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At least 19 records

Investigating the Determinants of Household Capabilities Burden During Power Outages: The Case of Winter Storm Uri

Existing research primarily uses census data to identify the vulnerability of communities to hazards. These vulnerability indices provide aggregated data and are not hazard-specific nor well-validated with post-event data. In contrast, our study uses household survey data (n=1065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, and the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden' and ‘heavier burden' households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have larger households with more young children, fewer adults over 65, lower household incomes, been impacted by the COVID-19 pandemic, and more family characteristics that made life harder. We also fit a logistic regression model to assess the role of outage, household, and community characteristics in predicting differences in capabilities burden. Our results offer insights into enumerating the consequences of utility service disruptions on households, which can inform more targeted and equitable resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Isotopic and trace element characteristics of an unusual refractory inclusion from Essebi

The isotopic and chemical properties of Ca-Al-rich inclusions (CAI) provide important clues to the early solar nebula environment. While the abundances of refractory major and trace elements are similar to those expected for high temperature condensates, the variety of textural, chemical, and isotopic signatures indicate most CAI experienced complex, multi-stage histories involving repeated episodes of condensation, evaporation, and metamorphism. Evidence of multiple processes is especially apparent in an unusual refractory inclusion from Essebi (URIE) described by El Goresy et al. The melilite (mel)-rich core of URIE contains polygonal framboids of spinel (sp) and hibonite (hb) or sp and fassaite (fas) and is surrounded by a rim sequence consisting of five layers. In contrast to rims on Allende, the mineralogy of the URIE rim layers becomes increasingly refractory from the core outwards, ending in a layer of spinel-Al2O3 solid solution + Sc-rich fassaite. The chemical and mineralogical features of URIE are inconsistent with crystallization from a homogeneous melt, and El Goresy et al. proposed a multi-step history involving condensation of sp + hb and aggregation into framboids, capture of framboids by a refractory silicate melt droplet, condensation of rim layers, and alteration of mel to calcite and feldspathoid. The PANURGE ion probe was used to investigate the isotopic and trace element characteristics of URIE to develop a more complete picture of the multiple processes leading to formation and metamorphism.

Deloule, E.↗

Normalizing Resource Identifiers using Lexicons in the Global Change Information System: Linking Earth Science Identifiers, Concepts, and Communities

Earth Science informatics involves collaboration between multiple groups of people with diverse specializations and goals,often using variations in terminology to refer to common resources. The uniformity of the resource identifiers often does not cross organizational boundaries. Because of this, permanent, widely used, unambiguous identifiers for resources are elusive. We examine real world cases of changing and inconsistent identifiers which inherently work against persistence and uniformity. We also present a solution which mediates factors in these situations; namely the creation of lexicons:mappings of sets of terms to URIs which are curated within the Global Change Information System (GCIS). We discuss aspects of the GCIS which facilitate the use of lexicons: an information model which disambiguates resources, a RESTful API which provides metadata through content-negotiation, and a strategy for long term curation of URIs, including mechanisms for handling changes to URIs and variations in terms used by different communities while providing persistent URIs and preserving relationships between resources We provide working definitions of terms,contexts, and lexicons, and relate them to the practical challenges of disambiguation and curation. We also discuss the mechanisms employed and architecture of the GCIS, and how these choices facilitate representation of persistent identifiers and mappings of them to identifiers used colloquially within various earth science communities of practice.

Linkded Data↗

Analyzing at-scale distribution grid response to extreme temperatures

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. Herein, this paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. These numbers are consistent with state-of-the-art prediction results published in the literature. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

STS-102 Expedition 2 Increment and Science Briefing

Merri Sanchez, Expedition 2 Increment Manager, John Uri, Increment Scientist, and Lybrease Woodard, Lead Payload Operations Director, give an overview of the upcoming activities and objectives of the Expedition 2's (E2's) mission in this prelaunch press conference. Ms. Sanchez describes the crew rotation of Expedition 1 to E2, the timeline E2 will follow during their stay on the International Space Station (ISS), and the various flights going to the ISS and what each will bring to ISS. Mr. Uri gives details on the on-board experiments that will take place on the ISS in the fields of microgravity research, commercial, earth, life, and space sciences (such as radiation characterization, H-reflex, colloids formation and interaction, protein crystal growth, plant growth, fermentation in microgravity, etc.). He also gives details on the scientific facilities to be used (laboratory racks and equipment such as the human torso facsimile or 'phantom torso'). Ms. Woodard gives an overview of Marshall Flight Center's role in the mission. Computerized simulations show the installation of the Space Station Remote Manipulator System (SSRMS) onto the ISS and the installation of the airlock using SSRMS. Live footage shows the interior of the ISS, including crew living quarters, the Progress Module, and the Destiny Laboratory. The three then answer questions from the press.

