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This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.
This report presents a state-of-the-art wind resource data set produced by the National Renewable Energy Laboratory (NREL) for the California Pacific Outer Continental Shelf (OCS). This data set replaces NREL's Wind Integration National Dataset (WIND) Toolkit for the California OCS, which was produced and released publicly in 2013 and is currently the principal data set used by stakeholders for wind resource assessment in the continental United States.
This data set includes collected wind speed and wind direction from 30 m to 200 m above ground level (AGL) in 10-m range gates.
As the wind energy industry continues to pushfor increased power production and lower cost of energy, thefocus of research has expanded from individual turbines toentire wind farms. Among a host of interesting problems to besolved when considering the wind farm as a whole, we considerthe challenge of scalar field estimation, based on informationalready collected at the individual turbine level. We aim toestimate the large-scale, low-frequency characteristics of thewind field, such as the mean wind direction and the overalldecrease in wind speed across the farm, and employ a Kalmanfilter that models the wind field using a polynomial function.We compare the proposed method’s performance to both asimple averaging technique and filtering of individual turbinemeasurements. The method presented is not limited to windturbines and is applicable in other situations where multipleremote agents are used to estimate a scalar field.
While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.
To provide lower-level wind speed and direction measurements.
To provide lower-level wind speed and direction measurements.
Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.
The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .
The availability of economically marginal land for energy crops is identified using the Cropland Data Layer and other soil, wind, climate data resources. All data are recognized on a 30m spatial resolution across the continental United States.
The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.