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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 includes 40km vertical wind profiles from late 2023 through 9/30/2024 for Oklahoma, Kansas, and the Northeastern US. The data are in text format. Key for interpreting data is included in this package. Additionally, a descriptor accompanies each data file.
The U.S. Department of Energy's Wind Powering America initiative provides high-resolution wind maps and estimates of the wind resource potential that would be possible from development of the available windy land areas after excluding areas unlikely to be developed. This postcard is a marketing piece that stakeholders can provide to interested parties; it will guide them to Wind Powering America's online wind energy resource maps.
This work uses journey maps to assess the deployment of distributed wind technologies through the perspective of installers. Journey mapping is a human-centered design method that chronologically traces processes from the perspective of those who participate in them. The journey map will be leveraged to identify deployment pain points (i.e., manifestations of generic deployment barriers) that the Strategize, Engage, Network and Deploy (SEND) Distributed Wind project team will seek to address in future work.
This final report describes the comprehensive findings on a project to validate and improve atmospheric models for offshore wind resource mapping and prediction.
In developing a national energy strategy, the United States has a number of objectives, including increasing economic growth, improving environmental quality, and enhancing national energy security. Wind power contributes to these objectives through the deployment of clean, affordable and reliable domestic energy. To achieve U.S. wind generation objectives, the Wind and Water Power Program within the Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) instituted the U.S. Offshore Wind: Removing Market Barriers Program in FY 2011. Accurate and comprehensive information on offshore wind resource characteristics across a range of spatial and temporal scales is one market barrier that needs to be addressed through advanced research in remote sensing technologies. There is a pressing need for reliable offshore wind-speed measurements to assess the availability of the potential wind energy resource in terms of power production and to identify any frequently occurring spatial variability in the offshore wind resource that may impact the operational reliability and lifetime of wind turbines and their components and to provide a verification program to validate the “bankability” of the output of these alternative technologies for use by finance institutions for the financing of offshore wind farm construction. The application of emerging remote sensing technologies is viewed as a means to cost-effectively meet the data needs of the offshore wind industry. In particular, scanning and buoy mounted LIDAR have been proposed as a means to obtain accurate offshore wind data at multiple locations without the high cost and regulatory hurdles associated with the construction of offshore meteorological towers. However; before these remote sensing technologies can be accepted the validity of the measured data must be evaluated to ensure their accuracy. The proposed research will establish a unique coastal ocean test-bed in the Mid-Atlantic for the evaluation of LIDAR-based wind measurement systems to validate the accuracy of remotely measured wind data in marine applications. Specifically, the test-bed will be utilized to systematically evaluate the capability of emerging scanning LIDAR and buoy mounted vertically profiling LIDAR by: (1) Evaluating a fixed scanning LIDAR against land-based 50 and 60 meter high meteorological masts fitted with research quality cup-vane and/or sonic anemometers; (2) Evaluating a buoy mounted vertically profiling LIDAR fixed on land and floating in a sheltered bay against a co-located 60 meter high meteorological mast fitted with a research quality cup-vane and/or sonic anemometers and the fixed scanning LIDAR; and (3) Offshore field evaluation of both LIDAR platforms through a comparison of the fixed scanning LIDAR data and data obtained by the buoy mounted LIDAR located 10 miles offshore. The proposed research will systematically validate Light Detection and Ranging (LIDAR) based wind measurement systems and assess the temporal and spatial variability of the offshore wind resource in the Mid-Atlantic east of New Jersey. The goal of the proposed project is to address the technical and commercial challenges of the offshore wind energy industry by validating and assessing cost-effective, over ocean wind resource characterization technologies. The objective is to systematically evaluate the capability of both scanning and vertically profiling LIDARs to accurately measure 3D wind fields through comparison with fixed met masts and intercomparison among LIDAR platforms. Once validated, data collected by both buoy mounted vertically profiling LIDARs and shore-based, pulsed horizontally scanning LIDARs can be used to accurately assess offshore wind resources and to quantify the spatial and temporal variability in the offshore wind fields. One of the fundamental research questions to be addressed in phase 1 is the assessment of various measurement and data processing schemes to retrieve accurate wind vectors in the marine environment over large sampling ranges (10 to 12 km) and varying atmospheric aerosol levels. Atmospheric conditions and aerosol content within the coastal ocean region of the Mid-Atlantic seaboard of the US can vary significantly over short time periods in response to frontal passages and extratropical and tropical low pressure system passage offshore of the coast. Since aerosols provide the scattering medium for the determination of LIDAR Doppler shifts in the atmosphere the accuracy and range of LIDAR derived velocity