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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California

Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

17 WIND ENERGY↗
DOE OSTI2015

Wind resource quality affected by high levels of renewables

For solar photovoltaic (PV) and wind resources, the capacity factor is an important parameter describing the quality of the resource. As the share of variable renewable resources (such as PV and wind) on the electric system is increasing, so does curtailment (and the fraction of time when it cannot be avoided). At high levels of renewable generation, curtailments effectively change the practical measure of resource quality from capacity factor to the incremental capacity factor. The latter accounts only for generation during hours of no curtailment and is directly connected with the marginal capital cost of renewable generators for a given level of renewable generation during the year. The Western U.S. wind generation is analyzed hourly for a system with 75% of annual generation from wind, and it is found that the value for the system of resources with equal capacity factors can vary by a factor of 2, which highlights the importance of using the incremental capacity factor instead. Finally, the effect is expected to be more pronounced in smaller geographic areas (or when transmission limitations imposed) and less pronounced at lower levels of renewable energy in the system with less curtailment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Improved Offshore Wind Resource Assessment in Global Climate Stabilization Scenarios

This paper introduces a technique for digesting geospatial wind-speed data into areally defined -- country-level, in this case -- wind resource supply curves. We combined gridded wind-vector data for ocean areas with bathymetry maps, country exclusive economic zones, wind turbine power curves, and other datasets and relevant parameters to build supply curves that estimate a country's offshorewind resource defined by resource quality, depth, and distance-from-shore. We include a single set of supply curves -- for a particular assumption set -- and study some implications of including it in a global energy model. We also discuss the importance of downscaling gridded wind vector data to capturing the full resource potential, especially over land areas with complex terrain. This paperincludes motivation and background for a statistical downscaling methodology to account for terrain effects with a low computational burden. Finally, we use this forum to sketch a framework for building synthetic electric networks to estimate transmission accessibility of renewable resource sites in remote areas.

17 WIND ENERGY↗
DOE OSTI2020

Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations

The offshore wind industry in the U.S. is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools in order to set performance expectations and quantify the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub-heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization.

lidar buoy, model validation, offshore wind energy↗
DOE OSTI2015

Improved Tools for Wind Resource Assessment with Remote Sensing Sodar Device: Cooperative Research and Development Final Report, CRADA Number: CRD-09-363

Under this Agreement, NREL will work with the Participant to characterize wind resource assessment measurement systems needed for the design, construction, and integration of wind energy conversion systems to produce electricity for utility grid applications. This work includes, but is not limited to, research and development of hardware and software systems needed to advance wind energy resource assessment technology at speed and scale for use by electric utilities and wind power system integrators.

17 WIND ENERGY↗
DOE OSTI2019

Workshop on Research Needs for Offshore Wind Resource Characterization

This report is an account of presentations and discussions that occurred during the Workshop on Research Needs for Offshore Wind Resource Characterization in Alexandria, Virginia on March 5–6, 2019. The workshop was organized by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy’s Wind Energy Technologies Office, who was the facilitator. The report was prepared by DOE’s Pacific Northwest National Laboratory with contributions from Argonne National Laboratory, Lawrence Livermore National Laboratory, and the National Renewable Energy Laboratory.

17 WIND ENERGY↗
NASA NTRS1982

Approaches to wind resource verification

Verification of the regional wind energy resource assessments produced by the Pacific Northwest Laboratory addresses the question: Is the magnitude of the resource given in the assessments truly representative of the area of interest? Approaches using qualitative indicators of wind speed (tree deformation, eolian features), old and new data of opportunity not at sites specifically chosen for their exposure to the wind, and data by design from locations specifically selected to be good wind sites are described. Data requirements and evaluation procedures for verifying the resource are discussed.

