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Results for “capacity factor data”

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

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. Further, an optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g -1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg -1 S) and high S-loading cathodes (>4 mg cm -2 ) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg -1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

25 ENERGY STORAGE↗
DOE OSTI2025

Environmental factors have a greater influence on photosynthetic capacity in C 4 plants than biochemical subtypes or growth forms

Our understanding of how photosynthetic capacity varies among C 4 species and across growth and measurement conditions remains limited. We collated 1696 CO 2 response curves of net CO 2 assimilation rate (A/C i curves) from C 4 species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (V pmaxA ) and CO 2 -saturated net photosynthetic rate (A max ), two key parameters describing photosynthetic capacity. We examined how V pmaxA and A max vary with species-specific traits, growth and measurement conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Abstract Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique. This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Efficient Removal of Lead Ions from Aqueous Media Using Sustainable Sources Based on Marine Algae

The goal of this project is to explore a new method to efficiently remove Pb(II) ions from water by processing Undaria pinnatifida into immobilized beads using sodium alginate and calcium chloride. The resulting biosorbent was characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Using immobilized U. pinnatifida, we investigated the effect of various factors on Pb(II) ion removal efficiency such as temperature, pH, ionic strength, time, and underlying biosorption mechanisms. For Pb(II) ion biosorption studies, Pb(II) ion biosorption data were obtained and analyzed using Langmuir and Freundlich adsorption models. It is found that the maximum Pb(II) ion adsorption capacity (X m ) of U. pinnatifida was estimated to be ~5 times greater than that of activated carbon, indicating the superior Pb(II) ion removal capability of U. pinnatifida compared to activated carbon. In addition, a thermodynamic study indicates that biosorption processes are found to be endothermic and an increase in the Pb(II) ion solution temperature provides a more preferential reaction toward Pb(II) ion biosorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Antitumor CD8 T cell responses in glioma patients are effectively suppressed by T follicular regulatory cells

Regulatory T (Treg) cells are thought to contribute to tumor pathogenesis by suppressing tumor immunosurveillance and antitumor immunity. T follicular regulatory (Tfr) cells are a recently characterized Treg subset that expresses both the Treg transcription factor (TF) Foxp3 and the T follicular helper (Tfh) TF Bcl-6. The role of Tfr cells in glioma patients remains unclear. In this study, we found that the level of Tfr cells, identified as Foxp3{sup +}Bcl-6{sup +} CD4 T cells, was significantly elevated in tumor-infiltrating CD4 T cells from resected glioma tumors. Both Tfr cells and Treg cells significantly suppressed the proliferation and the cytotoxic capacity of CD8 T cells toward glioma tumor cells, and the suppression was positively associated with the proportion of Tfr cells and Treg cells, respectively. Tfr and Treg cells from glioma tumor samples demonstrated higher suppression potency than those from healthy blood samples and glioma blood samples. Interestingly, canonical CXCR5{sup -} Treg cells could suppress both CXCR5{sup +} and CXCR5{sup -} CD8 T cells, albeit with stronger potency toward CXCR5{sup -} CD8 T cells. However, Tfr cells presented much higher suppression potency toward CXCR5{sup +} CD8 T cells, whereas CXCR5{sup +} CD8 T cells are a potent CD8 T cell subset previously described to have antiviral and antitumor roles. Overall, these data indicate that Tfr cells are enriched in glioma tumors and have suppressive capacity toward CD8 T cell-mediated effector functions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms

Abstract Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax , supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pore characterization of the Marcellus Shale by nitrogen adsorption and prediction of its gas storage capacity

