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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Verification and Enhancement of VIIRS Day-Night Band (DNB) Power Outage Detection Product

This case study of Hurricane Matthew (October 2016) uses the NASA Short-Term Prediction Research and Transition (SPoRT) Center DNB power outage product (using GSFC VIIRS DNB preliminary Black Marble product, Roman et al.. 2017) and 2013 LandScan Global population data to look for correlations between the post-event %-of-normal radiance and the utility company-reported outage numbers (obtained from EAGLE-1).

DISASTER↗
DOE OSTI2021

Designing resilient decentralized energy systems: The importance of modeling extreme events and long-duration power outages

Mitigating and adapting to climate change requires decarbonizing electricity while ensuring resilience of supply, since a warming planet will lead to greater extremes in weather and, plausibly, in power outages. Although it is well known that long-duration outages severely impact economies, such outages are usually not well characterized or modeled in grid infrastructure planning tools. Here, we bring together data and modeling techniques and show how they can be used to characterize and model long-duration outages. We illustrate how to integrate outages in planning tools for one promising mode of resilient energy supply—microgrids. Failing to treat these extremes in models can lead to microgrid designs (1) that do not realize their full value of resilience, since models do not see the benefits of protecting against extremes, and (2) that appear reliable on paper yet do not actually protect against extremes. Although utilities record power interruptions, lack of access to that data is hindering research on resilience; making datasets available publicly would substantially aid efforts to improve grid planning tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Individual and Collective Strategies to Limit the Impacts of Large Power Outages of Long Duration

As modern society becomes ever more dependent on the availability of electric power, the costs that could arise from individual and social vulnerability to large outages of long duration (LLD-outages) increases. During such an outage, even a small amount of power would be very valuable. Here, this article compares individual and collective strategies for providing limited amounts of electric power to residential customers in a hypothetical New England community during a large electric power outage of long duration. We develop estimates of the emergency load required for survival and assess the cost of strategies to address outages that last 5, 10, and 20 days in either winter or summer. We find that the cost of collective solutions could be as much as 10 to 40 times less than individual solutions (less than $2 per month per home). However, collective solutions would require community-wide coordination, and if local distribution system lines are destroyed, only individual back-up systems could provide contingency power until those lines are repaired. Costs might be reduced if more robust distributed generation were employed that could be operated continuously with the ability to sell power back to the grid. Our cost-effectiveness analysis only assesses what could be done, developing estimates of preparedness cost. A decision about what should be done would require additional input from a range of stakeholders as well as some form of analytical deliberative process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Capturing Infrastructure Interdependencies for Power Outages Prediction During Extreme Events

As extreme weather events such as hurricanes, severe thunderstorms, and floods grow in frequency and intensity, the disruption of power grid systems poses significant challenges, including widespread electrical outages, economic losses, and threats to public safety. This paper presents a forward-looking approach that leverages geographical graph-based machine learning models to predict county-level maximum power outages during such events. By capturing the intricate interdependencies within power system networks, our approach aims to provide precise and actionable predictions that can optimize emergency response efforts and enhance grid resilience. Through the integration of real-world data, including hurricane advisories and power outage records, we have trained and benchmarked multiple machine learning models, demonstrating the feasibility and potential of this method. While our initial results are promising, this paper also charts a course for advancing these models, addressing the remaining challenges, and ultimately transforming how we anticipate and respond to the impacts of extreme weather on power systems.

Lee, Sangkeun (Matt) [ORNL] (ORCID:000000021317511↗
NASA NTRS2008

Use of VIIRS DNB Data to Monitor Power Outages and Restoration for Significant Weather Events

NASA fs Short-term Prediction Research and Transition (SPoRT) project operates from NASA's Marshall Space Flight Center in Huntsville, Alabama. The team provides unique satellite data to the National Weather Service (NWS) and other agencies and organizations for weather analysis. While much of its work is focused on improving short-term weather forecasting, the SPoRT team supported damage assessment and response to Hurricane Superstorm Sandy by providing imagery that highlighted regions without power. The team used data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The VIIRS low-light sensor, known as the day-night-band (DNB), can detect nighttime light from wildfires, urban and rural communities, and other human activity which emits light. It can also detect moonlight reflected from clouds and surface features. Using real time VIIRS data collected by our collaborative partner at the Space Science and Engineering Center of the University of Wisconsin, the SPoRT team created composite imagery to help detect power outages and restoration. This blackout imagery allowed emergency response teams from a variety of agencies to better plan and marshal resources for recovery efforts. The blackout product identified large-scale outages, offering a comprehensive perspective beyond a patchwork GIS mapping of outages that utility companies provide based on customer complaints. To support the relief efforts, the team provided its imagery to the USGS data portal, which the Federal Emergency Management Agency (FEMA) and other agencies used in their relief efforts. The team fs product helped FEMA, the U.S. Army Corps of Engineers, and U.S. Army monitor regions without power as part of their disaster response activities. Disaster responders used the images to identify possible outages and effectively distribute relief resources. An enhanced product is being developed and integrated into a web mapping service (WMS) for dissemination and use by a broader end user community.

Jedlovec, Gary↗
DOE OSTI2023

Shedding light on the economic costs of long-duration power outages: A review of resilience assessment methods and strategies

Here this paper provides a literature review of methods and modeling techniques to estimate the cost of power system outages, along with the value of outage mitigation or system resilience. Regulators, policymakers, and infrastructure owners have a growing need to understand the methods for estimating the benefits of resilience improvements of electric infrastructure against natural and man-made disasters. There is a broad literature that estimates the cost of short-duration outages and a small but developing literature on estimating the cost of long-duration outages. This article reviews the models used to estimate the cost of outages and discusses their relative strengths. Additionally, this paper identifies key questions from stakeholders regarding resilience investment and maps them to the relevant models that would help answer them. We include recommendations for future work to include recent advances in regional economic modeling that can estimate region and demographic-specific costs and the distributional consequences of potential resilience projects.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

RePOWERD: Restoration of Power Outage from Wide-area Severe Weather Disruptions

The purpose of the RePOWERD project is to develop a probabilistic and a simulation based power restoration forecasting model for tropical storms based on geographic utility service areas, impacts to energy infrastructures and transportation networks, time-varying customer outage number, crew information (i.e., crew size, crew staging requirements), and utility restoration plans. Energy infrastructure is a critical lifeline essential for the United States’ national and economic security. Like other critical infrastructures, this infrastructure is aging and is at a high risk of damages from extreme weather events. Based on the number of reported outages in EAGLE-I during 2020 and 2021, it is evident that the current energy infrastructure is not equipped to handle extreme event impacts and will contribute to significant outages. From a resilience perspective, it is essential to forecast restoration time in the event of a severe weather induced power outage to assist the US Department of Energy, emergency managers, and first responders with resource planning. This project contributes to this crucial need. This report details the models, that were developed in this pilot study, to determine the rate of restoration and estimated time of restoration (ETR) during tropical storm events within counties and utility service areas based on damage to energy infrastructures and total number of customers experiencing outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Variational data augmentation for a learning-based granular predictive model of power outages

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. Here, in this study, we propose to develop and implement a multi-model framework as an operational tool based on a granular and multi-day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time-series data. Case studies are performed to demonstrate the performance of the new framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

54 ENVIRONMENTAL SCIENCES↗