Abstract
The electric power infrastructure in Puerto Rico suffered substantial damage as Hurricane Maria crossed the island on September 20, 2017. Despite significant efforts made by authorities, it took almost a year to achieve near-complete power recovery. In this study, we used spaceborne daily nighttime lights (NTL) imagery as a surrogate measure of power loss and restoration. We quantified the spatial and temporal extent of loss of electric power and trends in gradual recovery at the 889 county subdivisions for over eight months and computed days without service at the above tabulation areas. We formulated a Quasi-Poisson regression model to identify the association of the features from physical and socioeconomic domains with the power recovery effort. According to the model, the recovery time extended for areas closer to the landfall location of the hurricane, with every 50-kilometer increase in distance from the landfall corresponding to 30% fewer days without power (95% CI = 26% - 33%). Road connectivity was a major issue for the restoration effort, areas having a direct connection with hi-speed roads recovered more quickly with 7% fewer outage days (95% CI = 1% - 13%). The areas which were affected by moderate landslide needed 5.5% (95% CI = 1% - 10%), and high landslide areas needed 11.4% (95% CI = 2% - 21%) more days to recover. Financially disadvantaged areas suffered more from the extended outage. For every 10% increase in population below the poverty line, there was a 2% increase in recovery time (95% CI = 0.3% - 2.8%).
Highlights
On September 20th, 2017, Hurricane Maria passed through the island of Puerto Rico with winds exceeding 155 miles/hour [1]
Puerto Rico Electric Power Authority (PREPA) has its largest fossil fuel-based power generating plants are on the southern part of the island, where the population concentration is highest in the north
As noted above, we used the Quasi-Poisson model to identify the influential factors in the power recovery in Puerto Rico after Hurricane Maria
Summary
On September 20th, 2017, Hurricane Maria passed through the island of Puerto Rico with winds exceeding 155 miles/hour [1]. Puerto Rico Electric Power Authority (PREPA) has its largest fossil fuel-based power generating plants are on the southern part of the island, where the population concentration is highest in the north. This makes the power infrastructure highly dependent on 30,000 miles of distribution and 2,400 miles of transmission lines [11], [12]. Liu et al [25] leveraged the negative binomial model, VOLUME 9, 2021 a generalized linear model (GLM), and incorporated transformers and wind speed data to predict the level of outage before the hurricane landfall. We formularized a Quasi-Poisson regression model to identify the socioeconomic, and physical cofactors that influenced the post-hurricane power restoration effort
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