Saarblitz, Wind farm in HDR, flickr.com, creative commons by-nc-sa 2.0

Lillgrund Direction

Managed by

Scope

This document outlines three separate components of this benchmark.  It is the hope that the participant will simulate all three components, but the participant is free to simulate any or all of the components.

This benchmark is open to participants of the Wakebench project using wake and, possibly, atmospheric boundary layer models. This is based on the actual operational Lillgrund wind farm in which there are multiple turbines interacting within an array.  The benchmark aims to test a wake/atmospheric model to reproduce the power production observed at Lillgrund when wind is from a southwesterly, southeasterly, and northwesterly sector. 

For the Southwest case, the sector is centered upon 222° aligned with rows A-H in which there is 4.3 rotor diameter (D) spacing.  For the Southeast case, the sector is centered upon 120° aligned with rows 1-8 in which there is a 3.3 D spacing.  For the Northwest case, the sector is centered upon the 300° direction aligned again with rows 1-8, with flow coming from the opposite direction of the Southeast case, with a 3.3 D spacing.

Figure 1 shows the position of the Lillgrund wind farm with respect to water, land, and urban areas.  By comparing the Southwest case to the Southeast case, the winds of both experiencing a fairly long fetch over water and/or small amounts of land with the same low turbulence intensity, the effects of turbine spacing can be examined.  By comparing the Southeast to the Northwest case, Southeast having a long fetch over water and a small amount of land, Northwest having wind blowing from the turbulence-inducing urban area ofCopenhagen, the effects of turbulence intensity can be examined.  By simulating wind directions over a sector, partial wake effects and lateral wake merging can be examined in addition to direct waking in the “down-the-row” case.

Figure 1   A map showing the position of Lillgrund (from Bergström, 2009)

Objectives

Demonstrate how wake models perform and capture the wake formation and merging process in the presence of atmospheric shear and turbulence within a large modern wind farm composed of many modern multimegawatt turbines. 

Data Accessibility

These benchmarks are offered to participants of the IEA Task 31 Wakebench.

Input data

The benchmark conditions are summarized in Table 1 below followed by further explanation:

 

Table 1:  Matrix of conditions for the Southwest, Southeast, and Northwest benchmarks.

  • Location: 55.52° N, 12.78° E
  • Wind directions: Observed power production data will be provided in 5° wind direction increments with ±2.5° bin widths over a 30° wide sector centered on the row direction, so the participant should attempt to model each of these directions.  However, for high-computational-cost simulations, larger increments can be chosen.  In any case, the model should account for the fact that a ±2.5° bin width is used.
  • Hub height turbulence intensity:  This quantity is defined as ‹u’u’›1/2/U0 where ‹u’u’›1/2 is the streamwise velocity variance and U0 is the mean hub-height wind speed.
  • Surface roughness:  z0 = 0.0001 m unless a different value is needed by your simulation method to produce the desired hub-height turbulence intensity.
  • Hub height mean wind speed: 9.0 m/s.
  • Stability: Assume neutral stability because stability is not given by Dahlberg (2009) or Bergström (2009) and data is averaged over a long time period.
  • Turbine model:  Siemens SWT-2.3.93.
  • Rows to simulate:  The rows against which comparison will be made are given in Table 1; however, this should not preclude the participant from simulating more or all the rows if desired.  The rows listed are interior rows, and one row is a complete row and the other contains a “hole” where turbines were not installed.
    • The basic turbine characteristics of coefficient of power and thrust versus wind speed are given in the test case guide (Figure 2) and in the file SWT-2.3-93.txt. 
    • If a more detailed turbine model is used that requires blade and control system information, a detailed generic turbine characterization representative of the actual turbine and tuned to the Cp and CT data given in SWT-2.3-93.txt is given by Churchfield (2013).

Validation data

The validation data consists of average and standard deviation of power production of turbines in rows B and D and rows 3 and 5, normalized by the average of the average power production of the leading turbines.  An example of the validation from Dahlberg (2009) is shown in Fig. 2.

