Flow modelling for wind farm clusters

Transcription

1 Flow modelling for wind farm clusters Gerald Steinfeld, Project Manager LES & wake modelling, ForWind Center for Wind Energy Research, Carl von Ossietzky Universität Oldenburg Davide Trabucchi, Jörge Schneemann, Lars Segelken ClusterDesign Workshop Bilbao, April 15 th, 2016

2 Aspects of flow modelling for offshore wind farm clusters in ClusterDesign Ambient flow Impact of adjacent wind farms Intra wind farm flow

3 Solution for the regional flow and wake modelling in the ClusterDesign project Ambient flow (including or not including adjacent wind farms): Numerically derived data (WRF) or Measured data (met mast) Time series of ambient conditions (wind speed, wind direction, shear, TI, Obukhov length, air density, turbine operational mode) Converter level Converter level Flow inside single wind farms and wind farm clusters: Simulation with FarmFlow (farm, cluster of farms) Analytical model for wind farm wakes in FLaP Simulation with FLaP-Ainslie (farm)

4 Solution for the regional flow and wake modelling in the ClusterDesign project Ambient flow (including or not including adjacent wind farms): Offers the possibility of monitoring a farm Flow inside single wind farms and wind farm clusters: Numerically derived data (WRF) Simulation with FarmFlow (farm, cluster of farms) or Measured data (met mast) Time series of ambient conditions (wind speed, wind direction, shear, TI, Obukhov length, air density, turbine operational mode) Analytical model for wind farm wakes in FLaP Converter level Converter level Simulation with FLaP-Ainslie (farm)

5 Final output of the models for regional flow and wake modelling At each wind turbine of the target wind farm the following information is provided: Power Local wind speed at hub height Horizontal and vertical shear across the rotor disk Local turbulence intensity Input for load modelling provided, see later talk presented by Dirk Steudel

6 Wake modelling for the design of new offshore wind farm clusters Wind and stability atlas (derived from WRF): Data base providing time series of wind speed, wind direction, air density, inverse Obukhov length, roughness length and PBL height for the period between 1993 and 2012 Time series of ambient conditions Converter level Converter level Simulation with FarmFlow (farm, cluster of farms) Analytical model for wind farm wakes in FLaP Simulation with FLaP-Ainslie (farm)

7 Wind and stability atlas I From downscaling with WRF 240 x 111 grid points, spatial resolution 2 km Atlas covers the German bight and the southern north sea 6 height levels between 50 and 150 m

8 Wind and stability atlas II Setup of WRF based on results of a sensitivity study for PBL schemes: MYNN, YSU, QNSE, MYJ Reference in sensitivity study: Data from met mast FINO 1, year 2007 Forschungs- und Entwicklungszentrum Fachhochschule Kiel GmbH

9 WRF sensitivity on PBL scheme: RMSE of wind speed 40 m 60 m 80 m all Blue: YSU Green: MYJ Yellow: QNSE Red: MYNN Suselj and Sood (2010): 2.36 ms -1 Pena et al (2011): 1.85 ms -1 Shimada et al. (2011): 1.63 ms -1 Ohsawa et al. (2012): 1.46 ms -1

10 WRF sensitivity on PBL scheme: correlation between measured and simulated wind speed Height YSU MYJ QNSE MYNN correlation coefficient WRF-FINO1 sonic 40 m m m correlation coefficient WRF-FINO1 cup 40 m m m m m m m

11 WRF sensitivity on PBL scheme: statistical parameters for wind direction All wind speeds: Statistical parameter YSU MYJ QNSE MYNN 80 m mean bias root-meansquare error correlation coefficient Only wind speeds > 3 ms -1 : Statistical parameter YSU MYJ QNSE MYNN 80 m mean bias root-meansquare error correlation coefficient

12 Final result of the sensitivity study MYNN scheme should be used for the calculation of the wind and stability atlas for the selected region

13 Contents of the wind and stability atlas WASA consists of 4 parts (total size roughly 1 TB): Part 1: Geographical positions of WASA grid points (ASCII) Part 2: Time-series files for each WASA grid point: , temporal resolution 10 minutes (NetCDF) Part 3: Annual statistics files providing information for the whole WASA domain (NetCDF) Part 4: Monthly statistics files providing information for the whole WASA domain (NetCDF)

14 WASA part 2: Time-series files Contents of time-series files: Parameters available at six levels ( m with increments of 20 m) Wind direction Wind speed Air density including humidity effects Air density neglecting humidity effects Turbulence intensity Additional parameters Inverse Obukhov length Roughness length Planetary boundary layer height Used to provide input to wake models Flap and FarmFlow

