Power Outage Duration in Louisiana by Customer Endpoint and Environmental Conditions

Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, communities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the relationship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.

During the functioning of power supply system, there can be situations where the culprit in interruptions of power supply to consumers and a power quality violation is a power supply company or a consumer himself. Therefore, the economic incentive for power supply companies and consumers to increase power supply reliability and power quality is an urgent task. To implement such incentives, it is necessary to control the facts and time of power supply outages and their values as well as cases and time of non-compliance of power quality with the requirements of standards. It is possible with the use of a monitoring system for power supply reliability and power quality. (Research purpose) The research purpose is in developing a technical and economic method for stimulating power supply companies and consumers to increase efficiency of power supply system. (Materials and methods) The article provides a review of the structural diagram of a system for monitoring power supply reliability and power quality including devices for monitoring the number and duration of power outages and voltage deviations. (Results and discussion). An economic method has been developed to stimulate power supply companies and consumers to increase power supply system efficiency. The essence of the method is to control the parameters of power supply reliability and power quality, identify the violation of these parameters, determine the culprit of the violation, determine the time characteristics of the violation, summarize the duration of violations for the reporting period, compare the actual amount of duration with the allowable one, determine the amount of compensation for the violation and impose sanctions on payment compensation by the perpetrators of violations of these parameters. The article presents an algorithm for adjusting the cost of electricity supplied to consumers depending on the number and duration of voltage deviations and the number and duration of outages. The algorithm serves to ensure the operation of the specified technical and economic method. (Conclusions) The algorithm works in conjunction with a system for monitoring power supply reliability and power quality based on signals from devices that control the number and duration of outages and voltage deviations.

IntroductionElectricity outages are common in low/middle-income countries and have been shown to adversely affect the operation of health facilities; however, little is known about the effect of outages on the...

The need for monitoring the electrical network parameters is identified to use methods and means to improve power supply reliability and power quality. The article lists the exiting sensors for monitoring electrical parameters and substantiates the necessity of monitoring the parameters at both sides of switching devices. In the paper, there is basic information on the structure, operation and capabilities of the monitoring system for power supply reliability and power quality. A functional electrical circuit of the device for monitoring the number and duration of power outages and voltage deviations is proposed for monitoring the parameters at both sides of switching devices. An algorithm for the device operation has also been developed, which allows detecting the main emergency modes in the consumer’s internal network. The article also describes laboratory tests of a prototype of the device for monitoring the number and duration of power outages and voltage deviations, which is based on the Arduino NANO V3 ATmega 328 microprocessor.

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.

Many electricity customers in rural areas served by single radial transmission or sub-transmission line have been experiencing long duration of power outages. The need for reliable power supply, grid resiliency and rapid restoration is pushing utilities to take a renewed look at the operation of these rural feeders. One possible solution is to operate rural communities as a microgrid when the power supply line is out of service due to a permanent fault or for any other reason. The work presented in this paper examines technical and economic feasibility of operation of the two such remote communities in the rural areas of New York in a microgrid mode. The focus of this paper is to present high-level results of the case study and also to provide recommendations for utilities to evaluate the feasibility of microgrid operation as a potential solution to improve their power supply reliability and grid resiliency.

Abstract. Aim. Enable prediction and planning for large-scale unprecedented power outages of importance for emergency planning and national response actions. Predict outage probability, duration and restoration using a theoretical framework that is applicable globally. Methods. Data have been collected for power losses and outage duration for a wide range of events in Belgium, Canada, Eire, France, Japan, Sweden, New Zealand and USA. A new theory and correlation is given for the probability of large regional power losses of up to nearly 50,000 MW(e) without additional infrastructure or grid damage. For severe and rare events with damage (major floods, fire, ice storms, hurricanes etc.) the outages are longer and the restoration probability depends on the degree of difficulty that limits access and restoration. The dynamic reliability requirements for emergency back-up power and pumping systems are derived, and demonstrated using the flooding of New Orleans by Hurricane Katrina and of the Fukushima nuclear reactors by a tsunami. Conclusions . Explicit expressions have been given and validated for the probability and duration for the full range from “normal” large power losses to extended outages due to rare and more severe events with access and repair difficulty.

