Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems

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.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network

This article compares two nonparametric tree‐based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high‐resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2‐km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree‐leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.     

Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards

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.     

MANAGING ENGINEERING CHANGE: MARKET OPPORTUNITIES AND MANUFACTURING COSTS

Engineering changes in the design of a product, while attractive from a marketing viewpoint (in terms of increased sales opportunities, matching competitors innovations, etc.) cause disruptions in the manufacturing function of a firm. These disruptions include delays or backorders in the delivery of both committed-orders and forecast-demands of existing products, increased capacity requirements that could result in greater use of subcontracts, higher component inventories, and obsolescence of certain components. In this paper, we establish how the marketing opportunities and manufacturing costs associated with engineering changes can be managed so as to enhance the firm's profits over a planning horizon. Using an optimization model, we show that an enhanced product with increased marketing opportunities may not immediately replace the existing product; it may be phased in over a period of time. We further illustrate how the firm's overall profit, and the mechanics of phasing out the old product and phasing in the new product, are affected by factors such as the manufacturing lead time of the new product, its market attractiveness as compared to the old product, capacity availability, subcontracting premiums, and backorder costs. We develop several insights that allow managers to quickly establish whether an engineering change would be desirable and discuss a multitude of options that may be used to further enhance their desirability. Finally, we show that if the phase out period of the old product is set arbitrarily, rather than optimally, the result may be a substantial reduction in overall profits.     

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.     

Testing Branching Process Estimators of Cascading Failure with Data from a Simulation of Transmission Line Outages

We suggest a statistical estimator to quantify the propagation of cascading transmission line failures in large blackouts of electric power systems. We use a Galton‐Watson branching process model of cascading failure and the standard Harris estimator of the mean propagation modified to work when the process saturates at a maximum number of components. If the mean number of initial failures and the mean propagation are estimated, then the branching process model predicts the distribution of the total number of failures. We initially test this prediction on failure data generated by a simulation of cascading transmission line outages on two standard test systems. We discuss the effectiveness of the estimator in terms of how many cascades need to be simulated to predict the distribution of the total number of line outages accurately.     

MULTIPLE REGRESSION ANALYSIS APPLIED TO RESIDENTIAL PROPERTIES

An interesting and promising innovation in the assessment of property for tax purposes is the application of multiple regression analysis. Sales prices are regressed on various housing characteristics, and models developed from sold properties are used to generate value estimates for unsold properties. Two important issues relating to the development of such models are (1) the extent to which real estate markets are stable in terms of structural relationships over time, and (2) the extent to which sales prices reflect actual market values. The first issue is important because it affects how current sales must be before they can be used in model development, as well as the amount of information the assessor must collect and maintain on properties. The second issue is important because it concerns the amount of measurement error in the models and the extent to which the assessor/analyst can unambiguously interpret residual variance. These issues are investigated by comparing regression models developed from a sample of properties in Eugene, Oregon, which sold in each of two well separated time periods.     

Analysis of the Effects of Communication and Surveillance Facility Service Outages on Traffic Separations

This study examines air traffic separations in the service volumes of communication and surveillance facilities that experienced service outages. The data sample consists of 338 unscheduled service outages that happened in 2010 and 2011 at facilities located in the vicinity of 15 major traffic hubs. For each outage, radar track data were collected and used to calculate traffic separations during the period of 30 minutes before to 30 minutes after an outage. Then, the separation index, which indicates the percentage of horizontal separation retained between two aircraft at the same altitude, was estimated. The separation index and loss of separation events were analyzed using lognormal and negative binomial regression models. The results suggest that the count of separation events peaks during the 15 minutes after an outage. In addition, traffic collision avoidance system resolution advisory (TCAS RA) encounters and Category A separation events are 1.31 times more likely during the 30 minutes following the beginning of a service outage, as compared to the 30 minutes before the outage, for both types of facilities. Also, the separation index values are 19% lower following a surveillance facility outage and 4% lower following a communication facility service loss. This study provides evidence that unscheduled service outages of air traffic management facilities are associated with lost or reduced traffic separations and thus can be considered precursors to hazardous loss of separation events.     

Econometric Analysis of Realized Covariation: High Frequency Based Covariance,Regression, and Correlation in Financial Economics

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities.     

指令不均衡与股票收益关系研究——基于大规模数据分位数回归的实证

在指令不均衡与股票收益关系研究中,常常遇到两个困难：第一,不同市场环境下,前者对后者存在异质影响;第二,往往涉及大规模数据处理。为此,运用大规模数据分位数回归的方法,一方面揭示不同分位点处指令不均衡对股票收益的异质影响,细致刻画两者之间关系;另一方面适应大规模数据建模要求,得到更为可靠的结果。以上证A股和深证A股为研究对象,通过大规模数据分位数回归方法,得到了比均值回归更多有用信息。实证结果表明：第一,在高分位点处,滞后1期指令不均衡对股票收益具有正向影响且呈现上升趋势,而在低分位点却具有负向影响;第二,控制当期指令不均衡后,滞后期指令不均衡对股票收益具有负向影响,且随着分位点的增加呈现下降趋势。这些结果意味着,指令不均衡对股票收益具有一定的解释能力和预测能力。     

Providing Limited Local Electric Service During a Major Grid Outage: A First Assessment Based on Customer Willingness to Pay

While they are rare, widespread blackouts of the bulk power system can result in large costs to individuals and society. If local distribution circuits remain intact, it is possible to use new technologies including smart meters, intelligent switches that can change the topology of distribution circuits, and distributed generation owned by customers and the power company, to provide limited local electric power service. Many utilities are already making investments that would make this possible. We use customers' measured willingness to pay to explore when the incremental investments needed to implement these capabilities would be justified. Under many circumstances, upgrades in advanced distribution systems could be justified for a customer charge of less than a dollar a month (plus the cost of electricity used during outages), and would be less expensive and safer than the proliferation of small portable backup generators. We also discuss issues of social equity, extreme events, and various sources of underlying uncertainty.     

