Search

The U.S. federal government regulates the reliability of bulk power systems, while the reliability of power distribution systems is regulated at a state level. In this article, we review the history of regulating electric service reliability and study the existing reliability metrics, indices, and standards for power transmission and distribution networks. We assess the foundations of the reliability standards and metrics, discuss how they are applied to outages caused by large exogenous disturbances such as natural disasters, and investigate whether the standards adequately internalize the impacts of these events. Our reflections shed light on how existing standards conceptualize reliability, question the basis for treating large-scale hazard-induced outages differently from normal daily outages, and discuss whether this conceptualization maps well onto customer expectations. We show that the risk indices for transmission systems used in regulating power system reliability do not adequately capture the risks that transmission systems are prone to, particularly when it comes to low-probability high-impact events. We also point out several shortcomings associated with the way in which regulators require utilities to calculate and report distribution system reliability indices. We offer several recommendations for improving the conceptualization of reliability metrics and standards. We conclude that while the approaches taken in reliability standards have made considerable advances in enhancing the reliability of power systems and may be logical from a utility perspective during normal operation, existing standards do not provide a sufficient incentive structure for the utilities to adequately ensure high levels of reliability for end-users, particularly during large-scale events. [ABSTRACT FROM AUTHOR]

Supporting strong, resilient, and integrated critical infrastructure is vital to upholding the U.S. economy and its national security. Arguably, no sector of the economy could exist without the reliable and predictable networks on which it depends. To date, the research on interdependent infrastructure has concentrated on describing the sources of the dependencies and developing models for predicting performance and cascading disruptions after a hazardous event. However, these models fail to capture the perspective of the operators of these networks and how competing, independent objectives lead to suboptimal investment decisions and hence suboptimal network performance. Rather than focus on how interdependent infrastructures operate and possibly fail, we take the perspective of their operators and ask why they make the decisions that they do. The goal of this paper is to demonstrate how strategic interdependencies may impact performance of coupled systems by shifting investments away from what is collectively best toward decisions that are more myopic and optimal from the perspective of a single infrastructure. Through our model, we make inferences on the level of investments networks make, relate this to performance, and provide policy recommendations on how to promote reliable infrastructure. [ABSTRACT FROM AUTHOR]

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near-zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large-scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on-going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long-term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this 'Perspective', we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on-going research on the risks from wind farms. [ABSTRACT FROM AUTHOR]

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over- and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole-based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region. [Copyright &y& Elsevier]

Abstract: This paper documents the application of the Conway–Maxwell–Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model. [Copyright &y& Elsevier]

This paper develops statistical models for estimating the impacts of tree trimming on electric power system outages under normal (nonstorm) operating conditions. The models are based on an extensive data set from Duke Power, a company in the southeastern U.S., and the models used are a negative binomial generalized linear model and a Poisson generalized linear mixed model (GLMM). The results show that: 1) increasing tree trimming frequency does lead to a decrease in the number of outages on the electric power distribution system; 2) the effects of tree trimming on different circuits can be differentiated and measured; and 3) the Poisson GLMM provides a good fit to the data in this situation. In particular, the results of the model show that for the Duke Power system, one would have expected, on average, 0.9 fewer outages per circuit over the 43-month data recording period if the time between tree trimming cycles was decreased by 1 yr across the whole system. These models could be applied to other power systems, and the results should be useful for power managers in setting tree trimming frequencies and in focusing on the most frequent trimming efforts on those circuits for which trimming will have the greatest benefit. [ABSTRACT FROM AUTHOR]

Racial discrimination by housing and lending institutions occurs worldwide. In the United States, it was legal and widely practiced well into the 1960s and has persisted in varying forms since. The Home Owner's Loan Corporation (HOLC) was one of many institutions engaging in racially discriminatory practices and created neighborhood appraisal maps in the 1930s based in part on race, ethnicity, and economic class of a neighborhood's residents. Using these HOLC neighborhood boundaries, we compared 12 present day population characteristics including health, employment, education, and income measures in each of the four HOLC neighborhood ratings across 14 U.S. cities. We used population-weighted logistic regression for paired data comparison and unsupervised learning techniques to uncover the consistency between HOLC evaluations and today's socio-economic and health outcomes. While existing literature focuses on case studies of particular cities and outcomes, we took a broad view and found consistent relationships over our study's cities and variables with few exceptions. Though there are many factors that contribute to continued inequality in a particular area, this study demonstrates that socio-economic and health inequality measured across numerous indicators continues to align with historic discriminatory housing boundaries. • Historic redlining boundaries have lasting significance on population outcomes. • Logistic regression and k-means clustering demonstrate neighborhood patterns. • Racially discriminatory housing policies influenced neighborhood differences. • Neighborhood inequality is demonstrated across a broad range of metrics. • Poverty, high school graduation, health are among the most divided. [ABSTRACT FROM AUTHOR]

Abstract: In coastal and estuarine environments, near-surface salinity varies significantly in space and time. As absolute salinity and salinity gradients are central to many physical and ecological processes in these environments, reliable and consistent salinity estimates are a priority for marine research and application communities. Satellite remote sensing has a great potential to meet this need, yet sensors and algorithms designed to monitor open ocean salinity are typically ill-suited for high resolution applications to coastlines and estuaries. Here we present results of multiple statistical models that predict daily, gridded surface salinity at 1km resolution across Chesapeake Bay as a function of level 2 surface reflectance estimates from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua platform. Eight statistical methods were tested and it was found that sea surface salinity can be accurately predicted via remote sensed products with an accuracy that is more than sufficient for many physical and ecological applications. For the best-performing statistical model, mean absolute error was 1.82 relative to mean Chesapeake Bay salinity of 16.5. [Copyright &y& Elsevier]

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near-zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large-scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on-going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long-term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this "Perspective", we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on-going research on the risks from wind farms., (© 2015 Society for Risk Analysis.)

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over- and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole-based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed., (© 2010 Society for Risk Analysis.)

Some program managers share a common belief that adding a redundant component to a system reduces the probability of failure by half. This is true only if the failures of the redundant components are independent events, which is rarely the case. For example, the redundant components may be subjected to the same external loads. There is, however, in general a decrease in the failure probability of the system. Nonetheless, the redundant element comes at a cost, even if it is less than that of developing the first one when both are based on the same design. Identical parts save the most in terms of design costs, but are subjected to common failure modes from possible design errors that limit the effectiveness of the redundancy. In the development of critical systems, managers thus need to decide if the costs of a parallel system are justified by the increase in the system's reliability. NASA, for example, has used redundant spacecraft to increase the chances of mission success, which worked well in the cases of the Viking and Voyager missions. These two successes, however, do not guarantee future ones. We present here a risk analysis framework accounting for dependencies to support the decision to launch at the same time a twin mission of identical spacecraft, given incremental costs and risk-reduction benefits of the second one. We illustrate this analytical approach with the case of the Mars Exploration Rovers launched by NASA in 2003, for which we had performed this assessment in 2001.