A Prescriptive Model to Assess the Socio-Demographics Impacts of Resilience Improvements on Power Networks

Mehmet Baran Ulak*, Anil Yazici, Eren Erman Ozguven

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
1 Downloads (Pure)


This paper provides a prescriptive resilience modeling framework for power grids that can account for the socio-demographic impacts of system improvements in the case of hurricanes. The power infrastructure failure rate and recovery duration models are developed based on Hurricane Hermine power outage data obtained from the City of Tallahassee, FL. For the component failures, physical factors such as component type and age, and building age in the surrounding area were used. For the component restoration, factors such as component age, critical facilities, and land use characteristics are considered. Monte Carlo simulation is utilized to estimate the potential impacts of two resilience policy/investment decisions: 1) investment to renew infrastructure components, and 2) reducing the component restoration time for faster recovery. For each scenario, the time evolution of affected populations (i.e., percentage of population with power at any time) is broken into socio-economic categories such as income, age, and ethnicity. Due to significant impact of infrastructure and neighborhood age, the scenario simulation results indicated that lower income populations were affected more (i.e., higher percentage of residents lost power) due to the Hurricane Hermine. Hence, for social equity considerations, it can be recommended that policy makers should prioritize infrastructure investments over improving recovery operations within the available budget constraints. The scenario analysis results also indicate that infrastructure investments which spatially target lower income areas can provide reasonable resilience improvements across the board while significantly closing the recovery gap between lower and higher income populations.
Original languageEnglish
Article number101777
Pages (from-to)1-16
Number of pages16
JournalInternational journal of disaster risk reduction
Early online date29 Jul 2020
Publication statusPublished - Dec 2020
Externally publishedYes


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