Extreme weather and climate events have profound effects on societies, economies, and environments worldwide. As a result, meteorological and hydrological service providers and researchers are increasingly considering factors such as hazard, exposure, and vulnerability in order to gain a comprehensive understanding of the full impact of weather and to contribute to minimizing fatalities and losses, thus enhancing resilience. Implementing these goals involves various technical and methodological approaches and numerous challenges in developing effective operational products and services.In the case of the Basque Country, as in other parts of the world, improving the resilience of our societies against weather impacts is a current priority and will become increasingly important in the future due to the potential local increase in severe weather events and exposure. As a first step in this complex process of characterizing weather impacts, accurate information from past events must be collected, prepared, and maintained. To this end, a severe weather catalogue has recently been implemented, focusing on local weather impacts, to record detailed information about severe or adverse weather events that affect our society. This catalogue serves as a repository of valuable information for characterizing and evaluating extreme weather events and its consequences.In this contribution, we present an in-depth study of severe weather events that have occurred in our territory during this century, including an analysis of the associated environmental conditions and their impacts. The main objective of this work is to enhance our understanding of local weather impacts by examining synoptic and local hydro-ocean-meteorological conditions conveniently correlated with the degree of impact. We analyze different key aspects aggregated by season, event typology, hazard type, and other relevant factors. Throughout this work, we present and discuss statistical findings to provide perspective and characterize the main aspects of the common hazards and impacts experienced by our territory. Dealing with impact, various indicators are defined and categorized to characterize key aspects, including severe weather warning levels, economic damages, human fatalities, or disruptions to normal life.

This paper investigates household preferences regarding an improved supply of electricity in rural Bangladesh, where the expansion of stable electricity is an urgent policy issue. The paper examines household preferences regarding reductions in the frequency and duration of power outages. It also examines prior notification mechanisms that do not necessarily provide an increased supply of electricity but that allow households to prepare for potential power failures. A questionnaire survey designed as a choice experiment was applied to households to elicit preferences. The econometric analysis reveals that villagers prefer a reduction in both the frequency and duration of power outages and a 1-day prior notification of power outages. There are slight disparities in preferences according to the season and the timing of improvements (e.g., summer or winter and all day or peak hours). Thus, the present study may be beneficial for policymakers when considering the provision of electricity supply improvements in rural areas in exchange for slight increases in electricity tariffs.

The proposed system for monitoring number and duration of power outages and power quality in 0.38 kV power networks makes it possible to shorten the power supply restoration time by approximately one hour by reducing the time for obtaining information about the damage and by approximately one hour by the reduction of the time for determining the location and type of damage. Besides, the effect can also be obtained by minimizing power quality inconsistency time with the standardized values. The sensors of the monitoring system are proposed to be located at customer inputs or at several network points, for example, at the beginning, in the middle or at the end of the power network as well as at the transformer substation bus bars.

PurposeAn unreliable supply of grid electricity has a strong negative impact on industrial and commercial profitability as well as on household activities and government services that rely on electricity supply. This unreliable grid electricity could be a result of technical and security factors affecting the grid network. Therefore, this study aims to investigate the effects of technical and security factors on the transmission and distribution of grid electricity in Uganda.Design/methodology/approachThis study used the ordinary least squares (OLS) and autoregressive distributed lag (ARDL) models to examine the effects of technical and security factors on grid electricity reliability in Uganda. The study draws upon secondary time series monthly data sourced from the Uganda Electricity Transmission Company Limited (UETCL) government utility, which transmits electricity to both distributors and grid users. Additionally, data from Umeme Limited, the largest power distribution utility in Uganda, were incorporated into the analysis.FindingsThe findings revealed that technical faults, failed grid equipment, system overload and theft and vandalism affected grid electricity reliability in the transmission and distribution subsystems of the Ugandan power grid network. The effect was computed both in terms of frequency and duration of power outages. For instance, the number of power outages was 116 and 2,307 for transmission and distribution subsystems, respectively. In terms of duration, the power outages reported on average were 1,248 h and 5,826 h, respectively, for transmission and distribution subsystems.Originality/valueThis paper investigates the effects of technical and security factors on the transmission and distribution grid electricity reliability, specifically focusing on frequency and duration of power outages, in the Ugandan context. It combines both OLS and ARDL models for analysis and adopts the systems reliability theory in the area of grid electricity reliability research.