A MODEL FOR SWITCHING DISPATCHING RULES IN REAL TIME IN A FLEXIBLE MANUFACTURING CELL

We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern-recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If-Then” decision rules can be created from the model and portrayed in a decision-tree-like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.     

Applying data envelopment analysis on accounting data to assess and optimize the efficiency of Greek local governments

The efficiency and effectiveness of local governments has become one of the main points of interest in public sector administration, as decision and policy making gradually move from the central to the local level. This paper introduces an efficiency analysis framework based on accrual accounting data obtained from the local governments’ financial statements. Data envelopment analysis is used to obtain efficiency estimates, which are analyzed through a second stage regression against a set of efficiency explanatory factors. Furthermore, the optimal reallocation of the municipalities’ inputs and outputs is explored to provide policy recommendations that a central government could implement in a budget reduction context. Detailed empirical results are presented from a panel data set of Greek municipalities over the period 2002–2009.     

Pricing and momentum of syndicated credit in Europe

We investigate the determinants of syndicated loan prices for European borrowers, spanning the entire period of credit expansion and crisis. We construct a large data base consisting of 2102 rated syndicated loan deals from 1990 to 2008 in twenty three countries and ten economic sectors. We investigate the effects on the spread paid over the risk free rate of three major groups of factors: loan characteristics, borrower characteristics and characteristics of country of the borrower, using both qualitative and quantitative variables in a hedonic regression. The results show that all three groups are significant joint determinants of prices of syndicated credit. Our results confirm the existing literature but also provide for first time results stemming form the risk characteristics of the borrower and the country of the borrower. It is shown that distance-to-default as well as aggregate risk associated with country of the borrower are of great concern to the lenders and hence significantly affect the pricing of syndicated loans. Furthermore, we report that financial institutions and public utilities are able to negotiate for cheaper loans, but this result is reversed when financing is for acquisition purposes. Overall, risk, liquidity, solvency and sustainable performance by both the borrower and its domicile country are key determinants of syndicated loan prices.     

Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models

In this article, we discuss an outage‐forecasting model that we have developed. This model uses very few input variables to estimate hurricane‐induced outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.     

基于可变区间权重的中期用电量半参数预测模型

由于数据变化规律的多样性,中期电力负荷的波动有着不同于短期、长期负荷的特点。基于电力系统复杂性的研究视角,重点讨论了中期负荷预测过程中模型的不确定性、参数的时变特性以及负荷波动的周期性规律。根据中期负荷的数据特性,建立了基于非参数修匀的半参数模型,定义了函数区间的划分粒度以及模型权重的求解方法,提出了基于可变区间权重的动态预测方法,给出了基于经验模态分解和波动能量分析的噪声序列提取、检验方法。试验研究结果表明,气候因素对用电消耗的影响最大,经济因素次之;从选取的指标来看,不同时期的影响因素对于模型的解释能力是时变的;所提方法能够对电力负荷进行精确的多粒度、多维度分析,进而掌握其局部变化规律,可有效用于电力系统中期负荷预测。     

Dynamic DEA: A slacks-based measure approach

In data envelopment analysis, there are several methods for measuring efficiency changes over time, e.g. the window analysis and the Malmquist index. However, they usually neglect carry-over activities between two consecutive terms and only focus on the separate time period independently aiming local optimization in a single period, even if these models can take into account the time change effect. In the actual business world, a long time planning and investment is a subject of great concern. For these cases, single period optimization model is not suitable for performance evaluation. To cope with long time point of view, the dynamic DEA model incorporates carry-over activities into the model and enables us to measure period specific efficiency based on the long time optimization during the whole period. Dynamic DEA model proposed by Färe and Grosskopf is the first innovative contribution for such purpose. In this paper we develop their model in the slacks-based measure (SBM) framework, called dynamic SBM (DSBM). The SBM model is non-radial and can deal with inputs/outputs individually, contrary to the radial approaches that assume proportional changes in inputs/outputs. Furthermore, according to the characteristics of carry-overs, we classify them into four categories, i.e. desirable, undesirable, free and fixed. Desirable carry-overs correspond, for example, to profit carried forward and net earned surplus carried to the next term, while undesirable carry-overs include, for example, loss carried forward, bad debt and dead stock. Free and fixed carry-overs indicate, respectively, discretionary and non-discretionary ones. We develop dynamic SBM models that can evaluate the overall efficiency of decision making units for the whole terms as well as the term efficiencies.     

A study of DEA models without explicit inputs

In performance evaluations, data without explicit inputs (such as index data, pure output data) are widely used. To directly use such data, this paper presents a study on building DEA models without explicit inputs, so-called DEA-WEI models, which are applicable to the evaluation applications where inputs are not directly considered. We provide an axiom foundation of these kinds of models, and further discuss how to incorporate value judgments of decision makers into these DEA-WEI models. Several such models are derived. Finally, applications of the DEA-WEI models are presented.