Security constrained optimal power shutoff for wildfire risk mitigation

Abstract Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de‐energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade‐offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post‐contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade‐offs between both wildfire risk and pre‐ and post‐contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39‐bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.

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Publication Details

Journal
IET Generation Transmission & Distribution
Published
2024-08-22
DOI
https://doi.org/10.1049/gtd2.13246
Citations
3
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
0.63

Funders

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article

Security constrained optimal power shutoff for wildfire risk mitigation

Line Roald, Carleton Coffrin, Noah Rhodes
3 citations
IET Generation Transmission & Distribution
Optimal Power Flow Distribution
0.63
article

Security constrained optimal power shutoff for wildfire risk mitigation

Line Roald, Carleton Coffrin, Noah Rhodes
article en
3 citations

Abstract

Abstract Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de‐energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade‐offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post‐contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade‐offs between both wildfire risk and pre‐ and post‐contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39‐bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.

IET Generation Transmission & DistributionVol. 18(18)
Los Alamos National Laboratory (US), University of Wisconsin–Madison (US)
National Science Foundation, U.S. Department of Energy, Office of Science, Division of Electrical, Communications and Cyber Systems, Advanced Scientific Computing Research
Openalex Percentile: Top 32%
Optimal Power Flow Distribution
0.63
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Security constrained optimal power shutoff for wildfire risk mitigation — Line Roald, Carleton Coffrin, et al. · IET Generation Transmission & Distribution (2024) | TGRS Research Map | TGRS