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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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This dataset contains plant-level wind power production data from ERCOT, recorded at 15-minute intervals. The source of this data is ERCOT's 60-Day SCED Disclosure Report (product number NP3-965-ER), with timestamps provided in UTC.
This short report provides a time-sensitive update on the 20-year wind resource data set (named "CA20") produced in 2020 by the National Renewable Energy Laboratory (NREL) for the outer continental shelf (OCS) off the coast of California.
Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.
While $dq$ admittance models have shown to be very useful for stability analysis, extracting admittance models of inverter-based resources (IBRs) from the electromagnetic transient (EMT) simulation environment using frequency scans takes time. In this letter, a new perturbation method based on Gaussian pulses in combination with the system identification algorithms shows great promise for parametric $dq$ admittance model extraction. We present the $dq$ admittance model extracting method for a type-4 wind turbine. Challenges in implementing Gaussian pulse excitation are also pointed out. In conclusion, the extracted $dq$ admittance model via the new method shows to have a high matching degree with the measurements obtained from frequency scans.
Processed data for various datasets.
The purpose of the dataset is to provide filtered, averaged buoy data in NetCDF and CSV formats. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.
The purpose of the dataset is to provide filtered, averaged buoy data. The attached Lidar Buoy Data Dictionary provides further details on the various instruments mounted on the buoys, parameters measured by each instrument, and the frequency of data collection.
This dataset contains gridded estimates of total runoff (mm/day); subsurface runoff (mm/day); snow water equivalent (SWE, mm/day); vertical upward water flux of combined surface evaporation, evapotranspiration, and snow sublimation (mm/day); and soil temperature (C) across the pan-Arctic basin from simulations of the Permafrost Water Balance Model (PWBM v4). These data were generated to allow researchers to advance understanding of the changes unfolding in the Arctic's terrestrial water cycle under the influences of climate warming, hydrological cycle intensification, and permafrost thaw. The dataset will enable researchers to address important questions spanning multiple disciplines. For example, how will rising river flows exported to the coast impact critical zone ecosystems, near-shore ice dynamics, and ocean biogeochemical processes. Spatial resolution is 25x25 km with N=39926 grid cells spanning the domain. Three forcing datasets were used to drive simulations at an implicit daily time step over the period 1980-2100. Daily air temperature, precipitation, and wind speed data from the WATCH Forcing Data methodology applied to ERA5 reanalysis (W5E5) were used for meteorological forcings over the period 1980-2019. Simulations for the period 1980-2100 used meteorological data from two global climate models; IPSL-CM6A-LR and MPI-ESM1-2-HR. The PWBM hydrological outputs are organized by grid and month in gzipped ASCII text files within each archived tar file. Soil temperature is organized by grid cell and day of year from DOY 205 to DOY 260. No special software is required to work with these data. The package contains spatial domain and river basin information files. These data are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and soil thermal dynamics. A manuscript describing the model simulations and associated analysis has been accepted for publication in The Cryosphere (Rawlins and Karmalkar, 2024).
Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.
The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different operational and atmospheric conditions and validate the use of different wind plant control strategies and simulation frameworks. An extensive field campaign like AWAKEN requires proper coordination and long-term planning to be successful. In this paper, we review the major activities planned during AWAKEN to provide information for current and future project partners. Specifically, we provide information about the project sites, their planned instruments, and how these will relate to the scientific objectives of the overall AWAKEN project.
Solid-state transformers (SSTs) are a promising solution photovoltaic (PV), wind, traction, data center, battery energy storage system (BESS), and fast charging electric vehicle (EV) applications. The traditional SSTs are typically three-stage, i.e., hard-switching cascaded multilevel rectifiers and inverters with dual active bridge (DAB) converters, which leads to bulky passives, low efficiency, and high electromagnetic interference (EMI). This article proposes a new soft-switching solid-state transformer (S4T). The S4T has full-range zero-voltage switching (ZVS), electrolytic capacitor-less dc link, and controlled dv/dt, which reduces EMI. The S4T comprises two reverse-blocking current-source inverter (CSI) bridges, auxiliary branches for ZVS, and transformer magnetizing inductor as a reduced dc link with 60% ripple. Compared with the prior S4T, an effective change on the leakage inductance diode is made to reduce the number of the devices on the main power path by 20% for significant conduction loss saving and retain the same functionality of damping the resonance between the leakage and resonant capacitors and recycling trapped leakage energy. The conduction loss saving is crucial, being the dominating loss mechanism in SSTs. Importantly, the proposed single-stage SST not only holds the potential for high power density and high efficiency but also has full functionality, e.g., multiport dc loads integration, voltage regulation, and reactive power compensation, unlike the traditional single-stage matrix SST. The S4T can achieve single-stage isolated bidirectional dc–dc, ac–dc, dc–ac, or ac–ac conversion. It can also be configured input-series output-parallel (ISOP) in a modular way for medium-voltage (MV) grids. Hence, the S4T is a promising candidate for the SST. The full functionality, e.g., voltage buck–boost, multiport, etc., and the universality of the S4T for the dc–dc, dc–ac, and ac–ac conversion are verified through the simulations and experiments of two-port and three-port MV prototypes based on 3.3 kV SiC MOSFETs in dc–dc, dc–ac, and ac–ac modes at 2 kV.
