Results for “hurricane events” — Page 4

DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part II: Risk Assessment

This comprehensive report explores the intricate interplay through “sector coupling” between the Transportation Sector, Energy Sector, and the critical infrastructure challenges faced by the United States. Aligned with the ambitious federal goal of reducing greenhouse gas emissions, the report analyzes the present vulnerabilities in the fossil fuel supply chain, highlighting instances where the dependence on this industry has been susceptible to local and global disruptions. With a focus on risk assessment, this report establishes a framework to compare the fossil fuel-based transportation sector with the burgeoning electrified sector, considering the evolving technologies in electric vehicles (EVs) and the nascent charging infrastructure. This study delves into the historical reliability of fossil fuel-based transportation and identifies opportunities for strengthening sector coupling between transportation and the electric sector. Assessments of the gasoline-diesel supply chain provide insights into both opportunities and threats, offering a roadmap for creating a more reliable and resilient electrified transportation system. We modeled sectoral coupling scenarios for the current internal combustion engine (ICE)-based and future EV-dominant landscapes, leveraging qualitative sector attributes to identify relative strengths and weaknesses in Part 1 of the report. In this report (Part 2) we continue to understand the role of the electricity sector on transportation and develop a semi-quantitative methodology is employed to evaluate and compare the risks associated with the fossil fueled and electric transportation sector during emergency scenarios, drawing from real-world events like hurricanes and geopolitical disruptions. Different operational scenarios for the fossil fuel and electrified sectors have been developed to understand the risks associated with them. This report concludes with recommendations for the architectural designs of electric charging infrastructure, underlining the need for informed decisions in the transition towards a sustainable and resilient transportation future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2010

Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

Kirschbaum, Dalia B.↗

NASA NTRS2010

Assessment of the Vulnerability of Water Resources to Seasonal Fires Across the Northern Sub-Saharan African Region

The northern sub-Saharan African (NSSA) region, extending from the southern fringes of the Sahara to the Equator, and stretching west to east from the Atlantic to the Indian ocean coasts, plays a prominent role in the distribution of Saharan dust and other airborne matter around the region and to other parts of the world, the genesis of global atmospheric circulation, and the birth of such major (and often catastrophic) events as hurricanes. Therefore, this NSSA region represents a critical variable in the global climate change equation. Recent satellite-based studies have revealed that the NSSA region has one of the highest biomass-burning rates per unit land area among all regions of the world. Because of the high concentration and frequency of fires in this region, with the associated abundance of heat release and gaseous and particulate smoke emissions, biomass-burning activity is believed to be a major driver of the regional carbon, energy, and water cycles. We acknowledge that the rainy season in the NSSA region is from April to September while biomass burning occurs mainly during the dry season (October to March). Nevertheless, these two phenomena are indirectly coupled to each other through a chain of complex processes and conditions, including land-cover and surface-albedo changes, the carbon cycle, evapotranspiration, drought, desertification, surface water runoff, ground water recharge, and variability in atmospheric composition, heating rates, and circulation. In this presentation, we will examine the theoretical linkages between these processes, discuss the preliminary results based on satellite data analysis, and provide an overview of plans for more integrated research to be conducted over the next few years.

Ichoku, Charles M.↗

NASA NTRS2017

Developing a Robust, Interoperable GNSS Space Service Volume (SSV) for the Global Space User Community

