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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Wind power forecasting: IEA Wind Task 36 & future research issues

Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.

17 WIND ENERGY↗
DOE OSTI2019

Validation of Numerical Models of the Offshore Wind Turbine From the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on a Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) Idling below the cut-in wind speed; (2) Rotor-nacelle assembly (RNA) rotation maneuver below the cut-in wind speed; (3) Power production below and above the rated wind speed; and (4) Shutdown. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) Strains and accelerations recorded on the support structure; (2) Pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools. Therefore, strains were calculated based on out-of-plane bending moments, axial forces, and cross-sectional properties of the structural members. Also, a number of issues arose during the validation: (1) The need for a thorough quality check of sensor measurements; (2) The sensitivity of the turbine loads to the controller and airfoil properties, which were only approximated in the modeling approach; (3) The importance of estimating and applying an appropriate damping value for the structure; and (4) The importance of wind characteristics beyond turbulence on the loads. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, probability density functions of strains and accelerometers. A good match was achieved between the measurements and models set up by OC5 Phase III participants.

17 WIND ENERGY↗
DOE OSTI2016

IEA Wind Task 26: Offshore Wind Farm Baseline Documentation

This document has been produced to provide the definition and rationale for the Baseline Offshore Wind Farm established within IEA Wind Task 26--Cost of Wind Energy. The Baseline has been developed to provide a common starting point for country comparisons and sensitivity analysis on key offshore wind cost and value drivers. The baseline project reflects an approximate average of the characteristics of projects installed between 2012 and 2014, with the project life assumed to be 20 years. The baseline wind farm is located 40 kilometres (km) from construction and operations and maintenance (O&M) ports and from export cable landfall. The wind farm consists of 100 4-megawatt (MW) wind turbines mounted on monopile foundations in an average water depth of 25 metres (m), connected by 33-kilovolt (kV) inter-array cables. The arrays are connected to a single offshore substation (33kV/220kV) mounted on a jacket foundation, with the substation connected via a single 220kV export cable to an onshore substation, 10km from landfall. The wind farm employs a port-based O&M strategy using crew-transfer vessels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Development of a Wind Plant Large-Eddy Simulation with Measurement-Driven Atmospheric Inflow: Preprint

This paper details the development of an aeroelastic wind plant model with large-eddy simulation (LES). The chosen LES solver is the Simulator for Wind Farm Applications (SOWFA) based on the OpenFOAM framework, coupled to NREL's comprehensive aeroelastic analysis tool, FAST. An atmospheric boundary layer (ABL) precursor simulation was constructed based on assessments of meteorological tower, lidar, and radar data over a 3-hour window. This precursor was tuned to the specific atmospheric conditions that occurred both prior to and during the measurement campaign, enabling capture of a night-to-day transition in the turbulent ABL. In the absence of height-varying temperature measurements, spatially averaged radar data were sufficient to characterize the atmospheric stability of the wind plant in terms of the shear profile, and near-ground temperature sensors provided a reasonable estimate of the ground heating rate describing the morning transition. A full aeroelastic simulation was then performed for a subset of turbines within the wind plant, driven by the precursor. Analysis of two turbines within the array, one directly waked by the other, demonstrated good agreement with measured time-averaged loads.

