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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R 2 >0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

17 WIND ENERGY↗
DOE OSTI2024

U.S. Department of Energy Collegiate Wind Competition 2025 Rules - Phase 1

The U.S. Department of Energy (DOE) Wind Energy Technologies Office's (WETO) Collegiate Wind Competition (CWC, also referred to as the "Competition" in this rules document) invites interdisciplinary teams of undergraduate students from a variety of academic programs to solve complex wind energy challenges. Through the competition, WETO intends to offer students direct industry experience, valuable exposure to wind energy career pathways, and greater knowledge of wind energy's potential to contribute to a clean energy future. The competition will select up to 35 teams to start, making them eligible to compete for a cash prize pool of up to $280,000 . Each year, the competition identifies a new challenge and set of activities that address real-world research questions, thus demonstrating skills that students will need to work in the wind or wider renewable energy industries. The Collegiate Wind Competition 2025 challenge requires participants to compete simultaneously in four contests: 1) Turbine Prototype Contest: Design, build, and present a unique, wind-driven power system based on market research; 2) Turbine Testing Contest: Test the wind turbine in a competition wind tunnel at the final event; 3) Project Development Contest: Research wind resource data, transmission infrastructure, and environmental factors to create a site plan and financial analysis for a hypothetical wind farm; and Connection Creation Contest: Partner with wind industry professionals, raise awareness of wind energy in your local community, and work with local media to promote your team's accomplishments. The competition does not prescribe a power system market or wind regime. It is expected that each team will participate in all four contests.

collegiate wind competition↗
DOE OSTI2024

U.S. Department of Energy Collegiate Wind Competition 2025: Rules - Phases 2 and 3

The U.S. Department of Energy (DOE) Wind Energy Technologies Office's (WETO) Collegiate Wind Competition (CWC, also referred to as the "competition" in this rules document) invites interdisciplinary teams of undergraduate students from a variety of academic programs to solve complex wind energy challenges. Through the competition, WETO intends to offer students direct industry experience, valuable exposure to wind energy career pathways, and greater knowledge of wind energy's potential to contribute to a clean energy future. The competition will select up to 35 teams to start, making them eligible to compete for a cash prize pool of up to $280,000. Each year, the competition identifies a new challenge and set of activities that address real-world research questions, thus demonstrating skills that students will need to work in the wind or wider renewable energy industries. The Collegiate Wind Competition 2025 challenge requires participants to compete simultaneously in four contests: 1) Turbine Design Contest: Design, build, and present a unique, wind-driven power system. 2) Turbine Testing Contest: Test the wind turbine in a competition wind tunnel at the final event. 3) Project Development Contest: Research wind resource data, transmission infrastructure, and environmental factors to create a site plan and financial analysis for a hypothetical wind farm. 4) Connection Creation Contest: Partner with wind industry professionals, raise awareness of wind energy in your local community, and work with local media to promote your team's accomplishments. The competition does not prescribe a power system market or wind regime. It is expected that each team will participate in all four contests.

Collegiate Wind Competition↗
DOE OSTI2020

Tools Assessing Performance

For the distributed wind industry, it can be challenging to accurately predict the performance and annual energy production of projects prior to their installation. The U.S. Department of Energy’s Tools Assessing Performance (TAP) project aims to improve wind resource characterization, thereby reducing the uncertainty of project performance and financing costs, increasing consumer confidence, and lowering the levelized cost of distributed wind energy. A collaborative effort among DOE National Laboratories, TAP will create a computational framework that provides the distributed wind community with access to newly developed wind resource data and modeling capabilities. These capabilities will allow users to perform timely and accurate performance assessments for distributed wind projects at locations across the United States.

wind, distributed, tools, performance↗
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL’s 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis of the causes of high wind speed bias in the 20-year wind resource data set for offshore California that the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California Outer Continental Shelf, near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a 1-year period.

17 WIND ENERGY↗
DOE OSTI2023

Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast

Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s -1 (roughly 30m% of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.

17 WIND ENERGY↗
DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

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.