For over two decades, researchers, space users, Global Navigation Satellite System (GNSS) service providers, and international policy makers have been working diligently to expand the space-borne use of the Global Positioning System (GPS) and, most recently, to employ the full complement of GNSS constellations to increase spacecraft navigation performance. Space-borne Positioning, Navigation, and Timing (PNT) applications employing GNSS are now ubiquitous in Low Earth Orbit (LEO). GNSS use in space is quickly expanding into the Space Service Volume (SSV), the signal environment in the volume surrounding the Earth that enables real-time PNT measurements from GNSS systems at altitudes of 3000 km and above. To support the current missions and planned future missions within the SSV, initiatives are being conducted in the United States and internationally to ensure that GNSS signals are available, robust, and yield precise navigation performance. These initiatives include the Interagency Forum for Operational Requirements (IFOR) effort in the United States, to support GPS SSV signal robustness through future design changes, and the United Nations-sponsored International Committee on GNSS (ICG), to coordinate SSV development across all international GNSS constellations and regional augmentations. The results of these efforts have already proven fruitful, enabling new missions through radically improved navigation and timing performance, ensuring quick recovery from trajectory maneuvers, improving space vehicle autonomy and making GNSS signals more resilient from potential disruptions. Missions in the SSV are operational now and have demonstrated outstanding PNT performance characteristics; much better than what was envisioned less than a decade ago. The recent launch of the first in a series of US weather satellites will employ the use of GNSS in the SSV to substantially improve weather prediction and public-safety situational awareness of fast moving events, including hurricanes, flash floods, severe storms, tornados and wildfires. Thus, the benefits of the GNSS expansion and use into the SSV are tremendous, resulting in orders of magnitude return in investment to national governments and extraordinary societal benefits, including lives saved and critical infrastructure and property protected. However, this outstanding success is tempered by dual challenges: that for GPS, the current SSV specifications do not adequately protect SSV future use; and that for GNSS, the capabilities that are currently available are not protected in the future by specifications.

Bauer, Frank H.↗

NASA NTRS2017

Developing a Robust, Interoperable GNSS Space Service Volume (SSV) for the Global Space User Community

For over two decades, researchers, space users, Global Navigation Satellite System (GNSS) service providers, and international policy makers have been working diligently to expand the space-borne use of the Global Positioning System (GPS) and, most recently, to employ the full complement of GNSS constellations to increase spacecraft navigation performance. Space-borne Positioning, Navigation, and Timing (PNT) applications employing GNSS are now ubiquitous in Low Earth Orbit (LEO). GNSS use in space is quickly expanding into the Space Service Volume (SSV), the signal environment in the volume surrounding the Earth that enables real-time PNT measurements from GNSS systems at altitudes of 3000 km and above. To support the current missions and planned future missions within the SSV, initiatives are being conducted in the United States and internationally to ensure that GNSS signals are available, robust, and yield precise navigation performance. These initiatives include the Interagency Forum for Operational Requirements (IFOR) effort in the United States, to support GPS SSV signal robustness through future design changes, and the United Nations-sponsored International Committee on GNSS (ICG), to coordinate SSV development across all international GNSS constellations and regional augmentations. The results of these efforts have already proven fruitful, enabling new missions through radically improved navigation and timing performance, ensuring quick recovery from trajectory maneuvers, improving space vehicle autonomy and making GNSS signals more resilient from potential disruptions. Missions in the SSV are operational now and have demonstrated outstanding PNT performance characteristics; much better than what was envisioned less than a decade ago. The recent launch of the first in a series of US weather satellites will employ the use of GNSS in the SSV to substantially improve weather prediction and public-safety situational awareness of fast moving events, including hurricanes, flash floods, severe storms, tornados and wildfires. Thus, the benefits of the GNSS expansion and use into the SSV are tremendous, resulting in orders of magnitude return in investment to national governments and extraordinary societal benefits, including lives saved and critical infrastructure and property protected. However, this outstanding success is tempered by dual challenges: that for GPS, the current SSV specifications do not adequately protect SSV future use; and that for GNSS, the capabilities that are currently available are not protected in the future by specifications.

Bauer, Frank H.↗

NASA NTRS2018

Analysis and Applications of Water Vapor-Derived Multispectral Composites for Geostationary Satellites

Analysis of multispectral (red-green-blue, RGB) satellite image composites can be used to improve understanding of thermodynamic and / or dynamic features associated with the development of significant weather events (cyclones, hurricanes, intense convection, turbulence, etc.). These composites minimize the need for a more time consuming analysis of multiple images and can provide additional insight not available from a single channel. The enhanced water vapor imaging capabilities of the Advanced Baseline Imager on GOES-16,-17 satellites provide a unique opportunity to demonstrate this capability through a comparison of the Air Mass and Differential Water Vapor RGB image products for several case studies.

Jedlovec, Gary J.↗

NASA NTRS2019

Analysis and Applications of Water Vapor-Derived Multispectral Composites for Geostationary Satellites

Analysis of multispectral (red-green-blue, RGB) satellite image composites can be used to improve understanding of thermodynamic and / or dynamic features associated with the development of significant weather events (cyclones, hurricanes, intense convection, turbulence, etc.) The enhanced water vapor imaging capabilities of the Advanced Baseline Imager on GOES-16 and GOES-17 satellites provide a unique opportunity to demonstrate this capability through a comparison of the Air Mass (AM) and Differential Water Vapor (DWV) RGB image products for several case studies.

