AWAKEN will build on previous field campaigns for wind energy in order to extend the current understanding of the interaction of onshore wind plants with the atmospheric boundary layer.
eXperimental Planetary boundary layer Instrumentation Assessment (XPIA)
To assess capabilities for quantifying features of the complex flow in and near wind farms, DOE sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign (Lundquist et al. 2017). The experiment was conducted from March 2 to May 31, 2015 at the Boulder Atmospheric Observatory (BAO), located ~25 km east of the eastern slopes of the Rocky Mountains, ~25 km north of downtown Denver, Colorado, and ~20 km east northeast of Boulder, Colorado, at an elevation of 1,584 meters above sea level. The spring season offers a range of wind speed, direction, and precipitation conditions to challenge the instrumentation. XPIA was supplemented by the National-Science-Foundation-sponsored “Characterizing the Atmospheric Boundary Layer” educational outreach project, which provided in situ sensors such as radiosonde launches, 12 sonic anemometers deployed on the 300-m tower, and two surface flux stations, as well as opportunities to engage students from middle school through graduate studies. In addition to deploying state-of-the-art wind scanning remote-sensing technology, such as Ka-band radars and scanning lidars, the XPIA team developed and tested multi-Doppler scanning techniques for comparison to the in situ instrumentation. To quantify the uncertainty of these new types of measurements, results of these scans and retrievals are compared to standard measurements as well as to profiles from profiling lidars. In addition, radiosonde launches, along with temperature and moisture profiles from the tower, provide verification data for assessing microwave radiometer estimates of atmospheric stability. The collected data are archived for public use at the A2e Data Archive and Portal (https://a2e.energy.gov/data).
Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range, but provide more data during nonprecipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as RASS. Using a motion platform, motion-compensation algorithms for lidars to be mounted on offshore platforms were assessed. Cases for validation of mesoscale or large-eddy simulations were identified, providing information on accessing the archived data set. Modern remote-sensing systems provide a generational improvement in observational capabilities, enabling resolution of fine-scale processes critical to understanding inhomogeneous boundary-layer flows.
Sandia National Laboratories and NREL will jointly execute a two-phase experimental campaign on wind farm controls and wake characterization at the SWiFT facility in 2016 and 2017. The goal of the experiment is to demonstrate the capability of wake steering control to improve total wind turbine array power production.
In Phase I, an offset controller is applied to the SWiFT turbine WTGa1. This controller applies an offset to a nacelle-based wind direction sensor used to align the turbine to the wind direction to achieve a prescribed misalignment to the wind. Wake position data will be collected under multiple yaw misalignment angles and inflow conditions as simulated prior to the experiment using the Simulator fOr Wind Farm Applications (SOWFA) code. These data will be used to both verify the ability to steer the turbine wake at the SWiFT facility and to develop a look-up table for the FLOw Redirection and Induction in Steady-State (FLORIS) control model to be implemented in the Phase II controller.
In Phase II, the offset controller on WTGa1 is replaced by a wake steering controller that operates in a similar fashion to the offset controller. However, it uses a look-up table based on the FLORIS model to prescribe offsets to produce a desired wake steering amount based on the inflow parameters. The wake deflection will be verified by the scanning lidar along with turbine performance data (loads and power) for both the WTGa1 and WTGa2 turbines. The collected
The following GPS time-synchronized instrumentation supported this experiment:
- SWiFT wind turbines. The research-grade turbines known as WTGa1 and WTGa2 will be the primary instrumented research turbines to collect power and loads data. The upwind WTGa1 turbine will be enabled with the yaw-based wake steering controller.
- SWiFT meteorological tower. Upwind from the WTGa1 turbines is the meteorological tower METa1 with instrumentation that characterizes the inflow at multiple heights from below the rotor to above the rotor plane.
- DTU SpinnerLidar. A customized scanning lidar that is installed in the upwind WTGa1 turbine nacelle and faces downwind to characterize the wake at multiple ranges (Figure 21).