In a shale gas reservoir, pore characterization is an important factor used to determine gas storage capacity. However, the nanometer (nm)-scale pore system in shale is difficult to explore by traditional optical, scanning electron microscopy, or even nuclear magnetic resonance well logging. We have investigated the pore structure and storage capacity of the Marcellus Shale through integration of petrophysical analysis from laboratory and well-logging data and nitrogen adsorption. The isotherm of Marcellus Shale is a composite isotherm, which has features of type I, type II, and type IV isotherms with type H4 of the hysteresis loop, suggesting slit-like pores developed in the Marcellus Shale. Quantitative analysis of pore volumes from the nitrogen adsorption indicates that density porosity may be more properly used to approximate the shale porosity and estimate the shale gas volume. In addition, the specific surface area, micropore, and mesopore volumes have a positive relationship with the kerogen volume and total organic content (TOC). By using the Langmuir and Brunauer-Emmet-Teller models, the simulated result indicates that the higher adsorbed quantity of the Marcellus Shale could be the result of the increase of micropore volume contributed, by the increase of kerogen or TOC content. The proposed equations rapidly compute TOC, a key parameter to predict gas storage capacity in overmature shale such as the Marcellus Shale.

Geochemistry & Geophysics↗
DOE OSTI2020

Drug Resistance in Filarial Parasites Does Not Affect Mosquito Vectorial Capacity

Parasite drug resistance presents a major obstacle to controlling and eliminating vector-borne diseases affecting humans and animals. While vector-borne disease dynamics are affected by factors related to parasite, vertebrate host and vector, research on drug resistance in filarial parasites has primarily focused on the parasite and vertebrate host, rather than the mosquito. However, we expect that the physiological costs associated with drug resistance would reduce the fitness of drug-resistant vs. drug-susceptible parasites in the mosquito wherein parasites are not exposed to drugs. Here we test this hypothesis using four isolates of the dog heartworm (Dirofilaria immitis)—two drug susceptible and two drug resistant—and two vectors—the yellow fever mosquito (Aedes aegypti) and the Asian tiger mosquito (Ae. albopictus)—as our model system. Our data indicated that while vector species had a significant effect on vectorial capacity, there was no significant difference in the vectorial capacity of mosquitoes infected with drug-resistant vs. drug-susceptible parasites. Consequently, contrary to expectations, our data indicate that drug resistance in D. immitis does not appear to reduce the transmission efficiency of these parasites, and thus the spread of drug-resistant parasites in the vertebrate population is unlikely to be mitigated by reduced fitness in the mosquito vector.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Utility-Scale Solar, 2023 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2023 Edition” presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC (PV plants of 5 MWAC or less, including residential rooftop systems, are covered separately in Berkeley Lab’s companion annual report, Tracking the Sun). Highlights of this year’s update include: -10.4 GWAC of new utility-scale PV capacity came online in 2022, bringing cumulative installed capacity to more than 61.7 GWAC across 46 states. -94% of all new utility-scale PV capacity added in 2022 uses single-axis tracking. -Median installed project costs declined to $\$1.32$/WAC (or $\$1.07$/WDC) in 2022. -Plant-level capacity factors vary widely, from 9% to 35% (on an AC basis), with a sample median of 24%. The report explores drivers of this variation. -Utility-scale PV’s LCOE fell to $\$39$/MWh in 2022 ($\$29$/MWh if factoring in the federal investment tax credit, or ITC). -PPA prices have largely followed the decline in solar’s LCOE over time, but have recently stagnated and even moved slightly higher. Prices from a sample of recent contracts average around $\$20-30$/MWh (levelized) in the West and $\$30-40$/MWh elsewhere in the continental US. -In 2022, solar’s average market value (defined in the report to include only energy and capacity value) rose by 40% to $\$71$/MWh and exceeded average wholesale prices in 4 of the 7 ISOs/RTOs and 11 of 18 other balancing authorities analyzed. -Adding battery storage is one way to increase the value of solar. Our public data file tracks metadata and PPA prices from ~100 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -the end of 2022, there were at least 947 GW of utility-scale solar power capacity within the interconnection queues across the nation, 456 GW of which include batteries. For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Renewable Energy Potential Model: Hawaii Geothermal Supply Curves