High-resolution ice core records from coastal Antarctica are particularly useful to inform our understanding of environmental changes and their drivers. Here, we present a decadally resolved record of sea-salt sodium (a proxy for open-ocean area) and non-sea salt calcium (a proxy for continental dust) from the well-dated Roosevelt Island Climate Evolution (RICE) core, focusing on the time period between 40–26 ka BP. The RICE dust record exhibits an abrupt shift towards a higher mean dust concentration at 32 ka BP. Investigating existing ice-core records, we find this shift is a prominent feature across Antarctica. We propose that this shift is linked to an equatorward displacement of Southern Hemisphere westerly winds. Subsequent to the wind shift, data suggest a weakening of Southern Ocean upwelling and a decline of atmospheric CO 2 to lower glacial values, hence making this shift an important glacial climate event with potentially important insights for future projections.
Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s −1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.
This dataset presents an ensemble of streamflow projections covering the conterminous United States (CONUS), developed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Multiple Coupled Models Intercomparison Project phase 6 (CMIP6) Global Climate Models (GCMs) were downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling methods, based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed data were used to drive two calibrated hydrologic models (VIC and PRMS), with total runoff routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model, producing an ensemble of streamflow projections across 2.7 million NHDPlusV2 stream reaches across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Additionally, using only DBCCA and Daymet, the projections extend to the 2060-2099 far-future period and encompass three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is designed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Kao et al. (2022), Rastogi et al. (2022), and Ghimire et al. (2023).
This preliminary data archive includes meteorological data recorded at the Utah FORGE facility over the period of time including the 16A/16B stimulation activities, largely March/April/May of 2024. This information may prove useful for understanding seismic and other instrumentation response during monitoring. The bulk of relevant stimulation activities occurred between March 25th and April 28th, 2024 and the included files span this period. All sensors were installed at the 78-16 pad and hence reflect weather state at the site. This dataset was acquired by the FOGMORE R&D project (Fiber Optic MOnitoring for Reservoir Evolution), Utah FORGE R&D Project 3-2417.
This dataset contains estimates of dissolved organic carbon (DOC) yield (mg C m-2), runoff (mm day-1), snow water equivalent (SWE, mm day-1), and snowmelt (mm day-1) from a simulation of the Permafrost Water Balance Model (PWBM v4). The simulation effectively quantifies snowpack accumulation and melt, runoff, and DOC leaching, and broadly capture the seasonal cycle in DOC concentration and mass loadings. Daily air temperature, precipitation, and wind speed data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) were used for meteorological forcings. The model outputs are organized by grid and day of the month in gzipped ASCII text files contained within each archived ZIP file. No special software is required to work with these data. The basin and domain information files are also ASCII text. The gridded estimates are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and leachate DOC concentrations and loadings across parts of the western Arctic. A manuscript describing the data and associated analysis has been accepted for publication in Journal of Geophysical Research - Biogeosciences (Rawlins et al., 2021).
This project, funded by DOE Atmospheric System Research (DE-SC0020050), focused on improving the representation of mesoscale convective systems (MCSs) in numerical weather and climate models by leveraging high-resolution observations from the DOE Atmospheric Radiation Measurement (ARM) program. The research aimed to evaluate model sensitivities to grid spacing, microphysics, and planetary boundary layer (PBL) schemes, with a particular emphasis on improving convection parameterization for high-resolution modeling. Findings from this work highlight several key advancements. Model validation against ARM radar wind profiler data from the Southern Great Plains (SGP) and Manaus (MAO) sites revealed systematic biases in simulated convective mass flux profiles, leading to the development of an observationally constrained evaluation framework for diagnosing and improving model performance. Sensitivity analyses demonstrated that the representation of Amazonian MCSs was highly dependent on PBL scheme selection, while mid-latitude MCSs were more strongly influenced by microphysics parameterizations. A series of high-resolution WRF simulations, ranging from 4 km to 125 m grid spacing, provided insight into the behavior of convective drafts across scales. While updraft properties converged at sub-kilometer resolutions, biases in downdraft intensity persisted even at the finest resolution tested, emphasizing the need for further refinements in model physics. Additionally, comparisons of MCS vertical structures between mid-latitude and tropical environments revealed stronger updrafts and larger mass flux in mid-latitude MCSs, providing critical insights for improving climate model representations of storm-scale dynamics. The project’s findings have already contributed to advancing numerical modeling capabilities, particularly in WRF, MPAS, ICON, and DOE’s SCREAM model, by refining how convective processes are represented in high-resolution climate simulations. Results were disseminated through peer-reviewed publications, conference presentations, and ARM/ASR Research Highlights, engaging the broader scientific community. The project also provided valuable training opportunities for two postdoctoral researchers, who played central roles in model development, analysis, and dissemination of results. Their work contributed to several publications and conference presentations, helping prepare them for careers in atmospheric modeling. By improving the simulation of MCSs, this research directly supports the development of next-generation climate models capable of more accurately representing extreme precipitation and convective processes. The insights gained will inform future improvements in convective parameterization and guide the design of high-resolution weather and climate simulations, ultimately enhancing the reliability of climate projections and weather forecasts.
This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).