Field Testing the Hybrid Mobile Survey Method for Tidal Energy Resource Assessments
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Haas, Kevin A [Georgia Institute of Technology]↗
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Distribution System Congestion Management - A Survey of Reliable Integration for Aggregated Resources and Microgrids
Rising penetration of consumer-owned Distribution Grid Resources (DGRs), increasingly managed by third party aggregators and enrolled in grid services and wholesale market programs, can create localized congestion in distribution networks. Managing these constraints is challenging due to a persistent coordination and information gap: utilities are accountable for reliability and have network topology and state visibility, while aggregators control the DGR capability needed to relieve congestion. This survey synthesizes congestion management solutions for distribution systems with high DGR penetration, covering both market-based mechanisms (distribution level markets, locational pricing, flexibility auctions) and non-market-based solutions (network reconfiguration, direct DGR control, demand response, curtailment, etc.). The literature is organized across three decision horizons: long term planning, operational planning, and real-time operation. Special attention is devoted to emerging distribution system operator architectures and coordination frameworks spanning transmission system operators, aggregators, and microgrids. Drawing on recent case studies and implementations, we distill best practices, identify key technical and economic barriers, and outline research directions. The evidence points to a shift toward integrated congestion management that combines market signals with technical controls, enabled by improved monitoring, forecasting, and closed loop control capabilities.
Active Distribution Networks (ADN)↗
Empowering Women in Biomedical Informatics: Pivoting for Success in the Post-Pandemic World
The COVID-19 pandemic and the isolation measures implemented to contain its spread have exacerbated pre-existing barriers to advancement for women in biomedical informatics. In 2020, the Women in AMIA (WIA) Networking, Mentoring, and Lifecycle Committee administered a survey to identify resources and needs to assist women in informatics. Part of this survey identified several factors impacted by the pandemic: 1) work-life boundaries disappeared and caring for family while working from home became increasingly challenging; 2) isolation, stress and safety of well-being was heightened during the pandemic impacting physical and mental health; 3) diverse working environment and modality brought unforeseen challenges in collaborations and unified communications. These challenges present an opportunity for promoting career development for women professionals in biomedical informatics. This WIA-sponsored panel brings together leaders in the field to highlight their personal experiences and strategies to support women professionals.
Dua, Prerna↗
EVALUATING THE HYBRID MOBILE SURVEY METHOD (HMS) FOR TIDAL ENERGY RESOURCE ASSESSMENTS
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Kryl, Michelle [Georgia Technology Institute]↗
Real-World Subsynchronous Oscillation Events in Power Grids with High Penetrations of Inverter-Based Resources
This paper presents a survey of real-world sub-synchronous oscillation events associated with inverter-based resources (IBR) over the past decade. The focus is on those oscillations in the subsynchronous frequency range known to be influenced by power grid characteristics, e.g., series compensation or low system strength. A brief overview of the historical events is presented followed by detailed descriptions of a series of events. This paper also examines causation mechanisms and proposes future research directions to meet grid needs worldwide.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Survey and Assessment of the Ocean Renewable Energy Resources in the US Gulf of Mexico
This study was conducted by the National Renewable Energy Laboratory (NREL) and funded by the Bureau of Ocean Energy Management (BOEM). It provides a comprehensive feasibility assessment of multiple offshore renewable energy technologies in the Gulf of Mexico (GoM) to inform BOEM's strategic plans related to possible Outer Continental Shelf alternative energy leasing activities in the GoM. In coordination with Gulf Coast states, the study includes some information on offshore renewable energy potential in state waters for future energy planning. The goal of the study is to survey potential offshore renewable energy sources in the GoM and quantify their feasibility relating to resource adequacy, technology maturity, and the potential for competitive cost. The study provides a review of available technologies and concepts for generating offshore renewable energy, including a high-level assessment of the current state of each technology and its potential for future advances. It provides a breakdown of resource capacity for each renewable energy technology and a recommendation that offshore wind be pursued for future study as the most promising technology. The renewable technologies that were considered include offshore wind, wave energy, tidal energy, ocean current energy, offshore solar energy, ocean thermal energy conversion (OTEC), cold water source cooling, and hydrogen (as a storage medium to utilize existing pipeline infrastructure). The resource capacity for each of these renewable energy sources was quantified for both the gross resource capacity potential (gross resource) and the technical resource capacity potential (technical resource) using the methodology described in an earlier NREL report. Many of these sources are very immature from a commercial perspective, which makes some of the comparisons difficult. In many cases, new methods were developed to estimate nominal power density for each technology type, which were necessary to convert the resource areas into deployable gross and technical resource capacity potentials.