- Pentalum SpiDARs. Six vertical profiling lidars measuring the flow at 10 heights ranging between 20 and 65 m deployed in multiple configurations near the METa1 tower and in the wake of the WTGa1 turbine.
- Windar 4-beam Lidar. A nacelle-mounted lidar with four beams focused 40 m downwind of the WTGa1 turbine.
National Rotor Testbed (NRT)
The National Rotor Testbed (NRT) is an experimental platform that is used to study wind turbine wake physics at a scale that is both affordable and relevant to utility-size turbines. The rotor creates a scaled wake by having the same dimensionless circulation, induction, and thrust coefficient as a GE 1.5sle rotor. Instrumentation planned for the NRT include a suite of aerodynamic and structural sensors including five-hole pitot probes, pressure taps, tufts, strain gauges, and accelerometers.
Five-hole pitot probes will be used to measure relative flow angles and inflow velocities across the blade span during operation. From these probes, axial induction and tangential induction can be calculated to verify that the NRT blade performs consistently with its design. Induction is also important for describing the near-wake of a wind turbine. From the measured inflow angle and the known blade twist, the angle of attack at any blade station can be calculated. Angle of attack is useful to verify two-dimensional airfoil performance of the design, and for validation of simulation codes. Pressure taps at a few blade stations will be used to measure the static pressure around the airfoil sections. From pressure, lift and form drag can be directly calculated. These measurements are important for validating blade-resolved simulations, in which the flow around the blade surface is modeled. The pressure coefficient can also be calculated from the pressure taps and will be used to verify the airfoil polars used in the NRT design.
Tufts of string will be adhered to the root region of the blade and used as a qualitative flow visualization technique. Tufts lying parallel to the blade surface show an attached boundary layer, and oscillating, lifted tufts indicate the boundary layer has separated. This spatial distribution of tufts will be used to verify the separation and stall characteristics of the NRT design. The tufts can also be used to validate codes because the extent of separation is difficult for simulation codes to ensure accurate predictions, especially in three-dimensional and rotating flows.
Strain gages were installed to measure the flap and edge strains in the blade skin 200 mm from the blade root. At three additional blade stations (3,250 mm, 6,500 mm, and 9,750 mm) strains are only measured in the flap-wise bending direction. This configuration allows for integrated loads and torques to be quantified in the NRT blade. Strain gages are prone to temperature drift, therefore temperature sensors are included and located at 200, 4,875, and 9,750 mm.
Two accelerometers measure NRT blade acceleration in three orthogonal directions at 8,060 mm and 11,440 mm locations relative to the blade root. The first and second derivative of acceleration is blade velocity and displacement, respectively, which can also be calculated to understand the dynamic and elastic behavior of the blade. Modal analysis of the beam structure showed that these two sensor locations best capture the highest energy and lowest frequency structural modes of vibration.
Crop-Wind Energy Experiments (CWEX)
The series of Crop-Wind Energy Experiments (CWEX) (Rajewski et al. 2013; Rhodes and Lundquist 2013; Lundquist et al. 2014; Rajewski et al. 2014; Takle et al. 2014; Vanderwende et al. 2015; Rajewski et al. 2016, Bodini et al. 2017), which took place in a 200-MW wind farm in central Iowa, sought to measure interactions between wind turbines, the atmosphere, and the microclimate of intensive agriculture. The Story County I and II wind farms experience strong diurnal cycles of atmospheric stability and frequent nocturnal low-level jets. The area has flat topography, primarily devoted to large fields of corn (height 1-2 m) and soybeans (height 0.3-0.8 m). The region also has four small villages, some riparian areas, and a few trees and buildings associated with farmsteads.