14 SOLAR ENERGY↗
DOE OSTI2022

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

17 WIND ENERGY↗
DOE OSTI2020

Advancing Offshore Wind Resource Characterization Using Buoy-based Observations

As countries continue to implement sustainable and renewable energy goals, the need for affordable low-carbon technologies, including those related to offshore wind energy, is accelerating. The U.S. federal government recognizes the environmental and economic benefits of offshore wind development and is taking the necessary steps to overcome critical challenges facing the industry to realize these benefits. The U.S. Department of Energy (DOE) is investing in buoy-mounted lidar systems to facilitate offshore measurement campaigns that will advance our understanding of the offshore environment and provide the observational data needed for model validation, particularly at hub height where offshore observations are particularly lacking. On behalf of the DOE, Pacific Northwest National Laboratory manages a Lidar Buoy Program that facilitates meteorological and oceanographic data collection using validated methods to support the U.S. offshore wind industry. Since being acquired in 2014, two DOE lidar buoys have been deployed on the U.S. east and west coasts; and their data represent the first publicly available multi-seasonal hub height data to be collected in U.S. waters. In addition, the buoys have undergone performance testing, significant upgrades, and a lidar validation campaign to ensure the accuracy and reliability of the lidar data needed to support wind resource characterization and model validation. The Lidar Buoy Program is providing valuable offshore data to the wind energy community, while focusing data collection on areas of acknowledged high priority.

offshore wind energy, lidar buoy, wind resource ch↗
DOE OSTI2020

How Wrong Can the Operational AEP Uncertainty Estimate Be When We Ignore the Correlations Between the Uncertainty Components?

Calculations of wind farm annual energy production (AEP) on operational data are essential for a variety of financial transactions during the life of wind plants. The AEP estimate is associated with an uncertainty value, which is calculated by combining contributions connected to on-site measurements, long-term reference measurements, losses, regression, windiness adjustment, and wind resource interannual variability. Although very limited documentation on the topic exists, the conventional approach currently used by the wind energy community to assess the uncertainty connected to the operational AEP estimate assume that the different uncertainty components are uncorrelated and therefore calculates the overall uncertainty with a sum of squares approach. In this analysis, we contrast the traditional technique to estimate the overall AEP uncertainty by ignoring the correlation between its different components with a novel Monte Carlo based approach, which can instead take into account the correlation between different uncertainty categories. We consider monthly operational data from 472 wind farms from the Energy Information Administration (EIA) 923 database to assess the difference between the two approaches. Long-term wind speed data needed for the AEP assessment are taken from three reanalysis products: the Modern-Era Retrospective analysis for Research and Applications v2 (MERRA-2), the European Reanalysis Interim (ERA-interim), and the National Centers for Environmental Prediction v2 (NCEP-2). The results of the Monte Carlo approach show that three pairs of AEP uncertainty components do show a statistically significant correlation: the uncertainty connected with wind resource inter-annual variability is positively correlated with the one related to the windiness correction and negatively correlated with the one due to the regression, and the wind measurement uncertainty is positively correlated with the regression uncertainty. All these correlations, which are found between uncertainty components which are not only part of an operational analysis, but also of a wind resource assessment, are currently ignored in the conventional techniques used as industry standard. We further investigate the causes of these correlations, in terms of common dependencies of different uncertainty components on wind resource variability, number of data points, and quality of the regression between wind speed and energy production data. Next, we quantify the error in the current industry standard technique, in terms of the percentage difference in total uncertainty calculated with the two considered approaches, for all the analyzed wind farms. We find a mean absolute percentage difference of about 6%, with the largest differences being greater than 20%. The data clearly confirm that ignoring the actual correlation between the uncertainty components can lead to large errors in the assessment of the operational AEP uncertainty, and the proposed Monte Carlo approach should be preferred.

Monte Carlo↗
DOE OSTI2023

A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.

Gaussian process regression↗
DOE OSTI2023

Data on temporal complementarity of hybrid renewable energy systems [SWR-23-09]

These datasets describe multiple facets of the temporal complementarity of co-located hybrid renewable energy systems throughout the United States. Several metrics characterizing the complementarity of generation profiles are provided on an annual and monthly basis (for both hourly and daily aggregations). These generation profiles are underpinned by hourly resource data (e.g., the WIND Toolkit and National Solar Radiation Database (NSRDB)) spanning the multi-year period 2007-2013. The data include complementarity results for greater than 1.76 million individual locations within the continental United States (CONUS). The data are intended to accompany two publications on the topic of temporal complementarity: 1) Harrison-Atlas, Dylan, Caitlin Murphy, Anna Schleifer, and Nicholas Grue. "Temporal complementarity and value of wind-PV hybrid systems across the United States." Renewable Energy 201 (2022): 111-123, doi:10.1016/j.renene.2022.10.060; and 2) Murphy, Caitlin, Harrison-Atlas, Dylan, Nicholas Grue, Vahan Gevorgian, Juan Gallego-Calderon, Shiloh Elliot and Thomas Mosier. “A Resource Assessment for FlexPower”. NREL Technical Report.

Harrison-Atlas, Dylan↗