A Method to estimate the economy-wide consequences of widespread, long duration electric power interruptions

We partnered with a utility in the U.S. state of Illinois to develop and pilot an approach to estimate the economic impacts of widespread, long duration (WLD) power interruptions. We surveyed their customers about hypothetical blackouts, identifying and classifying mitigating/resilience behaviors and quantifying their costs and benefits. Survey results are scaled up to the broader regional economy, and used to drive a computational general equilibrium (CGE) simulation of the effects of power interruptions and attendant customer responses (e.g., relocation, backup generation). Impacts are severe: 1-, 3-, and 14-day interruptions reduce the utility service area's three-month GDP by $1.8 Bn (1.3%), $3.7 Bn (2.6%) and $15.2 Bn (10.4%), respectively, with losses driven overwhelmingly by disequilibrium responses to shortages as opposed to price signals (71%-88%). Doubling backup power penetration moderates GDP losses by 11%-14%, and is relatively least beneficial during the longest interruption duration. Results highlight previously unquantified economic losses that can potentially be avoided by investments in power system resilience.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-04-08
DOI
https://doi.org/10.1038/s41467-025-58537-4
Citations
1
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
article
Field-Weighted Citation Impact
0.95

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Method to estimate the economy-wide consequences of widespread, long duration electric power interruptions

Peter Larsen, Ridge Peterson, Sunhee Baik, Adam Z. Rose et al.
1 citations
Nature Communications
Infrastructure Resilience and Vulnerability Analysis
0.95
article

A Method to estimate the economy-wide consequences of widespread, long duration electric power interruptions

Peter Larsen, Ridge Peterson, Sunhee Baik, Adam Z. Rose, Ian Sue Wing, Dan Wei, Alan H. Sanstad, Juan Pablo Carvallo, Jeremy Smith, Christopher Ramee
article en
1 citations

Abstract

We partnered with a utility in the U.S. state of Illinois to develop and pilot an approach to estimate the economic impacts of widespread, long duration (WLD) power interruptions. We surveyed their customers about hypothetical blackouts, identifying and classifying mitigating/resilience behaviors and quantifying their costs and benefits. Survey results are scaled up to the broader regional economy, and used to drive a computational general equilibrium (CGE) simulation of the effects of power interruptions and attendant customer responses (e.g., relocation, backup generation). Impacts are severe: 1-, 3-, and 14-day interruptions reduce the utility service area's three-month GDP by $1.8 Bn (1.3%), $3.7 Bn (2.6%) and $15.2 Bn (10.4%), respectively, with losses driven overwhelmingly by disequilibrium responses to shortages as opposed to price signals (71%-88%). Doubling backup power penetration moderates GDP losses by 11%-14%, and is relatively least beneficial during the longest interruption duration. Results highlight previously unquantified economic losses that can potentially be avoided by investments in power system resilience.

Nature CommunicationsVol. 16(1)
University of Southern California (US), California Energy Commission (US), Lawrence Berkeley National Laboratory (US), Ambient Micro (United States) (US)
Lawrence Berkeley National Laboratory
Openalex Percentile: Top 27%
Infrastructure Resilience and Vulnerability Analysis
0.95
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.