The CWEX-10 experiment, highlighted in Rajewski et al. (2013) and discussed in detail in Rajewski et al. (2016) primarily consisted of surface flux station measurements taken between 2.5D upwind and 20D downwind of two rows of turbines to investigate impacts of wind turbine wakes on surface wind speed, temperature, fluxes, and turbulence. The CWEX-11 again employed surface flux stations between 2D and 10D from a row of turbines, but also employed wind-profiling lidars (Windcube v1 lidars) upwind and downwind from the row of turbines to quantify turbine wakes (Rajewski et al. 2013; Rhodes and Lundquist 2013). CWEX-13 expanded the scope of interest by including a scanning lidar (Windcube 200S), a microwave radiometer, several profiling lidars, and surface flux stations to explore the role of stability-driven phenomena like nocturnal low-level jets on wake interactions with the atmosphere and with each other (Lundquist et al. 2014; Takle et al. 2014; Vanderwende et al. 2015). Scanning lidar measurements suggest that the wakes from turbines at the outer edge of a row are fundamentally different from wakes from inner turbines because outer wakes expand faster and have smaller velocity deficits (Bodini et al. 2017).
Full-Scale Wake Testing
Numerous entities worldwide are engaged in full-scale wake testing using different technologies. Researchers have used ground-based research-grade lidars to observe turbine wakes (Aitken et al. 2014), whereas others have used ground-based commercial lidars (Rajewski et al. 2013), radar systems (Hirth et al. 2012), nacelle-mounted lidars (Aitken and Lundquist 2014; Gallacher and More 2014) advanced optical techniques (Hong et al. 2014), and even unmanned aerial vehicles (Kocer et al. 2012). The goal of each of these studies is to gain a better understanding of wind turbine wakes that greatly influence wind farm performance and reliability.
Recently, DOE funded tests on a single 1.5-MW wind turbine located at the NWTC through a partnership between NREL and the University of Stuttgart. NREL and DOE are providing the turbine testing platform and control access, while the University of Stuttgart is providing their scanning lidar and analysis capabilities (see Figure 23). In this study, researchers are interested primarily in the wake behavior influenced by different atmospheric and turbine operating conditions. In particular, researchers are interested in the difference between wind turbine wakes when the turbine is operating under normal operating settings, versus those in which the turbine is yawed relative to the incoming flow, up to 25 degrees from the predominant wind direction (Fleming et al. 2017). It has been shown that yawing a turbine steers the wake away from downstream turbines and can be an effective control method for improving wind farm performance. The data are also useful for validating modeling tools of varying fidelity and testing the similarity of subscale wake tests, such as those performed at SWiFT, that are being used to better understand wake interactions under the DOE A2e program.
Observations of wind speeds and other atmospheric quantities are performed through research- grade instrumentation installed on a 135-m meteorological tower at the NWTC (Clifton et al. 2013b). Wakes are tracked using the Stuttgart lidar at four different ranges, from 1 to 3D downstream of the operating turbine. Turbine operating conditions are observed through an augmented suite of sensors. Tests ran through the spring of 2018, with validation studies and comparisons to similar measurements at SWiFT to be published thereafter.
Wind Forecasting Improvement Project (WFIP)
The Wind Forecasting Improvement Projects are collaborations between DOE, the National Oceanic and Atmospheric Administration (NOAA), and awardees of competitive funding. The projects were intended to foster improvement in the foundational forecasts prepared by NOAA and used by the wind energy industry either directly or incorporated into third-party products.
The first Wind Forecasting Improvement Project (WFIP) ran from 2011 to 2012 and focused on the initialization problem by examining the impact that assimilating enhanced observations has on the accuracy of wind turbine hub-height forecasts in Texas and the northern U.S. Great Plains (North and South Dakota) (Wilczak et al. 2015). Three data sets were assimilated: special WFIP remote-sensing observations (including RWP and lidar data), tall tower wind speed measurements, and turbine nacelle anemometer observations. The combination of these three datasets reduced the root mean-squared error (RMSE) for wind power forecasts averaged over the first 6 forecast hours by 3% (Wilczak et al. 2015).