measurements as a function of variation in aerosol content in the marine environment is a key research question to be addressed. In phase 1, it is desired to capture as much variation in atmospheric conditions and aerosol content as possible. To this end, collocated measurements of LIDAR and standard anemometer wind fields will be captured by the project PIs over all four seasons and during specific events (e.g., coastal low pressure system passage) in year 1. Additionally, since the meteorological masts are permanent structures, additional events can be captured over the three year duration of the field research project. All research instruments are owned by Fishermen’s Energy and made available to the PIs though a lease agreement as part of the DOE grant. Energy Fishermen’s Energy will be responsible for the operation and maintenance of the scanning LIDAR and met mast anemometers. On a daily basis, environmental data and systems performance indicators will be transmitted from each measurement station to the Fishermen’s project team consisting of both in-house personnel and equipment manufacturer engineers. Data sets include compiled LIDAR files as well as data sets from ancillary sensors. Diagnostic parameters to be monitored include standard deviations of measured values, battery levels and charging systems output, and the operational status. Once data have been confirmed as complete and reliable, files will be transferred to the Garrad Hassan (a subcontractor to Fishermen’s Energy) for incorporation in to the validation database, which is accessible to other scientific team members. Data collection times and durations will be determined by the PI and Co-PIs in consultation with instrument engineers to ensure the capture of data representative of the expected range of mid-Atlantic atmospheric conditions (e.g., temperature, moisture, coastal low pressure systems, tropical systems, rain, snow, fog). The collection and processing of the data is a function of site specific measurement requirements (Kelley et.al. 2007; Hannon et.al. 2008). To determine the optimal profiles of wind speed and direction from the LIDAR radial velocities as a function of azimuth angle, rigorous estimates of the bias and random error of each radial velocity estimate are required. Lockheed Martin Coherent Technologies, Inc., under contract with Fishermen’s Energy, will provide analyses of raw and processed data using various scan patterns to determine optimal performance settings for the pulsed scanning LIDAR. Once optimized, appropriate processing and analyses techniques will be evaluated by Garrad Hassan for use in validating the accuracy of the LIDAR wind field measurements against the standard anemometer measurements from the meteorological masts. The most attractive capability of the scanning LIDAR is the ability to provide high spatial resolution observations in a three-dimensional volume which provides superior statistical accuracy due to the large number of samples obtained. Each radial scan provides measurements in 100 range gates over a distance of 10 to 12 km at an update rate of 5 to 10 Hz and rotation of 2.5° per second. Each rotation at a fixed azimuth requires 2.4 minutes. Depending on the number of azimuths desired a complete scan can take up to 10 minutes or longer to complete. Once collected the radial velocities are processed to produce vector wind velocity estimates based on a set of data distributed in angle and range around points of interest, typically a standard grid within the radial wind map. To calculate wind vectors over a limited spatial area of interest for the comparison of data with other measurement platforms a localized least-squares approach has been applied by Hannon et.al. (2008) and a Variation Assimilation (VAR) processing technique has been applied by Chan and al Assimilation (VAR) processing technique has been applied by Chan and Shao (2006). Additionally, Kelley et.al. (2007) applied a “stare” technique that fixed a scanning LIDAR in both azimuth and elevation angles to measure over collocated sampling volumes of the LIDAR and a 3D sonic anemometer mounted to a fixed mast. Although the stare technique is limited to wind directions aligned with the sampling radial of the LIDAR, it does provide a direct comparison of sampling volumes. Each of the processing techniques described above (and possibly others) will be evaluated to determine the validity of the LIDAR derived wind fields in the marine environment. Numerical methods such as linear regression and comparison of probability density functions of wind fields measured by each instrument platform will be used to assess the processing techniques. Linear regression has the advantage of directly evaluating corresponding pairs of wind data measured by each instrument and can lend insight into deviations and bias between instruments as a function of wind speed. Assessment of the coherency between the probability density function of the wind measured by each instrument provides insight into processes that may not be accurately resolved by each instrument at specific frequencies. Once processed and assessed the most appropriate technique will be utilized to provide valid wind measurements from the pulsed scanning LIDAR. A detailed analysis of the measurement data from the LIDAR and the three meteorological towers and comparison of the coastal wind characteristics from the different systems will be performed by the CO_PI at NREL. This analysis will evaluate how the WT LIDAR performance and measurement of the wind characteristics vary with distance from the LIDAR and by atmospheric conditions; using the tower measurements at different distances (4.8, 9.6, and 19 km) from the LIDAR as a reference. The comparative analysis will include, to the extent possible, evaluation of parameters such as wind speed and direction distributions, wind shear, turbulence intensity and their variations by atmospheric conditions, month or season, and time of day.
