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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Optimization of distributed compute resources utilization in the CMS Global Pool

The CMS Submission Infrastructure is the primary system for managing computing resources for CMS workflows, including data processing, simulation, and analysis. It integrates geographically distributed resources from Grid, HPC, and cloud providers into federated pools managed by HTCondor and Glidein- WMS, for a total of around 500k CPU cores. This system dynamically manages workloads based on priorities defined by the collaboration. Additionally, CMS scheduling strategies must be flexible to handle multiple concurrent workloads while considering changing processing demands and resource availability from various providers.Efficient utilization of vast amounts of distributed compute resources is a key element for the success of the scientific programs of the LHC experiments. Optimizing the system is essential to maximize resource efficiency and fully utilize the distributed computing power. The CMS Submission Infrastructure team thus systematically investigates sources of inefficiency in workload scheduling to reduce their impact. In addition, a strategy of pilot overloading has been introduced to compensate for other inefficiency sources, thereby optimizing resource utilization and enhancing computational throughput.

Mascheroni, Marco [UC, San Diego (main)]↗
DOE OSTI2024

Machine learning enables identification of an alternative yeast galactose utilization pathway

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype–phenotype map, and their application will uncover novel biology, even in well-studied traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unraveling the influence of microbial necromass on subsurface microbiomes: metabolite utilization and community dynamics

The role of microbial necromass (nonliving microbial biomass), a significant component of belowground organic carbon, in nutrient cycling and its impact on the dynamics of microbial communities in subsurface systems remains poorly understood. It is currently unclear whether necromass metabolites from various microbes are different, whether certain groups of metabolites are preferentially utilized over others, or whether different microbial species respond to various necromass metabolites. In this study, we aimed to fill these knowledge gaps by designing enrichments with necromass as the sole nutrient source for subsurface microbial communities. We used the soluble fraction of necromass from bacterial isolates belonging to Arthrobacter, Agrobacterium, and Pseudomonas genera, and our results indicate that metabolite composition of necromass varied slightly across different strains but generally included amino acids, organic acids, and nucleic acid constituents. Arthrobacter-derived necromass appeared more recalcitrant. Necromass metabolites enriched diverse microbial genera, particularly Massilia sp. responded quickly regardless of the necromass source. Despite differences in necromass utilization, microbial community composition converged rapidly over time across the three different necromass amendments. Uracil, xanthine, valine, and phosphate-containing isomers were generally depleted over time, indicating microbial assimilation for maintenance and growth. However, numerous easily assimilable metabolites were not significantly depleted, suggesting efficient necromass recycling and the potential for necromass stabilization in systems. This study highlights the dynamic interactions between microbial necromass metabolites and subsurface microbial communities, revealing both selective utilization and rapid community and necromass convergence regardless of the necromass source.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Comparative transcriptomics elucidates adaptive phenol tolerance and utilization in lipid-accumulating Rhodococcus opacus PD630

Lignin-derived (e.g. phenolic) compounds can compromise the bioconversion of lignocellulosic biomass to fuels and chemicals due to their toxicity and recalcitrance. The lipid-accumulating bacterium Rhodococcus opacus PD630 has recently emerged as a promising microbial host for lignocellulose conversion to value-added products due to its natural ability to tolerate and utilize phenolics. To gain a better understanding of its phenolic tolerance and utilization mechanisms, we adaptively evolved R. opacus over 40 passages using phenol as its sole carbon source (up to 373% growth improvement over wild-type), and extensively characterized two strains from passages 33 and 40. The two adapted strains showed higher phenol consumption rates (~20 mg/l/h) and ~2-fold higher lipid production from phenol than the wild-type strain.Whole-genome sequencing and comparative transcriptomics identified highly-upregulated degradation pathways and putative transporters for phenol in both adapted strains, highlighting the important linkage between mechanisms of regulated phenol uptake, utilization, and evolved tolerance. Our study shows that the R. opacus mutants are likely to use their transporters to import phenol rather than export them, suggesting a new aromatic tolerance mechanism. The identified tolerance genes and pathways are promising candidates for future metabolic engineering in R. opacus for improved lignin conversion to lipid-based products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Network bandwidth utilization forecast model on high bandwidth networks