Source record↗

A valuation framework for customers impacted by extreme temperature-related outages

Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI), such as increased mortality rates, property damage, and reduced productivity, due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. In conclusion, these findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Evaluating the Impact of Power Outages on Occupancy Patterns During the 2021 Texas Power Crisis

Large-scale power outages, such as those caused by extreme weather events, have a big impact on human behavior. A short power outage is merely a nuisance for most, and may not change people's locations. An outage that lasts for a few hours can result in spoiled food and medical supplies, and people will have to restock spoiled items. Long outages result in temperatures outside tolerable levels in homes, and may prompt people to acquire supplies, such as generators and gas, or change location. The long outages during Winter Storm Uri in Texas resulted in millions of dollars in property damage due to freezing pipes. This level of damage is expected to result in a sharp increase in supply runs and contractor activity. In this paper, we present a tool to explore differences in visiting patterns before, during, and after power outages. It allows to compare different points of interest like medical facilities, grocery stores, hardware stores, and other types of businesses.

big data↗

A Framework to Design Consumer-Centric Operational Strategies for Resilience Enhancement

Extreme temperature-related events like heat waves or cold snaps can significantly stress the power distribution grid as electricity demand spikes leading to brownouts or blackouts if not managed properly. In addition, such extreme events can exacerbate inequity, with vulnerable populations (for example, houses with poor insulation, located in non-critical zones, and lack of local resources) at greater risk. There is a growing deployment of distribution systems automation technologies such as advanced metering infrastructure, sensors, and automated control systems to enhance the visibility of the entire distribution grid while meeting resilience objectives. Here, this paper proposes a framework for investigating how automation can improve the distribution system's resilience and ensure customers' health and safety during such extremes. The proposed framework's efficacy will be demonstrated for the Electricity Reliability Council of Texas (ERCOT) during winter storm Uri with varying levels of distribution automation technologies such as feeder isolation using smart switches, remote outage signals using advanced meters, and comfort-aware outage using advanced analytics and communication. Simulation results show that an outage strategy involving advanced grid technology and communication can reduce the occupant exposure to severe cold by 93% and, simultaneously, reduce expected energy not served by 73.2% when compared against feeder isolation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Techno-Economic Analysis of Data-Driven and Transactive Approaches for Resilience Enhancement

As extreme weather events lead to more frequent power outages, understanding and enhancing grid resilience is critical to mitigating economic losses and non-energy impacts from service disruptions. Here, this study introduces a novel techno-economic analysis framework for evaluating resilience enhancement mechanisms. The framework combines grid response modeling with a co-simulation approach and valuation methodology to provide a comprehensive assessment. We apply this framework to a realistic case study of the Texas grid during Winter Storm Uri in February 2021. Two advanced resilience strategies are analyzed: a data-driven rolling outage mechanism and a transactive energy (TE) based allocation scheme. The rolling outage scheme selectively serves customers based on real-time curtailment needs, while the TE scheme allows customers to trade energy allocations according to their preferences. Our findings show that both the rolling outage and TE schemes significantly outperform conventional methods (i.e. controlled outages) by reducing the amount of energy not supplied to customers by 41% and 64%, respectively. These approaches also enhance flexibility and customer satisfaction, while improving energy utilization for greater resilience. Additionally, they maintain thermal comfort about 3.5 times better and substantially lower customer risk exposure. A key contribution of this study is addressing both utility and customer perspectives while considering both energy and non-energy impacts. The techno-economic analysis indicates that implementing these resilience enhancement strategies would incur an additional 1.1Bto1.6B in utility costs but has the potential to avoid 17.3Bto18B of customer losses as compared to existing solutions, thereby underscoring the value of investing in advanced resilience, as it provides significant societal benefits to customers.

42 ENGINEERING↗

Draft Specification for Representing Gamma Radiation Spectra in QR codes

This document defines a proposed specification for representing gamma radiation spectra, as commonly produced by handheld Radioisotope Identifiers, as a QR code, or as a Uniform Resource Identifier (URI). The intended primary application is transferring spectra between locations or devices using standard smart-phone capabilities when data transmission would otherwise be challenging or not possible. The proposed encoding also enables embedding of spectra within other documents as hyperlinks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Dataset for: Electric Vulnerability Index: Targeted Energy Storage Implementation Metric

Uninterrupted access to electricity is critical to the safety and security of American households. More frequent and extreme emergency events increase outages across the country, disproportionately impacting vulnerable communities that experience the most frequent and longest outages, are most sensitive to the loss of electric power, and have the least capacity to adapt to these conditions. This study devises a metric, the Electric Vulnerability Index (EVI), and validates this metric against the 2021 Winter Storm Uri in Texas. Though not ubiquitous, similar trends were observed between adjacent areas with higher EVI and those with higher outage rates from this storm. EVI is offered as a viable approach to quantify a population’s vulnerability to electric outages and maps that index across the continental United States to aid policymakers, advocates, and energy system stakeholders in the targeted deployment of resilience solutions, such as energy storage, to communities most in need. This dataset includes the geopackage file containing all relevant attributes used to generate the maps used in the accompanying paper.

