SEARCH · Engineering Papers

Results for “WIND”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,747 records · Page 97

All sources NASA NTRS DOE OSTI

NASA NTRS2021

Assimilation of DAWN Doppler Wind Lidar Data During the 2017 Convective Processes Experiment (CPEX): Impact on Precipitation and Flow Structure

An improved representation of 3-D air motion and precipitation structure through forecast models and assimilation of observations is vital for improvements in weather forecasting capabilities. However, there are few independent data to properly validate a model forecast of precipitation structure when the underlying dynamics are evolving on short convective timescales. Using data from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2 μmDoppler Aerosol Wind (DAWN) lidar collected during the2017 Convective Processes Experiment (CPEX), the NASA Unified Weather Research and Forecasting (WRF) Ensemble Data Assimilation System (EDAS) modeling system was used to quantify the impact of high-resolution sparsely sampled DAWN measurements on the analyzed variables and on the forecast when the DAWN winds were assimilated. Over-all, the assimilation of the DAWN wind profiles had a discernible impact on the wind field as well as the evolution and timing of the 3-D precipitation structure. Analysis of individual variables revealed that the assimilation of the DAWN winds resulted in important and coherent modifications of the environment. It led to an increase in the near-surface convergence, temperature, and water vapor, creating more favorable conditions for the development of convection exactly where it was observed (but not present in the control run). Comparison to APR-2 and observations by the Global Precipitation Measurement (GPM) satellite shows a much-improved forecast after the assimilation of the DAWN winds – development of precipitation where there was none, more organized precipitation where there was some, and a much more intense and organized cold pool, similar to the analysis of the dropsonde data. The onset of the vertical evolution of the precipitation showed similar radar-derived cloud-top heights, but delayed in time. While this investigation was limited to a single CPEX flight date, the investigation design is appropriate for further investigation of the impact of airborne Doppler wind lidar observations upon short-term convective precipitation forecasts

DAWN↗

NASA NTRS2024

Anisotropy of Density Fluctuations in the Solar Wind at 1 au

A well-known property of solar wind plasma turbulence is the observed anisotropy of the autocorrelations, or equivalently the spectra, of velocity and magnetic field fluctuations. Here we explore the related but apparently not well-studied issue of the anisotropy of plasma density fluctuations in the energy-containing and inertial ranges of solar wind turbulence. Using 10 yr (1998–2008) of in situ data from the Advanced Composition Explorer mission, we find that for all but the fastest wind category, the density correlation scale is slightly larger in directions quasi-parallel to the large-scale mean magnetic field as compared to quasi-perpendicular directions. The correlation scale in fast wind is consistent with isotropic. The anisotropy as a function of the level of correlation is also explored. We find at small correlation levels, i.e., at energy-containing scales and larger, the density fluctuations are close to isotropy for fast wind, and slightly favor more rapid decorrelation in perpendicular directions for slow and medium winds. At relatively smaller (inertial range) scales where the correlation values are larger, the sense of anisotropy is reversed in all speed ranges, implying a more "slablike" structure, especially prominent in the fast wind samples. We contrast this finding with published results on velocity and magnetic field correlations.

Interplanetary turbulence↗

DOE OSTI2018

Offshore Wind Initiatives at the U.S. Department of Energy

Coastal and Great Lakes states account for nearly 80% of U.S. electricity demand, and the winds off the shores of these coastal load centers have a technical resource potential twice as large as the nation’s current electricity use. With the costs of offshore wind energy falling globally and the first U.S. offshore wind farm operational off the coast of Block Island, Rhode Island since 2016, offshore wind has the potential to contribute significantly to a clean, affordable, and secure national energy mix. To support the development of a world-class offshore wind industry, the U.S. Department of Energy (DOE) has been supporting a broad portfolio of offshore wind research, development, and demonstration projects since 2011 and released a new National Offshore Wind Strategy jointly with the U.S. Department of the Interior (DOI) in 2016.

floating↗

DOE OSTI2023

On the Low Risk of Subsynchronous Resonance (SSR) in Type III Wind Turbines Operating in Grid-Forming Control

We have shown in a previous work that the risk of sub-synchronous resonance (SSR) between a wind power plant with Type III wind turbines and series-compensated transmission lines is low when the wind turbines in the plant are operated in grid-forming mode, instead of the standard grid-following mode. The fundamental mechanism behind the improved damping characteristics is explained in this paper by modeling the positive and negative sequence impedances of Type III wind turbines for GFM operation mode. It is discovered that the GFM control naturally acts against the negative resistance of Type III wind turbines that results from an interaction between the proportional gain of the rotor-side converter current controller and negative slip. The developed sequence impedance models and improved damping behavior are verified using PSCAD simulations of a 2.5-MW Type III GFM wind turbine. The modeling predictions are also supported by experimentally measuring the sequence impedance response of a 2.5-MW Type III wind turbine during operation in GFL and GFM modes.

grid-following turbine↗

DOE OSTI2016

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.