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Distributed Energy Resource-Cognizant Upgrade Paths to the Traditional Restoration Strategy of Utilities for Improved Load Restoration

Climate change has resulted in increasingly impactful and more frequent occurrences of extreme weather events. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks under fault scenarios; however, currently, utilities deploying the automated fault location, isolation and restoration (FLISR) function in their advanced distribution management system (ADMS) do not take into account the available generation and load-modification capabilities of distributed energy resources present in the disconnected network due to an upstream isolated fault. This results in the network reconfiguration and restoration to result in sub-optimal load restoration. Therefore, this paper presents two approaches that can upgrade the existing FLISR capabilities of distribution utilities to significantly increase the restoration of critical loads. The performance of the proposed approaches is evaluated on a numerical model of a real distribution feeder in Georgia, USA.

DER↗
DOE OSTI2021

Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping

Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers.

clustering↗
DOE OSTI2018

Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies

Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have realworld significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have high real-world utility, while ignoring the low-utility statistically anomalous instances. To this end, we propose a novel method called Latent Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution. This method uses the EM algorithm to simultaneously incorporate the Geometric Entropy Minimization principle for identifying statistical anomalies, and the Maximum Entropy Discrimination principle to incorporate utility labels, in order to detect highutility anomalies. Here, we apply our method in both simulated and real datasets to demonstrate that it has superior performance over existing alternatives that independently pre-process with unsupervised anomaly detection algorithms before classifying.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Coordinated Optimization of Multiservice Dispatch for Energy Storage Systems with Degradation Model for Utility Applications

The rapid development of energy storage technologies is motivating researchers to understand the possible value of energy storage. This paper evaluates the multiservice dispatch of distribution-sited energy storage systems with realistic degradation models by integrating a multiservice optimization model with a distribution system simulator. This work attempts to define the theoretical value energy storage can bring when multiple value streams are considered in the modeling environment. Use cases presented in this paper deliver multiple services to both distribution and transmission stakeholders that can provide positive net value to the electricity system under prevailing energy storage cost structures. Furthermore, this work was performed in collaboration with a distribution utility to help utility personnel understand the value distributed energy storage can provide by examining different utility use cases without considering implementation costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Geochemical, metagenomic and metaproteomic insights into trace metal utilization by methane-oxidizing microbial consortia in sulfidic marine sediments

Microbes have obligate requirements for trace metals in metalloenzymes that catalyze important biogeochemical reactions. In anoxic methane- and sulfide-rich environments, microbes may have unique adaptations for metal acquisition and utilization due to decreased bioavailability as a result of metal sulfide precipitation. However, micronutrient cycling is largely unexplored in cold ( 10 C) and sulfidic (>1 mM H2S) deep-sea methane seep ecosystems. We investigated trace metal geochemistry and microbial metal utilization in methane seeps offshore Oregon and California, USA, and report dissolved concentrations of nickel (0.5-270 nM), cobalt (0.5-6 nM), molybdenum (10-5,600 nM) and tungsten (0.3-8 nM) in Hydrate Ridge sediment porewaters. Despite low levels of cobalt and tungsten, metagenomic and metaproteomic data suggest that microbial consortia catalyzing anaerobic oxidation of methane utilize both scarce micronutrients in addition to nickel and molybdenum. Genetic machinery for cobalt-containing vitamin B12 biosynthesis was present in both anaerobic methanotrophic archaea (ANME) and sulfate-reducing bacteria (SRB). Proteins affiliated with the tungsten-containing form of formylmethanofuran dehydrogenase were expressed in ANME from two seep ecosystems, the first evidence for expression of a tungstoenzyme in psychrotolerant microorganisms. Finally, our data suggest that chemical speciation of metals in highly sulfidic porewaters may exert a stronger influence on microbial bioavailability than total concentration

Glass, DR. Jennifer↗
DOE OSTI2019

Interactive effects of dietary fishmeal level and plant essential oils supplementation on European sea bass, Dicentrarchus labrax : Growth performance, nutrient utilization, and immunological response