17 WIND ENERGY↗
DOE OSTI2021

Valuation of Wind Energy Turbines Using Volatility of Wind and Price

The limitedness of the nonrenewable local energy resources in Israel, even in the background of the later gas fields’ findings, continues to force the state to devote various efforts towards ‘green’ energy development. These efforts include installations, both for the solar and for wind energy, thus improving the diversity of energy sources. While the standard discounted cash flow (DCF) method using the net present value (NPV) criterion is extensively adopted to evaluate investments, the standard DCF method is inappropriate for the rapidly changing investment climate and for the managerial flexibility in investment decisions. In recent years, the real options analysis (ROA) technique has been widely applied in many studies for the valuation of renewable energy investment projects. Taking into account the above background, we apply, in this study, the real options analysis approach for the valuation of wind energy turbines and apply it to the analysis of wind energy economic potential in Israel, which is the context of our work. We hypothesize that due to nature of wind energy production uncertainties, the ROA method is better than the alternative. The novelty of this paper includes the following: real world wind statistics of the Merom Golan site in Israel (velocity 3.73 m/s, with a standard deviation of 2.03 m/s), a realistic power generation estimation (power generation of 1205.84 kW with a standard deviation of about 0.5% in annual value which is worth about 1.3 M$ per annum), and an economic model to evaluate the profitability of such a project. We thus discuss the existing challenges of diversifying renewable energy sources in Israel by adding wind installations. Our motivation is to introduce a method which will allow investors and officials to take into account uncertainties when deciding in investing in such wind installations. The outcomes of the paper, which are obtained using the method of Weibull statistics and the Black–Scholes ROA technique, include the result that market price volatility adds to the uncertainties much more than any wind fluctuations, provided that the analysis is integrated over a long enough time.

17 WIND ENERGY↗
DOE OSTI2025

Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign

In wind energy research, scientific challenges are often associated with complex terrain sites, where orography, vegetation, and buildings disrupt flow uniformity. However, even sites characterized as simple terrain can exhibit significant spatial variability in wind speed, particularly during stable boundary layers (SBLs) and low-level jets (LLJs). This study investigates these terrain interactions using both simulations and observations from the American WAKe ExperimeNt (AWAKEN). We employ a multiscale Weather Research and Forecasting (WRF) model simulation, integrating mesoscale forcing in the coarse domains and representing three rows of turbines from the King Plains wind farm as generalized actuator disks (GAD) in the large-eddy simulation (LES) domains. During a nocturnal LLJ event on 3 April 2023, the downstream, wake-affected turbine rows outperformed the upstream, unwaked row by 25 %–51 %. This counterintuitive result arises from terrain-induced streamwise variations in hub-height wind speed of approximately 4 m s −1 over 5 km – equivalent to ∼50 % of the upstream reference speed. This enhancement outweighs the wake-induced reduction in mean wind speed (∼12 %) and global blockage effects reported in the literature (∼1 %–3.4 %). The multiscale simulations capture the intra-farm spatial variability in power performance observed in SCADA data. Terrain-induced vertical displacement of the LLJ, coupled with large wind shear below the jet maximum, drives the substantial streamwise acceleration within the wind farm. These findings underscore the importance of accounting for spatial variability related to terrain, even in simple landscapes, particularly during LLJ conditions. Incorporating such effects into reduced-order modeling frameworks for wind farm design and control could significantly enhance their effectiveness.

17 WIND ENERGY↗
DOE OSTI2019

Validation of Numerical Models of the Offshore Wind Turbine from the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III: Preprint

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project, is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) Idling below the cut-in wind speed; (2) Rotornacelle assembly (RNA) rotation maneuver below the cut-in wind speed; (3) Power production below and above the rated wind speed; and (4) Shut-down. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) Strains and accelerations recorded on the support structure; (2) Pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools. Therefore, strains were calculated based on out-of-plane bending moments, axial forces, and geometrical properties of the structural members. Also, a number of issues arose during the validation: (1) The need for a thorough quality check of sensor measurements; (2) The sensitivity of the turbine loads to the controller and airfoil properties, which were only approximated in the modeling approach; (3) The importance of estimating and applying an appropriate damping value for the structure; and (4) The importance of wind characteristics beyond turbulence on the loads. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms (DFTs), power spectral densities (PSDs), probability density functions (PDFs) of strains and accelerometers. A good match was achieved between the measurements and models set up by OC5 Phase III participants.

17 WIND ENERGY↗
DOE OSTI2020

Grand Challenges in the Science of Wind Energy

The energy system of the future that depends on 80% or more renewable energy will put new demands on the wind systems that support it. There are significant areas of scientific uncertainly that need to be addressed to enable the innovation required for success. This presentation, based on a 2019 article in Science, lays out those areas of scientific challenges and proposes an integrated wind energy discipline that encompasses the needed research.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2016

Improving Wind Predictions in the Marine Atmospheric Boundary Layer through Parameter Estimation in a Single-Column Model

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this study, we use the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts.