The National Renewable Energy Laboratory (NREL) recently released new estimates of the U.S. potential for wind-generated electricity, using advanced wind mapping and validation techniques to triple previous estimates of the size of the nation's wind resources. The new study, conducted by NREL and AWS TruePower, finds that the contiguous 48 states have the potential to generate up to 37 milliongigawatt-hours annually. In comparison, the total U.S. electricity generation from all sources was roughly 4 million gigawatt-hours in 2009.
This dataset contains statistics (mean and standard deviation) calculated from quality-control lidar radial wind speed data through the LiSBOA method. This is an intermediate product for the synthesis of dual-Doppler wind maps.
This dataset contains statistics (mean and standard deviation) calculated from quality-control lidar radial wind speed data through the LiSBOA method. This is an intermediate product for the synthesis of dual-Doppler wind maps.
This dataset contains statistics (mean and standard deviation) calculated from quality-control lidar radial wind speed data through the LiSBOA method. This is an intermediate product for the synthesis of dual-Doppler wind maps.
This dataset contains statistics (mean and standard deviation) calculated from quality-control lidar radial wind speed data through the LiSBOA method. This is an intermediate product for the synthesis of dual-Doppler wind maps.
"A Supply Chain Road Map for Offshore Wind Energy in the United States" identifies pathways to developing a domestic offshore wind supply chain that can manufacture and deploy the major components needed to set the United States on a pathway to installing 30 GW of offshore wind by 2030 and 110 GW by 2050. The report estimates that this supply chain could require an investment of at least $\$$22.7 billion this decade to meet an annual demand for components, ports, and vessels in 2030. Although this is a considerable investment, it could allow the industry to install around $\$$100 billion worth of offshore wind this decade by reducing risk of delays due to global supply chain bottlenecks and creating a robust network of assets that will continue to be effective well beyond 2030. The United States would need at least 34 manufacturing facilities employing 10,000 workers, 39,000 jobs in the supporting supply chain, 10 marshaling ports, 4-6 dedicated wind turbine installation vessels, 4-6 dedicated heavy-lift vessels, and 4-8 U.S.-flagged specialized feeder barges to come online this decade to support an average annual deployment of 4-6 gigawatts offshore wind capacity per year. This supply chain could be developed in 6-9 years, but would require near-term decision making and efficient permitting and planning to strategically develop these resources by 2030. Additional investment and expansion would be required in the 2030s as the sector expands into new regions (such as the Gulf of Mexico) and new technologies (such as larger wind turbines and floating wind energy projects). Furthermore, the planning process needs to meaningfully engage with communities that will be impacted by supply chain expansion to achieve just outcomes and maximize benefits to these stakeholders, which will result in a more equitable and sustainable supply chain. While U.S. offshore wind has made significant progress in recent years, remaining supply chain challenges include uncertainty surrounding deployment and procurement timelines; a lack of port and vessel infrastructure; and limitations in the available workforce, supporting supplier networks, and energy justice best practices. However, many of these problems can be addressed through improved communication between key stakeholder groups, support from federal and state governments, and forward-thinking designs of supply chain assets to accommodate future technology changes for fixed-bottom and floating offshore wind. Although it is a significant task, developing these domestic capabilities represents a once-in-a-generation opportunity to contribute to a decarbonized energy future and also create massive economic benefits that are distributed throughout the country.
