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Register a new userU.S. Power Resilience for 2002--2019
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Added by
Zhanlin Liu
Publication date
2021
fields
Mathematical Statistics
and research's language is
English
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Prolonged power outages debilitate the economy and threaten public health. Existing research is generally limited in its scope to a single event, an outage cause, or a region. Here, we provide one of the most comprehensive analyses of U.S. power outages for 2002--2019. We categorized all outage data collected under U.S. federal mandates into four outage causes and computed industry-standard reliability metrics. Our spatiotemporal analysis reveals six of the most resilient U.S. states since 2010, improvement of power resilience against natural hazards in the south and northeast regions, and a disproportionately large number of human attacks for its population in the Western Electricity Coordinating Council region. Our regression analysis identifies several statistically significant predictors and hypotheses for power resilience. Furthermore, we propose a novel framework for analyzing outage data using differential weighting and influential points to better understand power resilience. We share curated data and code as Supplementary Materials.
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Community risk perceptions can influence their abilities to cope with coastal hazards such as hurricanes and coastal flooding.Our study presents an initial effort to examine the relationship between community resilience and risk perception at the county level, through innovative construction of aggregate variables. Utilizing the 2012 Gulf Coast Climate Change Survey merged with historical hurricane data and community resilience indicators, we first apply a spatial statistical model to construct a county level risk perception indicator based on survey responses. Next, we employ regression to reveal the relationship between contextual hurricane risk factors and community resilience, on one hand, and county level perceptions of hurricane risks, on the other. Results of this study are directly applicable in the policy making domain as many hazard mitigation plans and adaptation policies are designed and implemented at the county level. Specifically, two major findings stand out. First, the contextual hurricane risks represented by peak height of storm surge associated with the last hurricane landfall and land area exposed to historical storm surge flooding positively affect county level risk perceptions. This indicates that hurricanes another threat wind risks need to be clearly communicated with the public and fully incorporated into hazard mitigation plans and adaptation policies. Second, two components of community resilience higher levels of economic resilience and community capital are found to lead to heightened perceptions of hurricane risks, which suggests that concerted efforts are needed to raise awareness of hurricane risks among counties with less economic and community capitals.
Recently, resilience is increasingly used as a concept for understanding natural disaster systems. Landslide is one of the most frequent geohazards in the Three Gorges Reservoir Area (TGRA).However, it is difficult to measure local disaster resilience, because of special geographical location in the TGRA and the special disaster landslide. Current approaches to disaster resilience evaluation are usually limited either by the qualitative method or properties of different disaster. Therefore, practical evaluating methods for the disaster resilience are needed. In this study, we developed an indicator system to evaluate landslides disaster resilience in the TGRE at the county level. It includes two properties of inherent geological stress and external social response, which are summarized into physical stress and social forces. The evaluated disaster resilience can be simulated for promoting strategies with fuzzy cognitive map (FCM).
Direct current (DC) network has been recognized as a promising technique, especially for shipboard power systems (SPSs). Fast resilience control is required for an SPS to survive after faults. Towards this end, this paper proposes the indices of survivability and functionality, based on which a two-phase resilience control method is derived. The on/off status of loads are determined in the first phase to maximize survivability, while the functionality of supplying loads are maximized in the second phase. Based on a comprehensive model of a DC shipboard power systems (DC-SPS), the two-phase method renders two mixed-integer non-convex problems. To make the problems tractable, we develop second-order-cone-based convex relaxations, thus converting the problems into mixed-integer convex problems. Though this approach does not necessarily guarantee feasible, hence global, solutions to the original non-convex formulations, we provide additional mild assumptions, which ensures that the convex relaxations are exact when line constraints are not binding. In the case of inexactness, we provide a simple heuristic approach to ensure feasible solutions. Numerical tests empirically confirm the efficacy of the proposed method.
The US Census Bureau plans to protect the privacy of 2020 Census respondents through its Disclosure Avoidance System (DAS), which attempts to achieve differential privacy guarantees by adding noise to the Census microdata. By applying redistricting simulation and analysis methods to DAS-protected 2010 Census data, we find that the protected data are not of sufficient quality for redistricting purposes. We demonstrate that the injected noise makes it impossible for states to accurately comply with the One Person, One Vote principle. Our analysis finds that the DAS-protected data are biased against certain areas, depending on voter turnout and partisan and racial composition, and that these biases lead to large and unpredictable errors in the analysis of partisan and racial gerrymanders. Finally, we show that the DAS algorithm does not universally protect respondent privacy. Based on the names and addresses of registered voters, we are able to predict their race as accurately using the DAS-protected data as when using the 2010 Census data. Despite this, the DAS-protected data can still inaccurately estimate the number of majority-minority districts. We conclude with recommendations for how the Census Bureau should proceed with privacy protection for the 2020 Census.
Hurricanes have caused power outages and blackouts, affecting millions of customers and inducing severe social and economic impacts. The impacts of hurricane-caused blackouts may worsen due to increased heat extremes and possibly increased hurricanes under climate change. We apply hurricane and heatwave projections with power outage and recovery process analysis to investigate how the emerging hurricane-blackout-heatwave compound hazard may vary in a changing climate, for Harris County in Texas (including major part of Houston City) as an example. We find that, under the high-emissions scenario RCP8.5, the expected percent of customers experiencing at least one longer-than-5-day hurricane-induced power outage in a 20-year period would increase significantly from 14% at the end of the 20th century to 44% at the end of the 21st century in Harris County. The expected percent of customers who may experience at least one longer-than-5-day heatwave without power (to provide air conditioning) would increase alarmingly, from 0.8% to 15.5%. These increases of risk may be largely avoided if the climate is well controlled under the stringent mitigation scenario RCP2.6. We also reveal that a moderate enhancement of critical sectors of the distribution network can significantly improve the resilience of the entire power grid and mitigate the risk of the future compound hazard. Together these findings suggest that, in addition to climate mitigation, climate adaptation actions are urgently needed to improve the resilience of coastal power systems.
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