In 2015, observations commenced for the second Wind Forecast Improvement Project (WFIP2). WFIP2 sought to improve the representation of complex terrain boundary-layer physics and related processes in mesoscale models for improved wind power forecasts. The WFIP2 integrated observational and modeling study involved the wind industry, universities, NOAA, and DOE’s national laboratories. Observations spanned 18 months in and around the Columbia Basin of eastern Washington and Oregon; the observations are archived at the DOE A2e DAP (https://a2e.energy.gov/projects/wfip2). Telescoping nests of observational systems captured important atmospheric scales from the mesoscale (~ 400 km) to the numerical weather prediction model subgrid scale (the 2-km-by-2-km “Physics Site”). The WFIP2 study region extended from the mouth of the Columbia River around 200 km to the north and south, and 400 km west. Model improvements were tested in NOAA’s High-Resolution Rapid Refresh (HRRR) model. This focus facilitated the rapid WFIP2 model improvements into operational forecast models. More detail on the WFIP2 is provided in an overview (Shaw et al. 2019) and a focused discussion of the observational campaign (Wilczak et al. 2019).
The Askervein Hill experiments (Taylor and Teunissen 1987), conducted in 1982 and 1983, documented flow on and around the Askervein Hill, a 116-m elevation hill on the west coast of the island of South Uist in the Outer Hebrides, Scotland. The hill was isolated in all wind directions except the northeast-east sector. To the southwest, the fetch was flat and uniform for 34 km toward the coastline, with sand dunes and low cliffs. The hill had a uniform surface roughness of 0.03 m. The Askervein Hill project collected velocity and turbulence data, using over 50 meteorological towers with a total of 28 sonic anemometers, thereby providing a unique data set for comparison to numerical simulations. At the time, Askervein represented a significant advancement in measurements of flow over a hill: 35 of the masts were 10-m masts equipped with a cup anemometer to measure the mean flow. Vertical profiles were measured with taller masts at a reference point upstream, at the hill top, and at the center point.
Askervein Hill has become a standard test case for flow modeling (Raithby et al. 1987; Kim and Patel 2000; Lopes and Palma 2002; Castro et al. 2003; Undheim et al. 2006; Lopes et al. 2007; Chow and Street 2009).
New European Wind Atlas (NEWA) and Perdigão
The New European Wind Atlas is an ongoing multiyear (20142019), €13M project to reduce the uncertainty in wind resource assessment. Several field campaigns contribute to the New European Wind Atlas, sampling a range of flow conditions from complex terrains (mountains and forests), offshore, large changes in surface characteristics (roughness change), and cold climates (Mann et al. 2017).
One of the complex terrain campaigns, the Perdigão experiment in eastern Portugal, occurred from late 2016 through midsummer 2017 in the Vale Cobrão, a picturesque valley nestled within a double ridge located in central Portugal. The U.S. National Science Foundation’s Physical and Dynamical Meteorology Program sponsored a U.S.-based counterpart campaign. The pooled, coordinated measurement and modeling capabilities of European Union and U.S. investigators provided a holistic view of multiscale microscale processes in complex terrain to best capture diurnal flow variability, thermal circulation, turbine wake, and topographic effects (Fernando et al. 2019).
Perdigão collected a massive data set on microscales, covering an approximately 6-km-long, 2- km-wide swath of the Cobrão valley (Fernando et al. 2019). Vertically, the measurements extend to tens of kilometers, with dense measurements up to several kilometers above the ridge height of about 500 m measured from the plain. An important design consideration was to collect a rich set of data extending from the surface to 300 m above ground level, which is particularly relevant for wind turbines. The instrument array was unprecedentedly dense (Figure 24), including 50 flux towers with heights of 10100 m mounted with sonic anemometers (for mean flow, turbulence, and fluxes), thermistor arrays, rapid measurements of carbon dioxide and water vapor, and radiometers (incoming, outgoing, and net radiation); remote sensors for flow (sodars, lidars, and wind profilers) and temperature and/or humidity (microwave radiometers, RASS, atmospheric emitted radiance interferometer, water-vapor differential absorption lidar (DIAL), ceilometers, and radar wind profilers); tethered lifting (profiling) systems for fine-scale turbulence; micro- and nano-barographs; microphones for acoustics; and radiosonde launches. The lidar coverage is unique, with 28 scanning and profiling units operating to map the valley atmosphere in coordinated or autonomous modes. In a major shift from previous field studies, the mean and turbulent velocity fields were measured by six triumvirates of scanning Doppler lidars between a 25- and 75-m resolution over ~ 25-km hemispherical volumes. The scanning lidar datasets have already yielded insights into turbine wake behavior in complex terrain (Menke et al. 2018; Wildmann et al. 2018) as well as atmospheric recirculations in complex terrain (Menke et al. 2019). Perdigão publications will be collected at https://www.atmos-chem-phys.net/special_issue636_946.html.