Kerby, Jessica [Pacific Northwest National Laborat↗

DMTN-302: IVOA Identifier Usage at the Rubin Observatory

The IVOA define a standard URI scheme to reference individual datasets. In this document we will define how these IVOA Identifiers will be formed for Butler datasets and possibly object catalog IDs.

79 ASTRONOMY AND ASTROPHYSICS↗

Operational Characteristics of Four Commercially Available Personal Cooling Vests

Personal thermoregulatory systems which provide chest cooling are used in the industrial and aerospace environments to alleviate thermal stress. However, little information is available regarding the physiologic and circulatory changes produced by routine operation of these systems. The objectives of this study were to compare the effectiveness of two passive and two active cooling vests, and to measure the body temperature and circulatory changes produced by each cooling vest configuration. The MicroClimate Systems and the Life Enhancement Tech(LET) lightweight liquid cooling vests, the Steele Vest and LET's Zipper Front Garment were used to cool the chest region of 11 male and 10 female subjects (25 to 55 yr.) in this study. Calf, forearm and finger blood flows were measured using a tetrapolar impedance rheograph. The subjects, seated in an upright position at normal room temperature (approx.21 C), were tested for 60 min. with the cooling system operated at its maximum cooling capacity. Blood flows were recorded continuously using a computer data acquisition system with a sampling frequency of 250 Hz. Oral, right and left ear temperatures and cooling system parameters were logged manually every 5 min. Arm, leg, chest and rectal temperatures; heart rate; respiration; and an activity index were recorded continuously on a URI Inc. Biolog ambulatory monitor. In general, the male and female subjects' rectal and ear temperature responses to cooling were similar for all vest configurations tested. Oral temperatures during the recovery period were significantly (P<0.05) lower than during the control period, approx.0.2 - 0.5 C, for both men and women wearing any of the four different garments. The corresponding car temperatures were significantly (P<0.05) decreased approx.0.2 - 0.3 C by the end of the recovery period. Compared to the control period, no significant differences were found in rectal temperatures during cooling and recovery periods. These results show that all vest configurations elicit a similar thermal response in both male and female subject groups. However, subject population variance was rather large and may have masked differences between the vests. One vest may prove more effective than another for a given individual, and experience is the only means of determining this.

Ku, Yu-Tsuan E.↗

Automated Derivation of Complex System Constraints from User Requirements

International Space Station (ISS) payload developers submit their payload science requirements for the development of on-board execution timelines. The ISS systems required to execute the payload science operations must be represented as constraints for the execution timeline. Payload developers use a software application, User Requirements Collection (URC), to submit their requirements by selecting a simplified representation of ISS system constraints. To fully represent the complex ISS systems, the constraints require a level of detail that is beyond the insight of the payload developer. To provide the complex representation of the ISS system constraints, HOSC operations personnel, specifically the Payload Activity Requirements Coordinators (PARC), manually translate the payload developers simplified constraints into detailed ISS system constraints used for scheduling the payload activities in the Consolidated Planning System (CPS). This paper describes the implementation for a software application, User Requirements Integration (URI), developed to automate the manual ISS constraint translation process.

Muery, Kim↗

Success Factors in Human Space Programs - Why Did Apollo Succeed Better Than Later Programs?

The Apollo Program reached the moon, but the Constellation Program (CxP) that planned to return to the moon and go on to Mars was cancelled. Apollo is NASA's greatest achievement but its success is poorly understood. The usual explanation is that President Kennedy announced we were going to the moon, the scientific community and the public strongly supported it, and Congress provided the necessary funding. This is partially incorrect and does not actually explain Apollo's success. The scientific community and the public did not support Apollo. Like Apollo, Constellation was announced by a president and funded by Congress, with elements that continued on even after it was cancelled. Two other factors account for Apollo's success. Initially, the surprise event of Uri Gagarin's first human space flight created political distress and a strong desire for the government to dramatically demonstrate American space capability. Options were considered and Apollo was found to be most effective and technically feasible. Political necessity overrode both the lack of popular and scientific support and the extremely high cost and risk. Other NASA human space programs were either canceled, such as the Space Exploration Initiative (SEI), repeatedly threatened with cancellation, such as International Space Station (ISS), or terminated while still operational, such as the space shuttle and even Apollo itself. Large crash programs such as Apollo are initiated and continued if and only if urgent political necessity produces the necessary political will. They succeed if and only if they are technically feasible within the provided resources. Future human space missions will probably require gradual step-by-step development in a more normal environment.

Success factors↗

Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages

The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson’s correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.

Dumas, Melissa↗

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