Barchet, W. R.↗
DOE OSTI2025

Quantifying Uncertainties in Modeling Wind Resource Data from Different PBL Schemes in the WRF Model: A Case Study Over the Puerto Rico Region

This study examines the modeling uncertainty of wind resource data stemming from the use of various planetary boundary layer (PBL) parameterizations available in the Weather Research and Forecasting (WRF) model. WRF-based wind simulations spanning 20 years at 3-km resolution using 11 different PBL schemes are used to objectively investigate the uncertainty in modeling wind speed for land-based wind (LBW) and offshore wind (OSW) locations in Puerto Rico. The uncertainty in the wind modeling for the 20-year dataset is quantified using the spread index (SI) and standard deviation (SD). For virtual LBW and OSW sites, the SI and SD values are analyzed as calculated across various spatial and temporal scales. Because the PBL's atmospheric stability conditions can be characterized into two dominant categories, the study focuses on analyzing the SI and SD for daytime (mainly unstable PBL conditions) and nighttime (mainly stable PBL conditions). For wind shear (10 m-200 m) at the OSW and LBW sites, WRF-based numerical experiments indicate the following SI (or SD) ranges: 39%-94% (0.74 m/s-1.44 m/s) during the daytime for OSW, 50%-75% (0.68 m/s-1.19 m/s) during the daytime for LBW, 37%-60% (0.73 m/s-1.12 m/s) during the nighttime for OSW, and 57%-143 % (0.65 m/s-1.43 m/s) during the nighttime for LBW. While a high SI is observed when modeling LBW during the nighttime, there are notable modeling uncertainties during the daytime on the leeward side of the orographic barriers for Puerto Rico.

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 OSTI2012

Fort Carson Wind Resource Assessment

The U.S. Army has long been interested in reducing its dependence on fossil fuels for energy, increasing its energy security, and reducing its energy intensity. Increasing the use of renewable energy at Army bases serves all of these goals. As part of these efforts, the Army has been collaborating with the U.S. Department of Energy’s (DOE’s) National Renewable Energy Laboratory (NREL) to evaluate renewable energy options at Fort Carson, Colorado. This report focuses on the wind resource assessment, the estimated energy production of wind turbines, and economic potential of a wind turbine project on a ridge in the southeastern portion of the Fort Carson Army base. Wind data was collected from a 50-meter (m) meteorological (met) tower on a small ridge in the southeastern part of the Fort Carson base. A mini sonic detection and ranging (miniSODAR) unit was also deployed at this site to measure wind speeds at elevations 30 to 140 m (98 to 459 ft) above the ground. The data collected provided an effective dataset for comparative analysis of wind speed, turbulence intensity, and energy production of several wind turbines suitable for the wind conditions at the site.

17 WIND ENERGY↗
DOE OSTI2017

An improved global wind resource estimate for integrated assessment models

This study summarizes initial steps to improving the robustness and accuracy of global renewable resource and techno-economic assessments for use in integrated assessment models. We outline a method to construct country-level wind resource supply curves, delineated by resource quality and other parameters. Using mesoscale reanalysis data, we generate estimates for wind quality, both terrestrial and offshore, across the globe. Because not all land or water area is suitable for development, appropriate database layers provide exclusions to reduce the total resource to its technical potential. We expand upon estimates from related studies by: using a globally consistent data source of uniquely detailed wind speed characterizations; assuming a non-constant coefficient of performance for adjusting power curves for altitude; categorizing the distance from resource sites to the electric power grid; and characterizing offshore exclusions on the basis of sea ice concentrations. The product, then, is technical potential by country, classified by resource quality as determined by net capacity factor. Additional classifications dimensions are available, including distance to transmission networks for terrestrial wind and distance to shore and water depth for offshore. We estimate the total global wind generation potential of 560 PWh for terrestrial wind with 90% of resource classified as low-to-mid quality, and 315 PWh for offshore wind with 67% classified as mid-to-high quality. These estimates are based on 3.5 MW composite wind turbines with 90 m hub heights, 0.95 availability, 90% array efficiency, and 5 MW/km 2 deployment density in non-excluded areas. We compare the underlying technical assumption and results with other global assessments.