Berndt, Emily↗

NASA NTRS2019

Comprehensive Severe Weather Impact Assessment and Monitoring using Synthetic Aperture Radar and Auxiliary Data

Remote sensing datasets, particularly acquired by Synthetic Aperture Radar (SAR) sensors, have become increasingly important in severe weather disaster impact studies given their ability to observe the Earth largely irrespective of weather and sunlight conditions. The reliability of existing SAR change detection products applied on a pair of SAR images is constrained by the limitation of current methods to differentiate and classify disaster specific changes from anthropogenic surface alterations. Moreover, the inherent properties of the sensors, variations in SAR backscatter due to changes in surface conditions, and other factors exacerbate these limitations. We proposed a novel procedure expanding on earlier SAR-based change detection methods to exclude anthropogenic alterations and other sources of ambiguity that might lead to inaccurate mapping of the impacts of severe weather disasters. We applied the proposed procedure that is based on long term interferometric and amplitude-based change detection analyses of Sentinel- 1 SAR imagery to two study sites recently impacted by severe weather disasters (Flooding post severe weather events in urban centers; Hailstorm damage on crops). For the first case study, Sentinel-1 SLC scenes from two flood events in the Houston area (April 2016 flooding event and Hurricane Harvey of August-September 2017) were used to construct a flood map depicting areas repeatedly affected by the flood. Pixels with consistent coherence values in the pre-disaster coherence stack were retained for comparison with the pre- and post-disaster coherence stack and pixels with significant decline (greater than 60%) in coherence values were retained in the final flood map. The findings of the applied technique were calibrated and validated through datasets from NOAA/NWS Service storm reports, aerial imaging (NOAA and Civil Air Patrol), Federal Emergency Management Agency (FEMA) reporting, and targeted collections of NASA’s L-band UAVSAR data. Findings and products derived from the adopted methodology can be useful in disaster response and mitigation activities.

Gebremichael, Esayas↗

DOE OSTI2020

Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data

Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. Here, a synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response

controlled islanding↗

DOE OSTI2023

Real-time attribution of the influence of climate change on extreme weather events: a storyline case study of Hurricane Ian rainfall

Abstract As the Earth continues to warm due to human greenhouse gas emissions, there is a growing need to efficiently communicate the effect that global warming has on individual extreme weather events. Using a storyline approach, we present a rapid attribution of the increase in rainfall over Florida during Hurricane Ian in 2022 due to climate change as a case study. We find that extreme accumulated rainfall amounts associated with Hurricane Ian increased by 18%, far in excess of what would be expected by Clausius–Clapeyron scaling. The study offers a blueprint for rapid operational climate change attribution statements about extreme storms and other very rare weather events.

Reed, Kevin A. (ORCID:0000000337417080)↗

NASA NTRSDate not supplied

A Knowledge Graph Framework for Organizing Heterogeneous Datasets for Utilization in Classical and Quantum Computing: Current Challenges and Future Directions

"The escalating impact of climate change induced extreme weather events in urban, suburban, and rural environments demands a rethink of how we have been using the single event-based or use-case-based knowledge graph models. The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the interconnectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environmental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause mudslides, landslides, etc., The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc, can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a “common-denominator-variable” in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). We use the insights gained from EIKG to conduct classical and Quantum Machine Learning (QML) based data analysis on the research questions developed. Our preliminary study shows how the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC) are used along with the classical machine learning models to compare the model accuracies. Our study elaborates on how a quantitative analysis uses state-of-the-art machine learning techniques that include implementing both classical and quantum machine learning models and developing the knowledge graph. The EIKG is used to organize heterogeneous datasets and integrate the relations to case-specific extreme weather events such as floods. The study uses datasets such as county-to-country residential mobility data, socioeconomic datasets from the US Census Bureau, climate and weather-related Earth Observational data from NASA, and critical infrastructure data from the Homeland Infrastructure datasets."