17 WIND ENERGY↗

DOE OSTI2025

Gaussian FLOWERS: Wind-rose-based analytical integration of Gaussian wake model for extremely fast AEP estimation

A major cost in the study of wind farm layout optimization is the repeated evaluation of the annual energy production (AEP). The current approach to estimating AEP requires a large set of flow simulations to be performed that cover each discrete wind speed and direction combination contained within the wind rose, followed by a probability-weighted sum of the power production resulting from each simulation. Even with inexpensive engineering wake models, this numerical integration scheme can lead to high computational costs. In this paper, we derive an analytical formulation for estimating farm AEP across every wind direction, based on a Gaussian wake velocity model, which reduces the number of wind farm simulations to a single function evaluation. As a result, we find that the Gaussian-FLOWERS approach reduces the time for AEP calculations by more than two orders of magnitude with a small trade-off in accuracy when compared to a conventional approach. This massive reduction in computation cost is useful to reduce overall costs in wind farm layout optimization studies.

17 WIND ENERGY↗

DOE OSTI2025

Using system identification in modeling the yaw response of tail fins for small wind turbines with bearing friction

Here, we describe three main developments of our previous study of the nonlinear yaw dynamics of the tail fins for small wind turbines [Khedr et al., J. Renewable Sustainable Energy 16, 053305 (2024)]. First, the model constants derived from archived computational and experimental studies are adjusted by employing system identification (SI) to maximize the model's agreement with wind tunnel tests. This adjustment was done for high wind speeds, where yaw bearing friction can be ignored. When starting a turbine at low wind speed, however, friction can become important. Our second development is to implement a model for the frictional resistive torque and use SI to maximize its accuracy. These developments used wind tunnel experiments on generic delta, elliptical, and rectangular planforms that were described by Khedr et al. [J. Renewable Sustainable Energy 16, 053305 (2024)]. Since the aerodynamic and friction models employ a large number of constants, we describe ways to constrain the values using linearized solutions of the response equations for small and large yaw angles. Third, we test the generality of the aerodynamic and friction modeling using the complex planform from a commercial small turbine, for which limited theoretical and computational guidance is available in selecting the model constants. The model implemented with SI is shown to provide an accurate description of the yaw response of the complex planform. Guidelines are given for the use of wind tunnel tests to determine the model constants for tail fins of any planform.

17 WIND ENERGY↗

DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗

DOE OSTI2020

Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

Turbulence governs the development and erosion of wind farm wakes, which can deplete the offshore wind resource. Therefore, an accurate understanding of atmospheric turbulence is required to support the rapid growth of offshore wind energy. Using 13 months of lidar observations off the coast of Massachusetts, we find that offshore wind plants at the site will experience very low turbulence, quantified as lidar turbulence intensity, especially in summer, when the wind flows from the open ocean. Moreover, the lowest turbulence regimes are often associated with large wind veer conditions, which can impact the effectiveness of wake steering solutions.

17 WIND ENERGY↗

DOE OSTI2014

Voltage Impacts of Utility-Scale Distributed Wind

Although most utility-scale wind turbines in the United States are added at the transmission level in large wind power plants, distributed wind power offers an alternative that could increase the overall wind power penetration without the need for additional transmission. This report examines the distribution feeder-level voltage issues that can arise when adding utility-scale wind turbines to the distribution system. Four of the Pacific Northwest National Laboratory taxonomy feeders were examined in detail to study the voltage issues associated with adding wind turbines at different distances from the sub-station. General rules relating feeder resistance up to the point of turbine interconnection to the expected maximum voltage change levels were developed. Additional analysis examined line and transformer overvoltage conditions.

17 WIND ENERGY↗

DOE OSTI2015

2014 Distributed Wind Market Report

According to the 2014 Distributed Wind Market Report, distributed wind reached a cumulative capacity of almost 1 GW (906 MW) in the United States in 2014, reflecting nearly 74,000 wind turbines deployed across all 50 states, Puerto Rico, and the U.S. Virgin Islands. In total, 63.6 MW of new distributed wind capacity was added in 2014, representing nearly 1,700 units and $170 million in investment across 24 states. In 2014, America's distributed wind energy industry supported a growing domestic industrial base as exports from United States-based small wind turbine manufacturers accounted for nearly 80% of United States-based manufacturers' sales.