Figure 2  Example of average power data along a row from the Lillgrund wind plant.  The x-axis is wind direction and the y-axis is power relative to leading turbines from Dahlberg (2009).

Model runs

The participant should use a domain size sufficient for his or her model to work properly and devoid of spurious boundary effects.  It is up to the participant as to how to set up the run, either the rows of interest only, the entire wind farm, or just a subset of the farm.  Please run the “down-the-row” midsector case and as many cases as possible beyond that in 5° increments in the ±15° sector.  The observational data is binned in 5° widths, so the modeler should attempt to account for this and document the method for which bin width was accounted.  The simulation should be run for at least 10 minutes of simulation time past the time in which power production comes to an equilibrium state (or a quasi-equilibrium state for unsteady simulations).

Output data

Output data to be reported are statistical and based upon at least 10 minutes of simulation.

  • Report time-average and standard deviation of power production at each turbine simulated. 
  • Report the time-averaged velocity vector, turbulence intensity (based on variance along flow direction, and turbulent kinetic energy (based on all three velocity components) along the various lines shown below in Fig. 3. Use lines 1 and 2 for the Southwest case and lines 3 and 4 for the Southeast and Northwest cases.  Use a sampling resolution along these lines sufficiently fine to capture the details of the velocity/turbulent kinetic energy profiles.  All lines are horizontal and at hub height. The streamwise lines are offset ¼ D from the rotor center to the northwest or northeast.  These sampling lines as shown in Fig. 3, and a smaller portion of the line may be sampled if you use a smaller domain

Figure 3   Velocity and turbulent kinetic energy sampling line locations for Lillgrund benchmarks.

This describes the output data file format for submission.  Please follow this format as the benchmark manager will likely create a script to read all files and analyze the data.

Files  (southwest wind case)

  • Mean power data - Row B

LillgrundSW_UserID_ModelID_run#_powerMeanRowB.txt

A multiple column list of time-averaged dimensional power in megawatts (MW).  Each row (below the first row header) corresponds to a different wind direction simulated within the sector.  An example is shown below.

windDir(°), P_wt8(MW), P_wt9(MW), P_wt10(MW), P_wt11(MW), P_wt12(MW), P_wt13(MW), P_wt14(MW), P_wt15(MW)
207, 0.972, 0.961, 0.961, 0.959, 0.965, 0.976, 0.942, 1.226

217, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
222, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
227, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234

237, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
 

  • Mean power data - Row D

LillgrundSW_UserID_ModelID_run#_powerMeanRowD.txt

Same as above but for row D starting at turbine 24 and ending at turbine 30.

  • Standard deviation of power data - Row B

LillgrundSW_UserID_ModelID_run#_powerStdRowB.txt

A multiple column list of dimensional standard deviation power in megawatts (MW).  Each row (below the first row header) corresponds to a different wind direction simulated within the sector.  An example is shown below.

windDir(°), stdP_wt8(MW), stdP_wt9(MW), stdP_wt10(MW), stdP_wt11(MW), stdP_wt12(MW), stdP_wt13(MW), stdP_wt14(MW), stdP_wt15(MW)
207, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

217, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
222, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
227, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

237, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
 

  • Standard deviation of power data - Row D

LillgrundSW_UserID_ModelID_run#_powerStdRowD.txt

Same as above but for row D starting at turbine 24 and ending at turbine 30.