In summary, the Perdigão project was a giant step forward in gathering massive data sets in complex terrain at the microscale using cutting-edge measurement platforms, probing down to a spatial resolution of 100 m and frequencies of tens of Hz. The outcomes will include improved wind energy and microscale physics in complex terrain, new model-usable parameterizations, and a “gold standard” data set for high-fidelity microscale simulations and forecasting. For more information, including data access, see: https://www.eol.ucar.edu/field_projects/perdigao.
More than 12 GW of wind turbines are currently deployed offshore, in European waters, often in close proximity to each other. Due to close proximity, wind farms can induce large wind farm wakes that result in power deficits (Nygaard et al. 2014) or odd accelerations (Nygaard and Hansen 2016). Using a specially equipped research aircraft, the WIPAFF team conducted in situ measurements of large wind farm wakes in 2016 and 2017. They measured wind, turbulence, temperature, and moisture impacts from large wind farms. In stable atmospheric conditions, the found wake lengths with wind speed deficits of 40% in excess of tens of kilometers downwind The first direct in situ measurements of the existence and shape of large wind farm wakes by a specially equipped research aircraft in 2016 and 2017 confirm wake lengths of more than tens of kilometres under stable atmospheric conditions, with maximum wind speed deficits of 40%, and enhanced turbulence (Platis et al. 2018, Siedersleben et al. 2018). These measurements were the first step in a large research project to describe and understand the physics of large offshore wakes using direct measurements, together with the assessment of satellite imagery and models.
Experiments at the Iowa Atmospheric Observatory (IAO)
Identically configured twin 120-m tall towers, one inside and one outside the Story II wind farm, were installed in the CWEX domain in 2016 to provide a climatology of microclimate variability from natural and forced scales of turbulence from single turbines wakes, consecutive turbines wakes, and from the deep field of the wind farm wake (Takle et al. 2019). Conditions are monitored from measurements of wind speed, wind direction, air temperature, relative humidity at six levels, and air pressure at two levels on each tower. Turbulence measurements from both 120-m towers were available in episodic events. Wind speed and turbulence intensity tower biases due to slight differences in terrain and moderate differences in vegetation were quantified to isolate the relative forcing of wind turbines and the bulk wake of the wind farm during the growing season. Higher daytime and nighttime temperatures (>0.5 K) were measured in the wind farm wake, whereas temperature differences from ambient were small (±0.3 K) in the single turbine wake. Effects of forced turbulence on atmospheric humidity were low in the day and highly variable at night according to the strength of stratification. Additional comparisons of humidity differences were evaluated for wet and dry growing seasons with variable field cover surrounding the towers. Characteristics of the afternoon and evening transitions within single turbine wakes and the wind farm wake were also determined for post-harvest periods.
One intensive observational period featured a high-resolution 10-m turbulence tower to provide turbulence characterization during the evening transition to a nocturnal boundary layer very close to a low roughness surface (z0=0.02 m stubble; z0=0.005 m snow). Another experiment explored turbulence scaling for stable stratification conditions above a soybean canopy. Supplemental detection of low-level jets and other mesoscale events were determined from sodar and pulsed lidar 40-200 m measurement profiles in proximity to one of the tall towers. A surface weather station also monitored baseline conditions below 5 m. More information on the IAO tower configuration, facility, and data is located at www.mesonet.agron.iastate.edu/projects/iao.