17 WIND ENERGY↗
DOE OSTI2019

Economic Impacts from Wind Energy in Colorado Case Study: Rush Creek Wind Farm

This economic impact analysis of the Rush Creek Wind Farm 1) quantitatively modeled the gross economic impacts at the Colorado state-level and four-county region and 2) engaged with community members to draw specific insight on how wind development impacts their local economy. This study also provides useful insights on the economic impacts supported by Colorado's wind energy manufacturing and supply chain. The Rush Creek Wind Farm was Colorado's largest wind energy project in 2018. Jobs, gross domestic product, gross economic output, and earnings were estimated using the National Renewable Energy Laboratory's Jobs and Economic Development Impact (JEDI) model. Researchers also engaged with communities near the Rush Creek Wind Farm to collect empirical economic data as well as first-hand knowledge of community-level impacts during wind farm construction. The case studies and qualitative research in this report provide context for the quantitative JEDI model results, describing the economic impacts to rural communities.

17 WIND ENERGY↗
DOE OSTI2025

The influence of wind veer and drivetrain flexibility on fatigue loading for large floating wind turbines

To reduce costs, offshore wind turbines are expected to be designed with significantly increased rotor diameters. Larger turbines become more flexible and span a larger portion of the atmospheric boundary layer. With these changes, the validity of traditional modeling assumptions should be investigated. This work challenges two common assumptions: (1) that the drivetrain can be considered rigid (except in torsion) and does not couple with the rotor and tower and 2) that wind directional change with height (veer) does not greatly influence the fatigue damage in the tower, blades and drivetrain. Two large semi-submersible floating wind turbines are considered: a 15 and a 22 MW reference turbine. Both use direct-drive generators. Aero-hydro-servo-elastic simulations are performed using OpenFAST, with drivetrain bending flexibility and main bearing response implemented in the coupled analysis. The turbines are subjected to a set of load cases at below-, near- and above-rated mean wind speeds, assembled based on the 3 km Norwegian reanalysis (NORA3) hourly wind and wave hindcast data for Utsira Nord, off the coast of Norway. In each load case, conditions with and without veer are simulated to evaluate the influence of veer on damage equivalent loads (DELs) of the turbine tower, blades and main bearings. Further, these load cases are applied to evaluate the influence of drivetrain flexibility on aero-elastic turbine response. The results indicate that, depending on the veer gradient, mean wind speed, operating regime and turbine size, veer can be very important for tower-top DELs and the fluctuations of main bearing radial loads, while main bearing and blade-root flapwise DELs are less affected. Considering these specific load cases and turbine models, drivetrain flexibility is found to significantly influence tower-top DELs of the largest turbine: the tower-top fore-aft and torsional damage equivalent moments of the 22 MW turbine are reduced by more than 20 % at near-rated wind speeds when the drivetrain is modeled as flexible.

17 WIND ENERGY↗
DOE OSTI2019

Optimizing wind farm siting to reduce power system impacts of wind variability

We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics—forward and backward greedy algorithms—to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. Here, we find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm. Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset.

17 WIND ENERGY↗
DOE OSTI2016

IEA Task 32: Wind Lidar Systems for Wind Energy Deployment (LIDAR)

Under the International Energy Agency Wind Implementing Agreement (IEA Wind) Task 11, researchers started examining novel applications for remote sensing and the issues around them during the 51st topical expert meeting about remote sensing in January 2007. The 59th topical expert meeting organized by Task 11 in October 2009 was also dedicated to remote sensing, and the first draft of the Task's recommended practices on remote sensing was published in January 2013. The results of the Task 11 topical expert meetings provided solid groundwork for a new IEA Wind Task 32 on wind lidar technologies. Members of the wind community identified the need to consolidate the knowledge about wind lidar systems to facilitate their use, and to investigate how to exploit the advantages offered by this technology. This was the motivation that led to the start of IEA Wind Task 32 'Lidar Application for Wind Energy Deployment' in November 2011. The kick-off was meeting was held in May 2012.