17 WIND ENERGY↗
Evaluating cosmological biases using photometric redshifts for Type Ia Supernova cosmology with the Dark Energy Survey Supernova Program
Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance–redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here, we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z estimates. Using the DES 5-yr photometrically classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3 × 2 point analyses. With detailed catalogue-level simulations of the DES 5-yr sample, we find that the simulated w can be recovered within ±0.02 when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within ~1σ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses.
(cosmology:) dark energy↗
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates
Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey's 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score < 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret.
15 GEOTHERMAL ENERGY↗
Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment
Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections.
59 BASIC BIOLOGICAL SCIENCES↗
CY2019 Annual Closure Monitoring Report for Corrective Action Unit 98, Frenchman Flat, Underground Test Area, Nevada National Security Site, Nevada (January 2019–December 2019), Revision 1
Three types of monitoring are performed for CAU 98: water quality, water level, and institutional control. These are monitored to determine whether the URs remain protective of human health and the environment, and to ensure that the regulatory boundary objectives are being met. Monitoring data will be used in the future, once multiple years of data are available, to evaluate consistency with the groundwater flow and contaminant transport models because the contaminant boundaries calculated with the models are the primary basis of the UR boundaries. Six wells were sampled for water-quality monitoring in 2019. Contaminants of concern were detected only in the two source/plume wells already known to contain contamination as a result of a radionuclide migration experiment. Tritium concentrations in both of these wells, RNM-2S and UE-5n, remain above the Safe Drinking Water Act maximum contaminant level of 20,000 picocuries per liter but declined in 2019 as compared to measurements in 2018. All other contaminants of concern are below the minimum detection level plus analytical error. The water-level monitoring network includes 16 wells. Depth to water measured in 2019 is generally consistent with recent measurements for all wells. Many wells continue to exhibit a long-term downward trend in water level, though changes from 2018 to 2019 are minimal. The sharp 2016 decline in water level in Well ER-5-3-2 remains unexplained, with the lower level persisting through 2019. Rising water-level trends continue to be observed in Well ER-5-3 deep piezometer and former water supply Well WW-5A. Water supply Well WW-5B experienced a rise in water level as a result of an absence of pumping in the first part of the year (due to a mechanical problem), whereas water levels declined in WW-4 and WW-4A in response to greater pumping in 2019. Institutional control monitoring confirmed the URs are recorded in U.S. Department of Energy and U.S. Air Force land management systems, and that no activities within Frenchman Flat basin are occurring that could potentially affect the contaminant boundaries. Survey of groundwater resources in basins surrounding Frenchman Flat similarly identify no current or pending development that would indicate the need to increase monitoring activities or would otherwise cause concern for the closure decision. The URs continue to prevent exposure of the public, workers, and the environment to contaminants of concern by preventing use of potentially contaminated groundwater.
54 ENVIRONMENTAL SCIENCES↗
Computed Tomography Scanning and Geophysical Measurements of the Clinton Formation in Ohio
Computed tomography and multi-sensor core logging of core material from twelve Ohio wells held by the Ohio Department of Natural Resources, Division of Geological Survey to make publicly available core information from the Early Silurian Clinton Formation in Eastern Ohio. Describes data available can be accessed from NETL’s Energy Data eXchange (EDX) online system, https://edx.netl.doe.gov/dataset/ct-and-geophysical-data-of-ohio-clinton-sands.
58 GEOSCIENCES↗
CreelCat, a Catalog of United States Inland Creel and Angler Survey Data
The United States Inland Creel and Angler Survey Catalog (CreelCat) contains a national compilation of angler and creel survey data collected by natural resource management agencies across the United States (including Washington, D.C. and Puerto Rico). These surveys are used to help inform the management of recreational fisheries, by collecting information about anglers including what they are catching and harvesting, the amount of effort they expend, their angling preferences, and demographic information. As of May 1, 2023, CreelCat houses over 14,729 surveys from 33 states, Puerto Rico, and Washington, D.C., comprising 235 data fields across 8 tables. These tables contain 235,015 records of fish catch and harvest metrics, 27,250 angler preference metrics, 14,729 records of survey characteristics, 13,576 records of effort metrics, and 409 records of angler demographics. Though individual creel surveys are often deployed to meet local science and management objectives, creel data aggregated across jurisdictions has the potential to address larger scale research and management needs.