This dataset extends the development of the Renewable Energy Potential (reV) model to include geothermal energy, with a specific focus on Hawaii. Provided here are the results of two scenarios that were modeled for geothermal energy in Hawaii: binary enhanced geothermal systems (EGS) at a depth of 2.5 km and hydrothermal binary systems at a depth of 1.5 km. The resource data for both scenarios were derived from Lautze and Haskins (2024) using an exponential method. The PFA probability of heat map was used as a look up table for which temperature gradient to use (Lautze and Haskins, 2024). The dataset provides geospatial and techno-economic details for evaluating geothermal energy potential. It includes spatial coordinates, estimated capacity factors, developable area, resource potential, and annual energy production metrics. Economic details such as levelized cost of electricity (LCOE), site development costs, transmission costs, and fixed-charge rates are also included. The reV model, originally developed for wind and solar energy, incorporates these variables to evaluate deployment constraints related to land use, environmental and cultural factors, and grid integration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

The role of moisture in MgCl 2 salt: A multiscale approach to TES performance

This study presents a comprehensive multiscale analysis to evaluate the influence of moisture on the thermal performance of thermal energy storage systems using magnesium chloride (MgCl 2 ) as the phase-changing material. The system uses graphite foam with 90% relative density to enhance thermal conductivity. The analysis includes thermal conductivity calculations and specific heat capacity for different hydrate phases of MgCl 2 : Anhydrous, Mono, Di, Tetra, and Hexa. These properties were evaluated for the first time using the phonon density of states from Density Functional Theory simulations. Results showed that thermal conductivity decreased, while specific heat capacity increased by a factor of two as the phase changed from anhydrous to hexahydrate. Meso-scale models were created to account for the anisotropy of graphite foam and property variations of the MgCl 2 hydrate phases. Asymptotic Expansion homogenization simulations determined the anisotropic thermal conductivity for all phases. This unique methodology improved simulation accuracy, which matched experimental data for anhydrous MgCl 2 . A parametric study examined various operating conditions and their effect on TES performance. It revealed that higher charging temperatures did not enhance exergy efficiency, but increased discharge mass flow rates improved it due to better heat transfer. Thermal performance evaluated by the exergy efficiency remained consistent across hydrate systems under the tested conditions. Furthermore, the study suggests that this uniformity is linked to missing key data, particularly latent heat and melting point for different hydrates. Overall, it highlights the importance of multiscale effects and accurate material properties in designing and optimizing TES systems.

Molten salt degradation↗
DOE OSTI2021

Ecological generalism drives hyperdiversity of secondary metabolite gene clusters in xylarialean endophytes

Although secondary metabolites are typically associated with competitive or pathogenic interactions, the high bioactivity of endophytic fungi in the Xylariales, coupled with their abundance and broad host ranges spanning all lineages of land plants and lichens, suggests that enhanced secondary metabolism might facilitate symbioses with phylogenetically diverse hosts. Here, we examined secondary metabolite gene clusters (SMGCs) across 96 Xylariales genomes in two clades (Xylariaceae s.l. and Hypoxylaceae), including 88 newly sequenced genomes of endophytes and closely related saprotrophs and pathogens. We paired genomic data with extensive metadata on endophyte hosts and substrates, enabling us to examine genomic factors related to the breadth of symbiotic interactions and ecological roles. All genomes contain hyperabundant SMGCs; however, Xylariaceae have increased numbers of gene duplications, horizontal gene transfers (HGTs) and SMGCs. Enhanced metabolic diversity of endophytes is associated with a greater diversity of hosts and increased capacity for lignocellulose decomposition. Our results suggest that, as host and substrate generalists, Xylariaceae endophytes experience greater selection to diversify SMGCs compared with more ecologically specialised Hypoxylaceae species. Altogether, our results provide new evidence that SMGCs may facilitate symbiosis with phylogenetically diverse hosts, highlighting the importance of microbial symbioses to drive fungal metabolic diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗
DOE OSTI2021