17 WIND ENERGY↗
DOE OSTI2017

Improved Estimates of Moments and Winds from Radar Wind Profiler

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) operates nine radar wind profilers (RWP) across its sites. These RWPs operate at 915 MHz or 1290 MHz frequency and report the first three moments of the Doppler spectrum. The operational settings of the RWP were modified in summer, 2015 to have single pulse length setting for the wind mode and two pulse length settings for the precipitation mode. The moments data collected during the wind mode are used to retrieve horizontal winds. The vendor-reported winds are available at variable time resolution (10 mins, 60 mins, etc.) and contain a significant amount of contamination due to noise and clutter. In this data product we have recalculated the moments and the winds from the raw radar Doppler spectrum and have made efforts to mitigate the contamination due to instrument noise in the wind estimates. Additionally, the moments and wind data has been reported in a harmonized layout identical for all locations and sites.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Wind and Weather Variability within the Californian Offshore Wind Energy Areas

Weather variability over the Northeast Pacific (NEP) region and its influence on wind resources within the Californian offshore wind energy areas (WEAs) at Humboldt and Morro Bay are characterized using 20-years reanalysis model and satellite data. The hub-height (180 m) winds at both locations are predominantly northwesterly driven by the NEP high pressure system, with strong coastal gradients in surface pressure, fluxes, planetary boundary layer (PBL) depths and cloudiness. These sharp coastal gradients and strong annual cycles of temperature and moisture advections pose potential challenges in accurately modeling the local wind resource. Hub-height wind speeds and power capacity factors significantly vary for different regimes of PBL depths, surface fluxes and rain area fractions. This highlights the importance of studying the physical mechanisms driving these weather regimes, hence our analysis of how large-scale NEP weather variability drives the local meteorology at the WEAs. Furthermore, at both WEAs, PBL tops and cloud boundaries intersect the rotor layer (80-280 m) more than 30% and 20% of the time, respectively. While PBL depths significantly modulates hub-height winds and power, cloud boundaries do not have a similar impact, likely due to reanalysis errors in simulating cloud boundaries accurately. These findings underscore the challenges in deploying tall wind turbines in shallow cloudy boundary layers, where the interaction between clouds, precipitation, and atmospheric layers can impact turbine efficiency. As turbines grow taller and are deployed in more complex meteorological conditions, understanding these interactions is crucial for improving wind power forecasting and optimizing energy production in coastal regions.

17 WIND ENERGY↗
DOE OSTI2016

THE NEW HORIZONS SOLAR WIND AROUND PLUTO (SWAP) OBSERVATIONS OF THE SOLAR WIND FROM 11–33 au

The Solar Wind Around Pluto (SWAP) instrument on National Aeronautics and Space Administration's New Horizons Pluto mission has collected solar wind observations en route from Earth to Pluto, and these observations continue beyond Pluto. Few missions have explored the solar wind in the outer heliosphere making this dataset a critical addition to the field. We created a forward model of SWAP count rates, which includes a comprehensive instrument response function based on laboratory and flight calibrations. By fitting the count rates with this model, the proton density (n), speed (V), and temperature (T) parameters are determined. Comparisons between SWAP parameters and both propagated 1 au observations and prior Voyager 2 observations indicate consistency in both the range and mean wind values. These comparisons as well as our additional findings confirm that small and midsized solar wind structures are worn down with increasing distance due to dynamic interaction of parcels of wind with different speed. For instance, the T–V relationship steepens, as the range in V is limited more than the range in T with distance. At times the T–V correlation clearly breaks down beyond 20 au, which may indicate wind currently expanding and cooling may have an elevated T reflecting prior heating and compression in the inner heliosphere. The power of wind parameters at shorter periodicities decreases with distance as the longer periodicities strengthen. The solar rotation periodicity is present in temperature beyond 20 au indicating the observed parcel temperature may reflect not only current heating or cooling, but also heating occurring closer to the Sun.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Early GRB afterglows from reverse shocks in ultra-relativistic, long-lasting winds