15 WASA part 3: Annual statistics files Contents of annual statistics files (6 levels): - About 60 different parameters - E.g. means, minimum and maximum values - Frequency of parameters being in certain ranges, e.g. wind speed below 3 ms -1

16 WASA part 3: Wind speed at z=90 m averaged over 20 years > 10 ms -1 9,75-10 ms -1

17 WASA part 3: Frequency of strongly unstable and stable situations in 20 years strongly unstable strongly stable % %

18 WASA part 3: Interannual variation of mean speed and effect of averaging (5 masts)

19 WASA part 3: Monthly statistics files Contents of monthly statistics files (6 levels): - Same parameters as for annual statistics files, but on a monthly basis

20 WASA part 4: Comparison of mean wind speeds in January and May January May > ms -1 8,75-9 ms -1

21 WASA part 4: Comparison of frequency of strongly unstable cases in January and May January May % %

22 WASA part 4: Comparison of frequency of strongly stable cases in January and May January May 0-5 %

23 WASA part 4: Comparison of frequency of mean turbulence intensity in January and May January May 7-8 % 5-6 %

24 WP 1: Comparison with other wake models for AV test case Single wake case FA FLaP Ainslie FJ FLaP Jensen FF FarmFlow Fuga Fuga AWM Ansys Windmodeller WPJ WindPro Jensen WEV WindFarmer Eddy Viscosity

25 WP 1: Comparison with other wake models for AV test case Double wake case FA FLaP Ainslie FJ FLaP Jensen FF FarmFlow Fuga Fuga AWM Ansys Windmodeller WPJ WindPro Jensen WEV WindFarmer Eddy Viscosity

26 Modelling of shadowing by adjacent wind farms Option 1: Already included in the simulation of the ambient flow (WRF) Option 2: Included in the modelling with the wake model FarmFlow Option 3: Analytical wind farm wake model newly implemented in FLaP

27 Analytical wind farm wake model implemented in FLaP Based on work of Frandsen et al. (2007), Emeis (2010), Brand and Wagenaar (2012), Barthelmie et al. (2004), Best et al. (2008) Upstream wind farm as enhanced roughness Development of an internal boundary layer above the upstream wind farm Change of roughness again Wake recovery dependent on height of IBL above the upstream wind farm

28 First test case for analytical wind farm wake model: Wake behind Nysted wind farm Wind speed TI Red line: Model implemented in FLaP

29 Second test case for analytical wind farm wake model: Meerwind SO / NSO NO01 NO NO47 NO45 NSO RE NSO LM Meerwind

30 Test case: Cluster consisting of Meerwind Süd Ost and Nordsee Ost I Comparison of three simulations Reference 1: FLaP for whole cluster (red) Reference 2: FLaP only for NSO, Meerwind Süd Ost neglected (green) Simulation with analytical wind farm wake model for the consideration of Meerwind Süd Ost, NSO simulated with FLaP (blue)

31 Test case MSO/NSO: Sensitivity of the wind farm wake model on wind direction Blue: NSO with FLaP, Meerwind analytical Green: Only NSO with FLaP Red: FLaP for whole cluster As expected overestimation of wake deficit for certain wind directions

32 Large-eddy simulations of the wind farm wake Analytical wind farm wake model only a rough approximation

33 LiDAR measurement campaign generating data for a validation of wake models LiDAR of FoRWind on the nacelle of NO48 full load measurement partial load measurement 33

34 LiDAR measurement campaign: LiDAR after completed installation

35 LiDAR measurement campaign: Settings of the LiDAR scans

36 Objectives: Validation of wake flow at hub height Information on inflow: NSO met mast wind direction 36

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38 Comparison of measured and simulated wind field: Case with yaw misalignment LiDAR data FLaP data Yaw misalignment of a few degrees makes the comparison difficult

39 Comparison of measured and simulated wind field: Minimal yaw misalignment Wake effect seems to be stronger in case of the measurement

40 Red: LiDAR Blue: FLaP Red: LiDAR Blue: FLaP Indeed stronger wake deficit in case of the measurements

41 Adjustment of the near-wake length (4D instead of 2D) leads to a better agreement between measurement and simulation

42 Adjustment of the near-wake length (4D instead of 2D) leads to a better agreement between measurement and simulation

43 Summary Model chain combining modelling of ambient flow, impact of adjacent wind farms and intra wind farm effects developed Improved mesoscale modelling applied for generating a wind and stability atlas Wake models of ClusterDesign partners ECN (FarmFlow) and ForWind (FLaP) can compete with commercially available wake models LiDAR measurement campaign offers unique possibilities to validate and further develop wake models

44 Thank you for your attention!