17 WIND ENERGY↗
DOE OSTI2017

An Improved Global Wind Resource Estimate for Integrated Assessment Models: Preprint

This paper summarizes initial steps to improving the robustness and accuracy of global renewable resource and techno-economic assessments for use in integrated assessment models. We outline a method to construct country-level wind resource supply curves, delineated by resource quality and other parameters. Using mesoscale reanalysis data, we generate estimates for wind quality, both terrestrial and offshore, across the globe. Because not all land or water area is suitable for development, appropriate database layers provide exclusions to reduce the total resource to its technical potential. We expand upon estimates from related studies by: using a globally consistent data source of uniquely detailed wind speed characterizations; assuming a non-constant coefficient of performance for adjusting power curves for altitude; categorizing the distance from resource sites to the electric power grid; and characterizing offshore exclusions on the basis of sea ice concentrations. The product, then, is technical potential by country, classified by resource quality as determined by net capacity factor. Additional classifications dimensions are available, including distance to transmission networks for terrestrial wind and distance to shore and water depth for offshore. We estimate the total global wind generation potential of 560 PWh for terrestrial wind with 90% of resource classified as low-to-mid quality, and 315 PWh for offshore wind with 67% classified as mid-to-high quality. These estimates are based on 3.5 MW composite wind turbines with 90 m hub heights, 0.95 availability, 90% array efficiency, and 5 MW/km2 deployment density in non-excluded areas. We compare the underlying technical assumption and results with other global assessments.

17 WIND ENERGY↗
DOE OSTI2014

Wind Technology Advancements and Impacts on Western Wind Resources (Presentation)

Robi Robichaud made this presentation at the Bureau of Land Management West-wide Wind Opportunities and Constraints Mapping (WWOCM) Project public meeting in Denver, Colorado in September 2014. This presentation outlines recent wind technology advancements, evolving turbine technologies, and industry challenges. The presentation includes maps of mean wind speeds at 50-m, 80-m, and 100-m hub heights on BLM lands. Robichaud also presented on the difference in mean wind speeds from 80m to 100m in Wyoming.

17 WIND ENERGY↗
DOE OSTI2018

Offshore Wind Resource, Cost, and Economic Potential in the State of Maine

This report provides information for decision-makers about floating offshore wind technologies in the state of Maine. It summarizes research efforts performed at the National Renewable Energy Laboratory between 2015 and 2017 to analyze the resource potential, cost of offshore wind, and economic potential of offshore wind from four primary reports: Musial et al. (2016); Beiter et al. (2016, 2017); and Mone et al. (unpublished). From Musial et al. (2016), Maine's technical offshore wind resource potential ranked seventh in the nation overall with more than 411 terawatt-hours/year of offshore resource generating potential. Although 90% of this wind resource is greater than 9.0-meters-per-second average velocity, most of the resource is over deep water, where floating wind technology is needed. Levelized cost of energy and levelized avoided cost of energy were computed to estimate the unsubsidized 'economic potential' for Maine in the year 2027 (Beiter et al. 2016, 2017). The studies found that Maine may have 65 gigawatts of economic potential by 2027, the highest of any U.S. state. Bottom-line costs for the Aqua Ventus project, which is part of the U.S. Department of Energy's Advanced Technology Demonstration project, were released from a proprietary report written by NREL in 2016 for the University of Maine (Mone et al. unpublished). The report findings were that economies of scale and new technology advancements lowered the cost from $300/megawatt-hour (MWh) for the two-turbine 12-megawatt (MW) Aqua Ventus 1 project, to $126/MWh for the commercial-scale, 498-MW Aqua Ventus-2 project. Further cost reductions to $77/MWh were found when new technology advancements were applied for the 1,000-MW Aqua Ventus-3 project in 2030. No new analysis was conducted for this report.

17 WIND ENERGY↗
DOE OSTI2012

Greater Sage-Grouse Habitat Use and Population Demographics at the Simpson Ridge Wind Resource Area, Carbon County, Wyoming