17 WIND ENERGY↗

DOE OSTI2016

Floating Offshore Wind in Oregon: Potential for Jobs and Economic Impacts from Two Future Scenarios

Construction of the first offshore wind power plant in the United States began in 2015, off the coast of Rhode Island, using fixed platform structures that are appropriate for shallow seafloors, like those located off of the East Coast and mid-Atlantic. However, floating platforms, which have yet to be deployed commercially, will likely need to anchor to the deeper seafloor if deployed off of the West Coast. To analyze the employment and economic potential for floating offshore wind along the West Coast, the Bureau of Ocean Energy Management (BOEM) commissioned the National Renewable Energy Laboratory (NREL) to analyze two hypothetical, large-scale deployment scenarios for Oregon: 5,500 megawatts (MW) of offshore wind deployment in Oregon by 2050 (Scenario A), and 2,900 MW of offshore wind by 2050 (Scenario B). These levels of deployment could power approximately 1,600,000 homes (Scenario A) or 870,000 homes (Scenario B). Offshore wind would contribute to economic development in Oregon in the near future, and more substantially in the long term, especially if equipment and labor are sourced from within the state. According to the analysis, over the 2020-2050 period, Oregon floating offshore wind facilities could support 65,000-97,000 job-years and add $6.8 billion-$9.9 billion to the state GDP (Scenario A).

17 WIND ENERGY↗

DOE OSTI2016

An Initial Evaluation of Siting Considerations on Current and Future Wind Deployment

This report provides a deeper understanding of the wind project development process, from desktop studies to a successful project in the ground. It examines three siting consideration categories that wind project sponsors must include in the development process: wildlife (species that live in, near, or migrate through the area where wind development is possible), radar (wind turbines can cause interference with radar signals), and public engagement (representing communities and stakeholders who live near wind power projects). The research shows that although this country's abundant wind resource provides numerous options for addressing siting considerations, actually siting individual projects is becoming more difficult because of regulatory and other uncertainties. Model results are based on the premise that developers will be able to site, permit, and build successful projects, which is not always the case in reality.

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 OSTI2021

Distributed Wind Research Program Workshop Report

The U.S. Department of Energy’s (DOE’s) Wind Energy Technologies Office (WETO) Distributed Wind Research Program seeks to enable wind technology as an affordable, accessible, and compatible distributed energy resource (DER) option for individuals, businesses, and communities. WETO’s Distributed Wind Research Program includes national laboratory projects and partnerships with industry and academia. WETO organized the Distributed Wind Research Program Workshop to bring together laboratory and industry project stakeholders to achieve the following objectives: Educate - Create a shared high-level understanding for the breadth of WETO’s distributed wind research Collaborate - Facilitate coordination and collaboration between funding recipients to leverage resources Innovate - Understand future research challenges and opportunities. This workshop convened over 80 participants from various organizations. Workshop participants were asked for their individual feedback based on their own expertise and experience.

17 WIND ENERGY↗

DOE OSTI2021

Comparing Capacity Credit Calculations for Wind: A Case Study in Texas

The degree to which wind energy can contribute to the capacity needed to meet resource adequacy requirements, also known as capacity credit (CC), varies regionally with wind resource and correlation to net load. CC is an important metric widely used for resource planning and resource adequacy assessments. However, there are multiple methods for computing and estimating CC, depending on specific needs, access to data, and computational burden. It is unclear the extent to which the CC computation method may influence the result. To address this, we use a probabilistic resource adequacy tool and multiple approximation methods to systematically assess the CC of wind for near-term wind deployment under a case study in Texas. We find that proper consideration of transmission constraints is important; some approximation methods may overestimate the CC of wind due to a lack of consideration of transmission constraints, while other approximation methods may underestimate the CC by not capturing the ability of wind to be shipped to neighboring regions. In this case study, we find that several approximation methods do come close to the CC calculated by more robust probabilistic methods. However, the best approximation method may vary on a case-by-case basis, depending on system-specific considerations.

17 WIND ENERGY↗

DOE OSTI2022

Distributed Wind Energy Futures Study

To better understand distributed wind opportunities in the United States, researchers at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory explored cost, performance, and valuation benchmarks necessary for distributed wind to achieve widespread commercial viability in the United States by 2035. Building on DOE's prior benchmark report "Assessing the Future of Distributed Wind: Opportunities for Behind-the-Meter Projects", the new Distributed Wind Futures Study employs higher-resolution data and new modeling techniques to highlight geographic trends in technical and economic potential and to compare the cost and performance of distribution-connected, megawatt-scale wind applications (referred to as "front-of-the-meter" applications) and behind-the-meter systems, thereby informing the case for investment. The report identifies the best locations and sectors for behind-the-meter and front-of-the-meter applications and provides data to inform the trade-offs consumers face when deciding between distributed wind and solar photovoltaic (PV).

17 WIND ENERGY↗

DOE OSTI2022

Distributed Wind Energy Futures Study [Slides]

17 WIND ENERGY↗