59 BASIC BIOLOGICAL SCIENCES↗
Mixed-precision numerics in scientific applications: survey and perspectives
The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of up to 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this survey, we (1) review progress across diverse scientific domains—fluid dynamics, weather and climate, quantum chemistry, and computational genomics—that have begun adopting mixed-precision strategies; (2) examine state-of-the-art algorithmic techniques such as iterative refinement, splitting and emulation schemes, and adaptive precision solvers; (3) assess their implications for accuracy, performance, and resource utilization; and (4) survey the emerging software ecosystem that enables mixed-precision methods at scale. We conclude with perspectives and recommendations on cross-cutting opportunities, domain-specific challenges, and the role of co-design between application scientists, numerical analysts, and computer scientists. Collectively, this survey underscores that mixed-precision numerics can reshape computational science by aligning algorithms with the evolving landscape of hardware capabilities.
Graphics processing units↗
Computed Tomography Scanning and Petrophysical Measurements of Oriskany Core across Eastern Ohio
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core material from three Ohio wells. These wells are listed below along with their American Petroleum Institute (API) and Ohio Geological Survey (OGS) identification numbers: - New York Central System 1 well (API 34085200170000, OGS Core 855) - Herren Well (API 34099201650000, OGS Core 2914) - Garvin-King Well (API 34121215610000, OGS Core 2939) The primary impetus of this work was to capture a detailed digital representation of the available core from all three wells. The collaboration between the U.S. Department of Energy’s (DOE) NETL and the Ohio Department of Natural Resources, Division of Geological Survey enables other research entities to access information about this potential carbon storage location and its surrounding formations.
58 GEOSCIENCES↗
Hawai‘i National Marine Renewable Energy Center (HINMREC)
The Hawaii National Marine Renewable Energy Center (HINMREC) was established to facilitate the commercialization of wave energy converter (WEC) devices, and to accelerate development and testing of Ocean Thermal Energy Conversion (OTEC) technologies. Housed at the Hawaii Natural Energy Institute (HNEI) of the University of Hawaii (UH), this program started with execution of the contract between the US Department of Energy (DOE) and UH in March 2009, and activities ran through the end of the project in September 2019. The DOE tasked HINMREC with facilitating and accelerating the build-out of the Navy’s Wave Energy Test Site (WETS), the nation’s first grid-connected open water wave energy conversion test facility, located off Marine Corps Base Hawaii (MCBH) in Kaneohe, on the island of Oahu. WETS was expanded from an existing test berth at 30m water depth to include test berths at 60m and 80m water depths. In recognition of the high costs associated with at-sea testing and evaluation of WEC devices, it was deemed imperative that HINMREC seek ways to reduce costs to developers by providing key research support to these early-stage technologies. WETS and HINMREC’s support allowed development of wave energy technology by providing a testing infrastructure that allows technology developers to test, demonstrate and evaluate their WEC devices, and generate data in order to advance WEC designs toward commercial readiness. HINMREC’s roles have included support in the establishment of the full site, including of wave resource characterization and site survey, followed by the essential roles of independent WEC device power performance assessment and environmental monitoring in support of deployed pre-commercial WECs. This DOE-funded work transitioned effectively to continue Navy-funded tasks for OTEC development, and activities at WETS, now under Naval Facilities Engineering Command (NAVFAC) funding. A secondary objective in the establishment of HINMREC was to assist the private sector in moving OTEC systems beyond proof-of-concept to pre-commercialization, primarily focused on system and component engineering, and local and global investigations into the potential environmental impacts of OTEC systems. HINMREC was tasked with maintaining high-resolution models of ocean thermal resources and the potential sustainable power output of OTEC systems. Ongoing tests begun previously under Navy funding at the OTEC Heat Exchangers (HXs) Test Facility at the Natural Energy Laboratory of Hawaii Authority (NELHA), in Kona on Hawaii Island, have been continued to identify cost-effective aluminum alloys for use in OTEC systems operating in the corrosive marine environment.
16 TIDAL AND WAVE POWER↗
Review of Marine Renewable Energy in Integrated Resource Plans
As part of a literature review of reference material for grid applications of marine hydrokinetic energy capture technologies, Pacific Northwest National Laboratory (PNNL) surveyed U.S. electric utility integrated resource plans for the mention or treatment of marine renewable energy technologies, principally wave energy, tidal energy, and offshore wind energy. This review offers a window into utility decision-making and data utilization with regard to these generating technologies. The report also offers perspectives on the relationship between traditional and emerging resource planning paradigms and metrics.
16 TIDAL AND WAVE POWER↗
FOA_2364_CORE-CM_data_FE0032056 (ICPMS and pXRF FINAL data submission)
ICPMS and pXRF data for samples related to the Cherokee Forest City Basin Core CM project headed up by the Kansas Geological Survey. Submission contains all usable pXRF and ICPMS data collected during the project.
Cherokee Forest City Basin; Critical Minerals; Iow↗