This study was conducted to obtain baseline data on use of the proposed Simpson Ridge Wind Resource Area (SRWRA) in Carbon County, Wyoming by greater sage-grouse. The first two study years were designed to determine pre-construction seasonally selected habitats and population-level vital rates (productivity and survival). The presence of an existing wind energy facility in the project area, the PacifiCorp Seven Mile Hill (SMH) project, allowed us to obtain some information on initial sage-grouse response to wind turbines the first two years following construction. To our knowledge these are the first quantitative data on sage-grouse response to an existing wind energy development. This report presents results of the first two study years (April 1, 2009 through March 30, 2011). This study was selected for continued funding by the National Wind Coordinating Collaborative Sage-Grouse Collaborative (NWCC-SGC) and has been ongoing since March 30, 2011. Future reports summarizing results of this research will be distributed through the NWCC-SGC. To investigate population trends through time, we determined the distribution and numbers of males using leks throughout the study area, which included a 4-mile radius buffer around the SRWRA. Over the 2-year study, 116 female greater sage-grouse were captured by spotlighting and use of hoop nets on roosts surrounding leks during the breeding period. Radio marked birds were located anywhere from twice a week to once a month, depending on season. All radio-locations were classified to season. We developed predictor variables used to predict success of fitness parameters and relative probability of habitat selection within the SRWRA and SMH study areas. Anthropogenic features included paved highways, overhead transmission lines, wind turbines and turbine access roads. Environmental variables included vegetation and topography features. Home ranges were estimated using a kernel density estimator. We developed resource selection functions (RSF) to estimate probability of selection within the SRWRA and SMH. Fourteen active greater sage-grouse leks were documented during lek surveys Mean lek size decreased from 37 in 2008 to 22 in 2010. Four leks located 0.61, 1.3, 1.4 and 2.5 km from the nearest wind turbine remained active throughout the study, but the total number of males counted on these four leks decreased from 162 the first year prior to construction (2008), to 97 in 2010. Similar lek declines were noted in regional leks not associated with wind energy development throughout Carbon County. We obtained 2,659 sage-grouse locations from radio-equipped females, which were used to map use of each project area by season. The sage-grouse populations within both study areas are relatively non-migratory, as radio-marked sage-grouse used similar areas during all annual life cycles. Potential impacts to sage-grouse from wind energy infrastructure are not well understood. The data rom this study provide insight into the early interactions of wind energy infrastructure and sage-grouse. Nest success and brood-rearing success were not statistically different between areas with and without wind energy development in the short-term. Nest success also was not influenced by anthropogenic features such as turbines in the short-term. Additionally, female survival was similar among both study areas, suggesting wind energy infrastructure was not impacting female survival in the short-term; however, further analysis is needed to identify habitats with different levels of risk to better understand the impact of wind energy development on survival. Nest and brood-rearing habitat selection were not influenced by turbines in the short-term; however, summer habitat selection occurred within habitats closer to wind turbines. Major roads were avoided in both study areas and during most of the seasons. The impact of transmission lines varied among study areas, suggesting other landscape features may be influencing selection. The data provided in this report are preliminary and are not meant to provide a basis for forming any conclusions regarding potential impacts of wind energy development on sage-grouse. Although the data collected during the initial phases of this study indicate that greater sage-grouse may continue to use habitats near wind-energy facilities, research conducted on greater sage-grouse response to oil and gas development has found population declines may not occur until 2-10 years after development. Therefore, long-term data from several geographic areas within the range of the sage-grouse will likely be required to adequately assess impacts of wind-energy development on greater sage-grouse.

17 WIND ENERGY↗
DOE OSTI2024

California Offshore Wind Resource Bias Investigation and Correction [Slides]

This presentation highlights the findings of an investigation into the causes of high wind speed bias in offshore California's 20-year wind resource dataset, CA20, released by the National Renewable Energy Laboratory (NREL) in 2020. Utilizing observations from floating lidars deployed by the Pacific Northwest National Laboratory (PNNL), considerable bias in modeled hub-height winds was identified. The study reveals that the choice of planetary boundary layer (PBL) scheme significantly impacts wind speed accuracy, with the less common Yonsei University (YSU) scheme nearly eliminating bias compared to the widely used Mellor-Yamada-Nakanishi-Niino (MYNN) scheme. Furthermore, it shows that one of the key factors contributing to this modeled bias is the fact that MYNN overestimates the frequency of stable atmospheric conditions in offshore California. As a result, an updated dataset, NOW-23, incorporating YSU as the PBL scheme, has been produced.

17 WIND ENERGY↗