Knowledge Graphs, Quantum Computing, Heterogenous ↗

DOE OSTI2022

On the Use of Satellite Nightlights for Power Outages Prediction

Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A sequential Attacker-Defender game for distribution systems resilience enhancement against extreme weather events

Improving distribution system resilience against frequent extreme weather events is important for reliable power system operations. Especially when dealing with events such as hurricanes that have short-term predictions, proactive pre-event preparedness plays a vital role in system resilience performance. In this paper, we propose a novel approach to construct pre-event resource allocation plans for system operators to cope with upcoming threats through a sequential attacker-defender game framework. The sequential attacker-defender game is designed to model the interaction between the extreme weather and the system operator. In each round of the game, the attacker and the defender sequentially update their current strategies by accounting for the opponent’s action set. The attacker model is formulated as a bi-level problem to identify the severe outage scenarios, and the defender model is formulated as a two-stage optimization problem to determine the allocation of restoration resources including mobile responsive resources and repair crews. Two scale-reduction strategies are proposed to ensure the scalability of the game scheme. Finally, case studies on the IEEE 33-bus and a 7149-node practical utility system validate the effectiveness of the proposed sequential game and the efficiency of the scale-reduction strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1998

Statistical Aspects of ENSO Events (1950-1997) and the El Nino-Atlantic Intense Hurricane Activity Relationship

On the basis of Trenberth's quantitative definition for marking the occurrence of an El Nino (or La Nina), one can precisely identify by month and year the starts and ends of some 15 El Nino and 10 La Nina events during the interval of 1950-1997, an interval corresponding to the most reliable for cataloging intense hurricane activity in the Atlantic basin (i.e., those of category 3-5 on the Saffir-Simpson hurricane scale). The main purpose of this investigation is primarily two-fold: First, the statistical aspects of these identified extremes and the intervening periods between them (called "interludes") are examined and, second, the statistics of the seasonal frequency of intense hurricanes in comparison to the extremes and interludes are determined. This study clearly demonstrates that of the last 48 hurricane seasons, 20 (42 percent) can be described as being "El Nino-related" (i.e., an El Nino was in progress during all, or part, of the yearly hurricane season--June-November), 13 (27 percent) as "La Nina-related" (i.e., a La Nina was in progress during all, or part, of the yearly hurricane season), and 15 (31 percent) as "interlude-related" (i.e., neither an El Nino nor a La Nina was in progress during any portion of the yearly hurricane season). Combining the latter two subgroups into a single grouping called "non-El Nino-related" seasons, one finds that they have had a mean frequency of intense hurricanes measuring 2.8 events per season, while the El Nino-related seasons have had a mean frequency of intense hurricanes measuring 1.3 events per season, where the observed difference in the means is inferred to be statistically important at the 99.8-percent level of confidence. Therefore, as previously shown more than a decade ago using a different data set, there undeniably exists an El Nino-Atlantic hurricane activity relationship, one which also extends to the class of intense hurricanes. During the interval of 1950-1997, fewer intense hurricanes occurred during El Nino-related seasons (always less than or equal to 3 and usually less than or equal to 2, this latter value having been true for 18 of the 20 El Nino-related seasons), while more usually occurred during non-El Nino-related seasons (typically greater than or equal to 2, having been true for 22 of the 28 non-El Nino-related seasons). Implications for the 1998 and 1999 hurricane seasons are discussed.

Wilson, Robert M.↗

NASA NTRS2009

The Urban Environmental Monitoring/100 Cities Project: Legacy of the First Phase and Next Steps

