biden-Harris Administration announces additional $2 billion to protect the grid against growing threats of extreme weather, expand transmission,” 2024

    work page 2024

  7. [6]

    The White House, “Fact sheet: Biden-Harris Administration transforms nation’s infrastructure, celebrates historic progress in rebuilding America for the three-year anniversary of the Bipartisan Infrastructure Law,” 2024

    work page 2024

  8. [7]

    Berg,Tropical cyclone report: Hurricane Ike (al092008), 1-14 September 2008

    R. Berg,Tropical cyclone report: Hurricane Ike (al092008), 1-14 September 2008. National Hurricane Center, 2009

    work page 2008

Show all 40 references
  1. [8]

    How do power lines cause wildfires?

    Texas A&M, “How do power lines cause wildfires?”Texas Wildfire Mitigation Project, July 2014, https://wildfiremitigation.tees.tamus.edu/ faqs/how-power-lines-cause-wildfires

    2014

  2. [9]

    Extreme weather and electricity markets: Key lessons from the February 2021 Texas crisis,

    T. Levin, A. Botterud, W. N. Mann, J. Kwon, and Z. Zhou, “Extreme weather and electricity markets: Key lessons from the February 2021 Texas crisis,”Joule, vol. 6, no. 1, 2022

    2021

  3. [10]

    Some Houston-area power outages could last weeks after deadly storms cause widespread damage,

    D. Phillip, L. Baumann, and C. Weber, “Some Houston-area power outages could last weeks after deadly storms cause widespread damage,” The Texas Tribune, 2024. [Online]. Available: https://www.texastribune. org/2024/05/16/texas-storm-deaths-houston-power-outage-damage/

    2024

  4. [11]

    Severe weather, utility spending, and the long-term reliability of the US power system,

    P. H. Larsen, M. Lawson, K. H. LaCommare, and J. H. Eto, “Severe weather, utility spending, and the long-term reliability of the US power system,”Energy, vol. 198, 2020

    2020

  5. [12]

    The climate-energy nexus: A crit- ical review of power grid components, extreme weather, and adaptation measures,

    J. Garland, K. Baker, and B. Livneh, “The climate-energy nexus: A crit- ical review of power grid components, extreme weather, and adaptation measures,”Environmental Research: Infrastructure and Sustainability, vol. 4, no. 3, September 2024

    2024

  6. [13]

    Power system resilience: Current practices, challenges, and future directions,

    N. Bhusal, M. Abdelmalak, M. Kamruzzaman, and M. Benidris, “Power system resilience: Current practices, challenges, and future directions,” IEEE Access, vol. 8, pp. 18 064–18 086, 2020

    2020

  7. [14]

    Proactive operations and investment planning via stochastic optimization to enhance power systems’ extreme weather resilience,

    M. Bynum, A. Staid, B. Arguello, A. Castillo, B. Knueven, C. D. Laird, and J.-P. Watson, “Proactive operations and investment planning via stochastic optimization to enhance power systems’ extreme weather resilience,”Journal of Infrastructure Systems, vol. 27, no. 2, 2021

    2021

  8. [15]

    Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines,

    J. St ¨urmer, A. Plietzsch, T. V ogt, F. Hellmann, J. Kurths, C. Otto, K. Frieler, and M. Anvari, “Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines,”Nature Energy, vol. 9, no. 5, pp. 526–535, May 2024

    2024

  9. [16]

    Enhancing power grid resilience to winter storms via generator winterization with equity considerations,

    B. Bilir, E. Kutanoglu, J. J. Hasenbein, B. Austgen, M. Garcia, and J. K. Skolfield, “Enhancing power grid resilience to winter storms via generator winterization with equity considerations,”Sustainable Cities and Society, vol. 114, 2024

    2024

  10. [17]

    Three-stage optimization model to inform risk-averse investment in power system resilience to winter storms,

    B. G. Austgen, M. Garcia, J. J. Yip, B. Arguello, B. J. Pierre, E. Ku- tanoglu, J. J. Hasenbein, and S. Santoso, “Three-stage optimization model to inform risk-averse investment in power system resilience to winter storms,”IEEE Access, vol. 12, pp. 135 117–135 134, 2024

    2024

  11. [19]

    Long duration battery sizing, siting, and operation under wildfire risk using progressive hedging,

    R. Piansky, G. Stinchfield, A. Kody, D. K. Molzahn, and J. P. Watson, “Long duration battery sizing, siting, and operation under wildfire risk using progressive hedging,”Electric Power Systems Research, vol. 235, no. 110785, October 2024, presented at the23rd Power Systems Com...

    2024

  12. [20]

    Transmission and capacity expansion planning against rising temperatures: A case study in Arizona,

    J. K. Skolfield, J. Ramirez-Vergara, and A. R. Escobedo, “Transmission and capacity expansion planning against rising temperatures: A case study in Arizona,”arXiv:2106.12687, 2021

    2021 arXiv

  13. [21]

    Multi-stage and resilience-based distribution network expansion planning against hur- ricanes based on vulnerability and resiliency metrics,

    A. Nasri, A. Abdollahi, and M. Rashidinejad, “Multi-stage and resilience-based distribution network expansion planning against hur- ricanes based on vulnerability and resiliency metrics,”International Journal of Electrical Power & Energy Systems, vol. 136, p. 107640, 2022

    2022

  14. [22]

    Co-optimized expansion planning to enhance electrical system resilience in Puerto Rico,

    C. J. Newlun, A. L. Figueroa-Acevedo, J. D. McCalley, A. Kimber, and E. O’Neill-Carrillo, “Co-optimized expansion planning to enhance electrical system resilience in Puerto Rico,” in51st North American Power Symposium (NAPS), 2019