Abstract The inclusion of phytogenics in fish feed is a promising strategy to compensate for the negative performance effects of replacing fishmeal (FM) with vegetable sources. The present work assessed the interactive effects of different dietary FM levels (22.5 and 10% of formulation) and the supplementation of a commercial blend of anise, citrus, and oregano essential oils (Digestarom PEP M.G.E 150) on European sea bass, Dicentrarchus labrax growth performance, nutrient utilization, gut morphology, antioxidant status, and immunological response over a 60‐day growth trial. Results showed decreased growth and protein efficiency ratio and increased feed conversion ratio with a low dietary FM level. In contrast, supplementation of the phytogenic product demonstrated improved performance and nutrient utilization together with increased protein and energy retention. Supplementation with the plant essential oils fully compensated for the negative intestinal changes observed in sea bass fed a low‐FM diet but showed little improvement in fish immunological response, except for the 30% increase in lysozyme activity observed in fish fed the low FM‐supplemented diet compared to those fed the standard high‐FM diet. Overall, this study supports the use of this phytogenic product in low‐FM diets as a possible tool to decrease feed costs associated with FM without compromising fish performance, nutrient utilization, and health.

Gonçalves, Rui A.↗
DOE OSTI2023

Simultaneous carbon catabolite repression governs sugar and aromatic co-utilization in Pseudomonas putida M2

ABSTRACT Pseudomonas putida have emerged as promising biocatalysts for the conversion of sugars and aromatic compounds obtained from lignocellulosic biomass. Understanding the role of carbon catabolite repression (CCR) in these strains is critical to optimize biomass conversion to fuels and chemicals. The CCR functioning in P. putida M2, a strain capable of consuming both hexose and pentose sugars as well as aromatic compounds, was investigated by cultivation experiments, proteomics, and CRISPRi-based gene repression. Strain M2 co-utilized sugars and aromatic compounds simultaneously; however, during cultivation with glucose and aromatic compounds ( p- coumarate and ferulate) mixture, intermediates (4-hydroxybenzoate and vanillate) accumulated, and substrate consumption was incomplete. In contrast, xylose-aromatic consumption resulted in transient intermediate accumulation and complete aromatic consumption, while xylose was incompletely consumed. Proteomics analysis revealed that glucose exerted stronger repression than xylose on the aromatic catabolic proteins. Key glucose (Eda) and xylose (XylX) catabolic proteins were also identified at lower abundance during cultivation with aromatic compounds implying simultaneous catabolite repression by sugars and aromatic compounds. Reduction of crc expression via CRISPRi led to faster growth and glucose and p -coumarate uptake in the CRISPRi strains compared to the control, while no difference was observed on xylose+ p -coumarate. The increased abundances of Eda and amino acid biosynthesis proteins in the CRISPRi strain further supported these observations. Lastly, small RNAs (sRNAs) sequencing results showed that CrcY and CrcZ homologues levels in M2, previously identified in P. putida strains, were lower under strong CCR (glucose+ p -coumarate) condition compared to when repression was absent ( p -coumarate or glucose only). IMPORTANCE A newly isolated Pseudomonas putida strain, P. putida M2, can utilize both hexose and pentose sugars as well as aromatic compounds making it a promising host for the valorization of lignocellulosic biomass. Pseudomonads have developed a regulatory strategy, carbon catabolite repression, to control the assimilation of carbon sources in the environment. Carbon catabolite repression may impede the simultaneous and complete metabolism of sugars and aromatic compounds present in lignocellulosic biomass and hinder the development of an efficient industrial biocatalyst. This study provides insight into the cellular physiology and proteome during mixed-substrate utilization in P. putida M2. The phenotypic and proteomics results demonstrated simultaneous catabolite repression in the sugar-aromatic mixtures, while the CRISPRi and sRNA sequencing demonstrated the potential role of the crc gene and small RNAs in carbon catabolite repression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Genomics and Transcriptomics Analyses of the Oil-Accumulating Basidiomycete Yeast Trichosporon oleaginosus : Insights into Substrate Utilization and Alternative Evolutionary Trajectories of Fungal Mating Systems