  • Velocity/turbulence intensity/turbulent kinetic energy data along sample line 1

LillgrundSW_UserID_ModelID_run#_prof1_windDir#.txt

A separate file is provided for each wind direction simulated and the “#” after “windDir” in the file name is replaced with the actual wind direction represented by the file.  Extract wind velocity (all three components), turbulence intensity (based on the variance of the component of the velocity along the mean wind direction), and turbulent kinetic energy (based on all three components of the velocity fluctuations) along sample lines 1 shown in Fig. 3.  Use sampling resolution needed to resolved details of the solution.  Each file will consist as many rows as there are sampling points in the sampling line and 6 columns of data.  Let the position of turbine 15 be (x=0, y=0).

x (m), y (m), u (m/s), v (m/s), w (m/s), TI,  k (m/s)
0, 0, 6.4, 3.2, 0.0, 0.054, 0.24
2, 2, 5.9, 2.9, -1.0, 0.073, 0.33,

1503, 1604, 4.3, 2.2, 0.1, 0.123, 0.45

  • Velocity/turbulence intensity/turbulent kinetic energy data along sample line 2

LillgrundSW_UserID_ModelID_run#_prof2_windDir#.txt

Same as above, but for sample line 2.  Let the position of turbine 30 be (x=0, y=0)

 

 

Files (southeast wind case)

  • Mean power data - Row 3

LillgrundSE_UserID_ModelID_run#_powerMeanRow3.txt

A multiple column list of time-averaged dimensional power in megawatts (MW).  Each row (below the first row header) corresponds to a different wind direction simulated within the sector.  An example is shown below.

windDir(°), P_wt3(MW), P_wt10(MW), P_wt18(MW), P_wt26(MW), P_wt33(MW), P_wt38(MW), P_wt43(MW), P_wt47(MW)
105, 0.972, 0.961, 0.961, 0.959, 0.965, 0.976, 0.942, 1.226

115, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
120, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
125, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234

135, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234

  • Mean power data - Row 5

LillgrundSE_UserID_ModelID_run#_powerMeanRow5.txt

Same as above but for row 5 starting at turbine 5 and ending at turbine 45.

  • Standard deviation of power data - Row 3

LillgrundSE_UserID_ModelID_run#_powerStdRow3.txt

A multiple column list of dimensional standard deviation power in megawatts (MW).  Each row (below the first row header) corresponds to a different wind direction simulated within the sector.  An example is shown below.

windDir(°), stdP_wt3(MW), stdP_wt10(MW), stdP_wt18(MW), stdP_wt26(MW), stdP_wt33(MW), stdP_wt38(MW), stdP_wt43(MW), stdP_wt47(MW)
105, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

115, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
120, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
125, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

135, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

  • Standard deviation of power data - Row 5

LillgrundSE_UserID_ModelID_run#_powerStdRow5.txt

Same as above but for row 5 starting at turbine 5 and ending at turbine 45.

  • Velocity/turbulence intensity/turbulent kinetic energy data along sample line 3

LillgrundSE_UserID_ModelID_run#_prof3_windDir#.txt

A separate file is provided for each wind direction simulated and the “#” after “windDir” in the file name is replaced with the actual wind direction represented by the file.  Extract wind velocity (all three components), turbulence intensity (based on the variance of the component of the velocity along the mean wind direction), and turbulent kinetic energy (based on all three components of the velocity fluctuations) along sample lines 1 shown in Fig. 3.  Use sampling resolution needed to resolved details of the solution.  Each file will consist as many rows as there are sampling points in the sampling line and 6 columns of data.  Let the position of turbine 3 be (x=0, y=0)

x (m), y (m), u (m/s), v (m/s), w (m/s), TI,  k (m/s)
0, 0, 6.4, 3.2, 0.0, 0.054, 0.24
2, 2, 5.9, 2.9, -1.0, 0.073, 0.33,

1503, 1604, 4.3, 2.2, 0.1, 0.123, 0.45

  • Velocity/turbulence intensity/turbulent kinetic energy data along sample line 4

LillgrundSE_UserID_ModelID_run#_prof4_windDir#.txt

Same as above, but for sample line 4.  Let the position of turbine 5 be (x=0, y=0)

Files  (northwest wind case):

  • Mean power data - Row 3

LillgrundNW_UserID_ModelID_run#_powerMeanRow3.txt

A multiple column list of time-averaged dimensional power in megawatts (MW).  Each row (below the first row header) corresponds to a different wind direction simulated within the sector.  An example is shown below.

windDir(°), P_wt3(MW), P_wt10(MW), P_wt18(MW), P_wt26(MW), P_wt33(MW), P_wt38(MW), P_wt43(MW), P_wt47(MW)
285, 0.972, 0.961, 0.961, 0.959, 0.965, 0.976, 0.942, 1.226

295, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
300, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234
305, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234

315, 0.987, 0.976, 0.965, 0.971, 0.974, 0.966, 0.945, 1.234

 

  • Mean power data - Row 5

LillgrundNW_UserID_ModelID_run#_powerMeanRow5.txt

Same as above but for row 5 starting at turbine 5 and ending at turbine 45.