We develop a model of early Gamma-Ray Burst (GRB) afterglows with dominant X-ray contribution from the reverse shock (RS) propagating in highly relativistic (Lorentz factor γ{sub w} ∼ 10{sup 6}) magnetized wind of a long-lasting central engine. The model reproduces, in a fairly natural way, the overall trends and yet allows for variations in the temporal and spectral evolution of early optical and X-ray afterglows. The high energy and the optical synchrotron emission from the RS particles occurs in the fast cooling regime; the resulting synchrotron power L{sub s} is a large fraction of the wind luminosity, L{sub s}≈L{sub w}/√(1+σ{sub w}) (L{sub w} and σ{sub w} are wind power and magnetization). Thus, plateaus—parts of afterglow light curves that show slowly decreasing spectral power—are a natural consequence of the RS emission. Contribution from the forward shock (FS) is negligible in the X-rays, but in the optical both FS and RS contribute similarly: FS optical emission is in the slow cooling regime, producing smooth components, while RS optical emission is in the fast cooling regime, and thus can both produce optical plateaus and account for fast optical variability correlated with the X-rays, e.g., due to changes in the wind properties. We discuss how the RS emission in the X-rays and combined FS and RS emission in the optical can explain many puzzling properties of early GRB afterglows.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Dependence of wind-farm-induced gravity waves and wind farm performance on non-dimensional atmospheric parameters and simulation configuration

This large-eddy simulation (LES) study examines how wind-farm-induced atmospheric gravity waves (AGWs) and wind farm performance depend on non-dimensional atmospheric parameters and simulation configuration. A hypothetical aligned wind farm of actuator disks is simulated under neutral surface conditions, with a stable capping inversion and a mildly stable free atmosphere, to assess the effects of stratification beyond the atmospheric boundary layer (ABL) on ABL flow. Simulation set-ups fully resolving AGWs are validated to minimize spurious wave generation and reflection from the domain boundaries. The validated set-up is then used to analyze AGW types and characteristics, as well as stratification impacts under conventionally neutral boundary layer (CNBL) conditions. These conditions are governed by four non-dimensional parameters: the Froude numbers of the free atmosphere and capping inversion (Fr, Fr i ), and the aspect ratios of the ABL and wind farm (H̃ i , S h ). Simulation configurations that fully resolve AGWs – capturing at least one wavelength both horizontally and vertically – yield the most realistic stratification effects on ABL flow, whereas partial or unresolved configurations produce non-physical, channel-like behavior. A coherent description of the AGW phenomena is provided, highlighting the central role of capping inversion displacement in linking ABL fluctuations with AGWs. Trapped waves are confined within the capping inversion, while interfacial and internal waves aloft are identified as the AGW types most relevant to wind farm performance. The wavy inversion, analogous to an interfacial wave, forms converging and diverging zones that drive power fluctuations across the farm. The interfacial wavelength, measured over the wind farm, corresponds to one diverging, one converging, and one mildly diverging zone. As the interfacial wavelength decreases with Fr i , multiple convergence–divergence zones develop under sub-critical conditions (Fr i <1.0), while for super-critical conditions (Fr i > 1.0), the wavelength approaches the farm length. Wave amplitude increases with decreasing H̃ i (i.e., shallower capping inversions). Wind farm performance is most sensitive to H̃ i : shallow boundary layers increase blockage and reduce efficiency, while deeper layers enhance efficiency. Increasing Fr and Fr i mitigates blockage, and increasing S h mainly improves wake recovery. Although local power fluctuations arise from AGWs, overall wind farm efficiency remains nearly constant with Fr and Fr i , improving primarily with larger H̃ i and S h .

17 WIND ENERGY↗