Microbial fermentation of agro-industrial waste holds great potential for reducing the environmental impact associated with the production of lipids for industrial purposes from plant biomass. However, the chemical complexity of many residues currently prevents efficient conversion into lipids, creating a high demand for strains with the ability to utilize all energy-rich components of agricultural residues. Here, we present results of genome and transcriptome analyses of Trichosporon oleaginosus. This oil-accumulating yeast is able to grow on a wide variety of substrates, including pentoses and N-acetylglucosamine, making it an interesting candidate for biotechnological applications. Transcriptomics shows specific changes in gene expression patterns under lipid-accumulating conditions. Furthermore, gene content and expression analyses indicate that T. oleaginosus is well-adapted for the utilization of chitin-rich biomass. We also focused on the T. oleaginosus mating type, because this species is a member of the Tremellomycetes, a group that has been intensively analyzed as a model for the evolution of sexual development, the best-studied member being Cryptococcus neoformans. The structure of the T. oleaginosus mating-type regions differs significantly from that of other Tremellomycetes and reveals a new evolutionary trajectory paradigm. Comparative analysis shows that recruitment of developmental genes to the ancestral tetrapolar mating-type loci occurred independently in the Trichosporon and Cryptococcus lineages, supporting the hypothesis of a trend toward larger mating-type regions in fungi. IMPORTANCE: Finite fossil fuel resources pose sustainability challenges to society and industry. Microbial oils are a sustainable feedstock for biofuel and chemical production that does not compete with food production. We describe genome and transcriptome analyses of the oleaginous yeast Trichosporon oleaginosus, which can accumulate up to 70% of its dry weight as lipids. In contrast to conventional yeasts, this organism not only shows an absence of diauxic effect while fermenting hexoses and pentoses but also effectively utilizes xylose and N-acetylglucosamine, which are building blocks of lignocellulose and chitin, respectively. Transcriptome analysis revealed metabolic networks that govern conversion of xylose or N-acetylglucosamine as well as lipid accumulation. These data form the basis for a targeted strain optimization strategy. Furthermore, analysis of the mating type of T. oleaginosus supports the hypothesis of a trend toward larger mating-type regions in fungi, similar to the evolution of sex chromosomes in animals and plants.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans’ robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%–65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.

carbohydrate metabolism↗
DOE OSTI2022

MEASUR - Manufacturing Energy Assessment Software for Utility Reduction

MEASUR your energy savings with the free DOE MEASUR software The Department of Energy (DOE), with Oak Ridge National Laboratory (ORNL), released version 1.0 of their energy efficiency software tool MEASUR (Manufacturing Energy Assessment Software for Utility Reduction). MEASUR has been available for several years as a beta version, being tested by industry experts and real users, and will continue to be updated and improved in the coming years. It is an integrated suite of tools to aid manufacturers in improving the efficiency of energy systems and equipment within a plant, including motors, pumps, fans, process heating, steam, and compressed air. Additionally, there are modules for wastewater energy analysis and to help perform energy treasure hunts. Several calculators are also included, allowing users to independently perform smaller calculations and analyses (such as estimating pump head, performing a fan traverse analysis, estimating waste heat recovery potential, and cataloging compressed air leaks). The MEASUR modules are based on previous DOE software tools that have been used by industry since the early 2000s (such as MotorMaster, AirMaster+, PSAT, PHAST, and FSAT). The original tools only ran on Windows operating systems, and by Windows 10, most of them were inoperable. DOE started their energy efficiency software tool revitalization effort in 2016, first with PSAT (for pumps), then began to integrate the other tools and expand their functionality and utility. The new MEASUR suite provides an extensively more user-friendly, modern, and versatile set of tools. All the assessment modules and most of the calculators have several visual components and graphs and detailed help text for every user input. To help reach international users, the tool utilizes Google translate and users can easily change unit systems, even converting existing user inputs if desired. The assessment files can be organized within the internal file system and easily shared to other users, regardless of their operating system. The entire suite is free, open-source, and can be downloaded on Windows, Mac, or Linux operating systems.

Accawi, Gina [Oak Ridge National Lab. (ORNL), Oak ↗
DOE OSTI2022

Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

California Consumers’ Beliefs and Trust in Electric Utilities

The authors use survey and interview data from California homeowners to understand customers’ trust in their utility company. The authors find that customers’ beliefs about the reliability of electricity do not predict trust. Instead, what matters are beliefs that the utility company does a bad job managing customer service, costs, and wildfires. Distrust among survey respondents aligns with interview participants’ descriptions of utility companies as powerful, greedy monopolies that benefit themselves at the expense of helpless consumers. These results have implications for the energy transition and, more generally, understanding potential challenges experienced by technocratic institutions.

Horne, Christine↗