We investigated the link between satellite-based lightning detection and severe weather occurrences, focusing on the hypothesis that severe thunderstorms with strong updrafts would produce a high quantity of relatively small lightning flashes. This study utilizes data from the Meteosat Third Generation Lightning Imager (MTG-LI) and ground reports from the European Severe Weather Database (ESWD), encompassing 22.7 million lightning flashes and 26,000 severe weather reports from July 2024 to May 2025.While over half of all severe weather events were not accompanied by ‘small’ lightning flashes, size boundary defined by the 25th percentile of the lightning size distribution of the data, a distinct subset of events, particularly large hail, exhibited an exceptionally high concentration, with some instances exceeding 1900 small flashes within the 45- minute window and 20km radius. Specifically, 19.7% of all severe weather events, with and without lightning, were associated with a ‘high’ density of small flashes, a threshold determined by the 75th percentile of the distribution of a density grid specifically designed for this research. Results showed that hail events are notably more correlated with a higher concentration of small lightning flashes compared to other severe weather types, with this correlation increasing significantly with hail size, reaching up to 83.3% for hail over 8cm. We will report on our efforts to geographically and statistically distinguish regions less prone to reporting, aiming to improve the reliability of hotspot-to-event correlations. This study highlights the potential of MTG-LI data as a valuable indicator for nowcasting severe weather, especially for large hail events.

An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

A real-time, weather-adaptive three-dimensional variational data assimilation (3DVAR) system has been adapted for the NOAA Warn-on-Forecast (WoF) project to incorporate all available radar observations within a moveable analysis domain. The key features of the system include 1) incorporating radar observations from multiple Weather Surveillance Radars-1988 Doppler (WSR-88Ds) with NCEP forecast products as a background state, 2) the ability to automatically detect and analyze severe local hazardous weather events at 1-km horizontal resolution every 5 min in real time based on the current weather situation, and 3) the identification of strong circulation patterns embedded in thunderstorms. Although still in the early development stage, the system performed very well within the NOAA's Hazardous Weather Testbed (HWT) Experimental Warning Program during preliminary testing in spring 2010 when many severe weather events were successfully detected and analyzed. This study represents a first step in the assessment of this type of 3DVAR analysis for use in severe weather warnings. The eventual goal of this real-time 3DVAR system is to help meteorologists better track severe weather events and eventually provide better warning information to the public, ultimately saving lives and reducing property damage.

The present work established a 7-year climatology of the initiation, decay, and morphology of severe convective storms (SCSs) during the warm seasons (May–September) of 2011–2018 (except 2014) over North China. This was achieved by using severe weather reports, precipitation observations, and composite Doppler radar reflectivity data. A total of 371 SCSs were identified. SCSs primarily initiated around noon with the highest frequency over the high terrain of Mount Taihang, and they mostly decayed over the plains at night. The storm morphologies were classified into three types of cellular storms (individual cells, clusters of cells, and broken lines), six types of linear systems (convective lines with no stratiform, with trailing stratiform, leading stratiform, parallel stratiform, embedded lines, and bow echoes), and nonlinear systems. Three types of severe convective weather, namely, short-duration heavy rainfall, hail, and thunderstorm high winds associated with these morphologies were investigated. Nonlinear systems were the most frequent morphology, followed by clusters of cells. Convective lines with trailing stratiform were the most frequent linear morphology. A total of 1,429 morphology samples from the 371 SCSs were found to be responsible for 15,966 severe convective weather reports. Linear (nonlinear) systems produced the most short-duration heavy rainfall (hail and thunderstorm high wind) reports. Bow echos were most efficient in producing both short-duration heavy rainfall and thunderstorm high wind reports whereas broken lines had the highest efficiency for hail production. The results in the present study are helpful for local forecasters to better anticipate the storm types and associated hazardous weather.