We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.
In this study, we develop an updated global estimate of onshore wind energy potential using reanalysis wind speed data, along with updated wind turbine technology performance and cost assumptions as well as explicit consideration of transmission distance in the calculation of transmission costs. We find that wind has the potential to supply a significant portion of world energy needs, although this potential varies substantially by region as well as with assumptions such as on what types of land can be used to site wind farms. Total global wind potential under central assumptions is estimated to be approximately 89 petawatt hours per year at less than 9 cents/kWh with substantial regional variations. One limitation of global wind analyses is that the resolution of current global wind speed reanalysis data can result in an underestimate of high wind areas. A sensitivity analysis of eight key parameters is presented. Wind potential is sensitive to a number of input parameters, particularly those related to land suitability and turbine density as well as cost and financing assumptions which have important policy implications. Transmission cost has a relatively small impact on total wind costs, changing the potential at a given cost by 20-30%. As a result of sensitivities studied here we suggest that further research intended to inform wind supply curve development focus not purely on physical science, such as better resolved wind maps, but also on these less well-defined factors, such as land-suitability, that will also have an impact on the long-term role of wind power.
This report summarized the data collected from two 60-meter (m) meteorological towers (met masts or met towers) and three sonic detection and ranging (SODAR) units on St. Thomas and St. Croix in 2012 and 2013. These results are an update to the previous feasibility study; the collected data are critical to the successful development of a wind project at either site (Lantz et al. 2012). The wind maps for the territory were updated based on these improved data. The National Renewable Energy Laboratory (NREL) conducted measurements from met towers and SODAR units for several locations in the U.S. Virgin Islands (USVI). Both met masts were deployed for more than a year and produced high-quality data (standard industry practice in the wind industry). The Longford (St. Croix) and Bovoni (St. Thomas) sites are estimated to have long-term corrected wind speeds that exceed 7 meters per second (m/s), which may produce financially viable wind projects for both sites. The levelized cost of energy for both sites was estimated at $\$$0.08–$\$$0.14 per kilowatt-hour for typical utility-scale wind turbine with the uncertainty in the estimate driven by uncertainty in the installed capital costs of the proposed wind farms. These estimates assume that the wind production tax credit is not reinstated, but that accelerated depreciation is in place.
This presentation describes the current standards for conducting a national-scale wind resource assessment for power generation, along with the risk/benefit considerations to be considered when beginning a wind resource assessment. The presentation describes changes in turbine technology and viable wind deployment due to more modern turbine technology and taller towers and shows how the Philippines national wind resource assessment evolved over time to reflect changes that arise from updated technologies and taller towers.
We study the influence of the spatial resolution on scales of 5 Degree-Sign and smaller of solar surface magnetic field maps on global magnetohydrodynamic solar wind models, and on a model of coronal heating and X-ray emission. We compare the solutions driven by a low-resolution Wilcox Solar Observatory magnetic map, the same map with spatial resolution artificially increased by a refinement algorithm, and a high-resolution Solar and Heliospheric Observatory Michelson Doppler Imager map. We find that both the wind structure and the X-ray morphology are affected by the fine-scale surface magnetic structure. Moreover, the X-ray morphology is dominated by the closed loop structure between mixed polarities on smaller scales and shows significant changes between high- and low-resolution maps. We conclude that three-dimensional modeling of coronal X-ray emission has greater surface magnetic field spatial resolution requirements than wind modeling, and can be unreliable unless the dominant mixed polarity magnetic flux is properly resolved.
A robust workforce is essential to growing domestic wind manufacturing capabilities. This presentation provides an overview of an NREL analysis of wind-focused education at American colleges and universities. The second part of the presentation discusses DOE/NREL workforce-related projects, such as the Wind Career Map, the Collegiate Wind Competition, and the Wind for Schools project.
The United States needs a skilled and qualified wind energy workforce to produce domestic clean power. To assist with wind energy workforce development, the U.S. Department of Energy (DOE) and National Renewable Energy Laboratory are engaged with several efforts.This presentation by Suzanne Tegen describes these efforts, including a wind industry survey, DOE's Wind Career Map, the DOE Wind Vision report, and an in-depth discussion of the Jobs & Economic Development Impacts Model.