The Urban Environmental Monitoring (UEM) project, now known as the 100 Cities Project, at Arizona State University (ASU) is a baseline effort to collect and analyze remotely sensed data for 100 urban centers worldwide. Our overarching goal is to use remote sensing technology to better understand the consequences of rapid urbanization through advanced biophysical measurements, classification methods, and modeling, which can then be used to inform public policy and planning. Urbanization represents one of the most significant alterations that humankind has made to the surface of the earth. In the early 20th century, there were less than 20 cities in the world with populations exceeding 1 million; today, there are more than 400. The consequences of urbanization include the transformation of land surfaces from undisturbed natural environments to land that supports different forms of human activity, including agriculture, residential, commercial, industrial, and infrastructure such as roads and other types of transportation. Each of these land transformations has impacted, to varying degrees, the local climatology, hydrology, geology, and biota that predate human settlement. It is essential that we document, to the best of our ability, the nature of land transformations and the consequences to the existing environment. The focus in the UEM project since its inception has been on rapid urbanization. Rapid urbanization is occurring in hundreds of cities worldwide as population increases and people migrate from rural communities to urban centers in search of employment and a better quality of life. The unintended consequences of rapid urbanization have the potential to cause serious harm to the environment, to human life, and to the resulting built environment because rapid development constrains and rushes decision making. Such rapid decision making can result in poor planning, ineffective policies, and decisions that harm the environment and the quality of human life. Slower, more thought-out, decision making could result in more favorable outcomes. The harm to the environment includes poor air quality, soil erosion, polluted rivers and aquifers, and loss of wildlife habitat. Human life is then threatened because of increased potential for disease spreading, human conflict, environmental hazards, and diminished quality of life. The built environment is potentially threatened when cities are built in areas that can be impacted by events such as hurricanes, tsunamis, earthquakes, fires, and landslides. Our goals include assessing the threat of such events on cities and the people living there.

Stefanov, William L.↗

DOE OSTI2020

Financing Microgrids in the Federal Sector

Recent high-impact events such as hurricanes and wildfires highlight the need for energy system resilience. One potential impact of these events is the loss of utility grid power to federal sites, negatively affecting the ability to carry out their missions. Depending on the need to sustain critical functions, it may be important to continue operations during an outage of the electric grid, or at least have power restored quickly after the event. One approach to addressing this need is through a microgrid. Microgrids can provide a more reliable solution than the traditional approach of using a backup generator at individual buildings. A microgrid can enhance a site’s electrical power system during normal operations, as well as provide reliable backup power to critical loads when electric utility power is interrupted. Many times, certain components of a microgrid (and potentially the entire system) can be justified economically and paid for out of energy savings or avoided costs. In cases where savings can cover the project costs, one of several privately financed procurement mechanisms may be used to implement the project. Procurement options available to all federal agencies include utility energy services contracts (UESC), energy savings performance contracts (ESPC), and utility services contracts (USC); other mechanisms such as a power purchase agreement (PPA), enhanced use lease (EUL), and utility privatization (UP) may also be useful to agencies that have the authority to use them. Implementing a microgrid in a comprehensive effort that also includes energy efficiency can provide an important savings component, while reducing power requirements for critical loads, thus reducing the cost of the microgrid and associated distributed energy resources (DER). However, it can be difficult to achieve enough savings and avoided costs to cover the entire microgrid system, so leveraging these avoided costs in combination with appropriated funds may be necessary to implement the full project. When a site is deciding if a microgrid is the best solution, activities early in the process include identifying critical loads and existing DERs, conversations with the local electric utility, and implementation options that work best for the agency and site. This paper explores procurement options and agreements that may be useful to federal agencies interested in implementing microgrids at their sites. Microgrid complexities, implementation considerations, and suggestions to get started are also discussed. This information will be most useful to agencies when they are exploring electric energy system resilience options and/or considering different procurement options to meet their needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Financing Microgrids in the Federal Sector

Recent high-impact events such as hurricanes and wildfires highlight the need for energy system resilience. One potential impact of these events is the loss of utility grid power to federal sites, negatively affecting the ability to carry out their missions. Depending on the need to sustain critical functions, it may be important to continue operations during an outage of the electric grid, or at least have power restored quickly after the event. One approach to addressing this need is through a microgrid. Microgrids can provide a more reliable solution than the traditional approach of using a backup generator at individual buildings. A microgrid can enhance a site’s electrical power system during normal operations, as well as provide reliable backup power to critical loads when electric utility power is interrupted. Many times, certain components of a microgrid (and potentially the entire system) can be justified economically and paid for out of energy savings or avoided costs. In cases where savings can cover the project costs, one of several privately financed procurement mechanisms may be used to implement the project. However, it can be difficult to achieve enough savings and avoided costs to cover the entire microgrid system, so leveraging these avoided costs in combination with appropriated funds may be necessary to implement the full project. This paper explores procurement options and agreements that may be useful to federal agencies interested in implementing microgrids at their sites. Microgrid complexities, implementation considerations, and suggestions to get started are also discussed. This information will be most useful to agencies when they are exploring electric energy system resilience options and/or considering different procurement options to meet their needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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↗