    2019

  15. [23]

    Climate-aware generation and transmission expansion planning: A three-stage robust optimization approach,

    A. Moreira, D. Pozo, A. Street, E. Sauma, and G. Strbac, “Climate-aware generation and transmission expansion planning: A three-stage robust optimization approach,”European Journal of Operational Research, vol. 295, no. 3, pp. 1099–1118, 2021

    2021

  16. [24]

    Fire ignition data,

    California Public Utilities Commission, “Fire ignition data,” 2023. [Online]. Available: www.cpuc.ca.gov/industries-and-topics/wildfires

    2023

  17. [25]

    California power shutoffs: Deficiencies in data and reporting,

    M. Sotolongo, C. Bolon, and S. H. Baker, “California power shutoffs: Deficiencies in data and reporting,”Initiative for Energy Justice, October 2020, https://iejusa.org/wp-content/uploads/2020/10/V3.3-Policy-Brief- CA-Shutoffs-Data-Brief.pdf

    2020

  18. [26]

    Resilient expansion planning of electricity grid under prolonged wildfire risk,

    R. Bayani and S. D. Manshadi, “Resilient expansion planning of electricity grid under prolonged wildfire risk,”IEEE Transactions on Smart Grid, vol. 14, no. 5, 2023

    2023

  19. [27]

    Managing wildfire risk and promoting equity through optimal configuration of networked microgrids,

    S. Taylor, G. Setyawan, B. Cui, A. Zamzam, and L. A. Roald, “Managing wildfire risk and promoting equity through optimal configuration of networked microgrids,” inACM e-Energy, June 2023

    2023

  20. [28]

    Optimizing transmission infrastructure investments to support line de-energization for mitigating wildfire ignition risk,

    A. Kody, R. Piansky, Molzahn, and D. K, “Optimizing transmission infrastructure investments to support line de-energization for mitigating wildfire ignition risk,”11th IREP Symposium on Bulk Power System Dynamics and Control, 2022

    2022

  21. [29]

    A robust optimiza- tion approach to determine power line undergrounding under wildfire risk,

    S. Taylor, A. Musselman, L. Roald, and J. Watson, “A robust optimiza- tion approach to determine power line undergrounding under wildfire risk,” Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), Tech. Rep., 2023

    2023

  22. [30]

    A framework for risk assessment and optimal line upgrade selection to mitigate wildfire risk,

    S. Taylor and L. A. Roald, “A framework for risk assessment and optimal line upgrade selection to mitigate wildfire risk,”Electric Power Systems Research, vol. 213, 2022, presented at the22nd Power Systems Computation Conference (PSCC)

    2022

  23. [31]

    Creation of synthetic electric grid models for transient stability studies,

    T. Xu, A. B. Birchfield, K. S. Shetye, and T. J. Overbye, “Creation of synthetic electric grid models for transient stability studies,” in10th Bulk Power Systems Dynamics and Control Symposium (IREP), 2017

    2017

  24. [32]

    A dataset of recorded electricity outages by United States county 2014–2022,

    C. Brelsford, S. Tennille, A. Myers, S. Chinthavali, V . Tansakul, M. Den- man, M. Coletti, J. Grant, S. Lee, K. Allenet al., “A dataset of recorded electricity outages by United States county 2014–2022,”Scientific Data, vol. 11, no. 1, p. 271, 2024

    2014

  25. [33]

    Storm events database, version 3.1,

    National Climatic Data Center, NESDIS, NOAA, U.S. Department of Commerce, “Storm events database, version 3.1,” 2024. [Online]. Available: https://www.ncdc.noaa.gov/stormevents/

    2024

  26. [34]

    A review of public safety power shutoffs (PSPS) for wildfire mitigation: Policies, practices, models and data sources,

    C. Huang, Q. Hu, L. Sang, D. D. Lucas, R. Wong, B. Wang, W. Hong, M. Yao, and V . Donde, “A review of public safety power shutoffs (PSPS) for wildfire mitigation: Policies, practices, models and data sources,” IEEE Transactions on Energy Markets, Policy and Regulation, vol. 1,...

    2023

  27. [35]

    Modeling, tuning, and val- idating system dynamics in synthetic electric grids,

    T. Xu, A. B. Birchfield, and T. J. Overbye, “Modeling, tuning, and val- idating system dynamics in synthetic electric grids,”IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 6501–6509, 2018

    2018

  28. [36]

    Analysis and applications of the beta prime distribution in statistical modeling,

    R. S. Mahmood, R. J. Mizban, M. A. Sarhan, A. Rashid, M. Rasheed, and T. Saidani, “Analysis and applications of the beta prime distribution in statistical modeling,”Journal of Positive Sciences, vol. 3, no. 6, pp. 34–41

  29. [37]

    Utilizing beta distribution for probabilistic modeling: Five numer- ical examples,

    ——, “Utilizing beta distribution for probabilistic modeling: Five numer- ical examples,”Journal of Positive Sciences, vol. 3, no. 5, pp. 40–48

  30. [38]

    The IEEE Reliability Test System-1996. A Report Prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee,

    C. Grigg, P. Wonget al., “The IEEE Reliability Test System-1996. A Report Prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee,”IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1010–1020, 1999

    1996

  31. [39]

    Wildland Fire Potential Index,

    U.S. Geological Survey, “Wildland Fire Potential Index,” 2024

    2024

  32. [2024]

    Available: https://www.climatecentral.org/climate- matters/weather-related-power-outages-rising

    [Online]. Available: https://www.climatecentral.org/climate- matters/weather-related-power-outages-rising

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