  • Standard deviation of power data - Row 3

LillgrundNW_UserID_ModelID_run#_powerStdRow3.txt

A multiple column list of dimensional standard deviation power in megawatts (MW).  Each row (below the first row header) corresponds to a different wind direction simulated within the sector.  An example is shown below.

windDir(°), stdP_wt3(MW), stdP_wt10(MW), stdP_wt18(MW), stdP_wt26(MW), stdP_wt33(MW), stdP_wt38(MW), stdP_wt43(MW), stdP_wt47(MW)
285, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

295, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
300, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119
305, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

315, 0.123, 0.134, 0.125, 0.131, 0.127, 0.131, 0.121, 0.119

 

  • Standard deviation of power data - Row 5

LillgrundNW_UserID_ModelID_run#_powerStdRow5.txt

Same as above but for row 5 starting at turbine 5 and ending at turbine 45.

  • Velocity/turbulence intensity/turbulent kinetic energy data along sample line 3

LillgrundNW_UserID_ModelID_run#_prof3_windDir#.txt

A separate file is provided for each wind direction simulated and the “#” after “windDir” in the file name is replaced with the actual wind direction represented by the file.  Extract wind velocity (all three components), turbulence intensity (based on the variance of the component of the velocity along the mean wind direction), and turbulent kinetic energy (based on all three components of the velocity fluctuations) along sample lines 3 shown in Fig. 3.  Use sampling resolution needed to resolved details of the solution.  Each file will consist as many rows as there are sampling points in the sampling line and 6 columns of data.  Let the position of turbine 47 be (x=0, y=0)

x (m), y (m), u (m/s), v (m/s), w (m/s), TI,  k (m/s)
0, 0, 6.4, 3.2, 0.0, 0.054, 0.24
2, 2, 5.9, 2.9, -1.0, 0.073, 0.33,

1503, 1604, 4.3, 2.2, 0.1, 0.123, 0.45

 

  • Velocity/turbulence intensity/turbulent kinetic energy data along sample line 4

LillgrundNW_UserID_ModelID_run#_prof4_windDir#.txt

Same as above, but for sample line 4.  Let the position of turbine 45 be (x=0, y=0)

Remarks

References

Bergström, H., “Meteorological Conditions at Lillgrund,” [online report] Vattenfall Vindkraft AB, 6_2 LG Pilot Report, Mar. 2009, URL: http://www.vattenfall.se/sv/file/16_Meteorological_conditions.pdf_ 16614584.pdf  [cited 27 June 2012].

Churchfield, M. J., “Generic Siemens SWT-2.3-93 Specifications,” informal NREL document, 2013.

Dahlberg, J.-Å., “Assessment of the Lillgrund Wind Farm: Power Performance Wake Effects,” [online report] Vattenfall Vindkraft AB, 6_1 LG Pilot Report, Sept. 2009, URL:http://www.vattenfall.se/sv/file/15_Assessment_of_the_Lillgrund_ W.pdf_16596737.pdf  [cited 27 June 2012].

Jeppsson, J., Larsen, P.E., Larsson, Å.  “Technical Description of Lillgrund Wind Power Plant,” [online report] Vattenfall Vindkraft AB, 2_1 LG Pilot Report, Sept. 2008, URL:http://www.vattenfall.se/sv/file/2_Technical_Description_Lillgru_8459792.pdf_16611908.pdf  [cited 27 June 2012].