Geothermal Representation in Power System Models

Power system models generally fail to capture the range of characteristics geothermal resources provide and the value they potentially contribute to decarbonization and reliability of future electricity grids as firm, dispatchable, non-combustion power resources. This study reviews the results of power system modeling efforts to investigate geothermal deployment potential in the United States, including the U.S. DOE GeoVision analysis and ongoing modeling and analysis efforts to support planning and development of future grids with 100% renewable energy in California. Several themes are identified that could be implemented immediately to improve the accuracy of geothermal representation in power system models: consistency of model inputs, modeling of baseload and dispatchable geothermal resources, accurate valuation of grid services, improved representation of capacity factor, use of contemporary LCOE estimates, improved understanding of the evolution of geothermal value, and use of accurate resource potential constraints. Many of the models reviewed produced significantly different amounts of geothermal resource selection - even when modeling the same region and time period. This highlights the variability of inputs and assumptions among models, so creating a consistent set of geothermal inputs is a first step toward more accurate representation of geothermal in models. Research opportunities are identified that could help improve geothermal data inputs in modeling efforts, including analyses of historical data, sensitivity to model inputs, and comparative value of geothermal generators as baseload or dispatchable resources. Outcomes of such research can inform the geothermal community about how best to guide geothermal development toward wider deployment in support of future electricity grids through improved understanding of the evolution of geothermal value over time and the characteristics that contribute to that value.

capacity expansion models↗
DOE OSTI2026

Pressure-Stabilized MnSb2 with Complex Incommensurate Magnetic Order

Marcasite-type compounds have been proposed as promising hosts of exotic magnetic quantum states, yet experimental realizations in stoichiometric, disorder-free systems remain limited. Here, we report the high-pressure stabilization and magnetic characterization of MnSb2, a marcasite-type compound that is thermodynamically metastable under ambient pressure. Single crystals were synthesized using a cubic multianvil press at 3.3 GPa and 490 °C for 24 h, and powder and single-crystal X-ray diffraction confirm the orthorhombic Pnnm structure. These crystals are stable at ambient pressure for a long time up to between 450 and 500 K. Heat-capacity measurements reveal phase transitions at approximately T0 ∼ 118 K and T1 ∼ 220 K. Neutron diffraction uncovers an unconventional magnetic state below T1 ∼ 220 K. Magnetic powder neutron diffraction refinements reveal possible multiple magnetic configurations that provide comparably acceptable fits to the experimental data. While most solutions are consistent with a spin-density-wave (SDW) description, helical models systematically yield inferior agreement factors. Across a broad range of models, the Mn ordered moment reaches a maximum value of approximately 2 μB and remains predominantly collinear, with minimal canting along the c-axis. At 200 K, the magnetic propagation vector is q = (0, 0.3975, 0.3783); upon cooling, the b component increases toward 0.5, reflecting a temperature-dependent evolution of the modulation. The need for modification of the magnetic model between high and low temperatures further highlights the complex and strongly temperature-dependent nature of the magnetic order in this system. These results establish MnSb2 as a pressure-stabilized marcasite magnet with a tunable, complex magnetic state and a compelling stoichiometric platform for exploring unconventional magnetic behavior, including potential altermagnetism.

Xu, Mingyu [Michigan State University , , , ,; Iow↗
DOE OSTI2023

A General Method for Estimating Zonal Transmission Interface Limits from Nodal Network Data: Preprint

Capacity expansion models for the electric power system often employ zonal (rather than nodal) resolution, necessitating estimates of aggregate power transfer limits across the interfaces between model zones. Interface limits between planning areas are sometimes published, but they are not generalizable to arbitrary zone shapes. There is thus a need for a reproducible method for estimating interface transfer limits (ITLs) between user-defined zones directly from nodal transmission system data. Here, we present a simple method for estimating ITLs using a DC power flow approximation via the power transfer distribution factor (PTDF) matrix. Linear optimization is performed to identify the distribution of power flows that maximizes the total flow on interface-crossing lines, subject to individual line ratings, limits on bus injection/withdrawal, and the relationships among flows, injections, and withdrawals imposed by the PTDF matrix. We demonstrate the application of the method on a 134-zone ~65000-bus system, and we explore the influence of flow direction, contingency level, and zone size on the estimated ITLs. There is significant heterogeneity in the ratio of the ITL to the sum of interface-crossing line ratings, which highlights the importance of accounting for the physical constraints on power flows imposed by Kirchhoff's laws when estimating zonal ITLs.

17 WIND ENERGY↗