Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2022
Research article |
| 20 Jun 2022
| 20 Jun 2022
Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling
Beatriz Cañadillas
CORRESPONDING AUTHOR
Institute of Flight Guidance, Technische Universität Braunschweig, Braunschweig, Germany
Renewables, UL International GmbH, Oldenburg, Germany
Maximilian Beckenbauer
Institute of Flight Guidance, Technische Universität Braunschweig, Braunschweig, Germany
Juan J. Trujillo
Renewables, UL International GmbH, Oldenburg, Germany
Martin Dörenkämper
Fraunhofer Institute for Wind Energy Systems, Oldenburg, Germany
Richard Foreman
Renewables, UL International GmbH, Oldenburg, Germany
Thomas Neumann
Renewables, UL International GmbH, Oldenburg, Germany
Astrid Lampert
Institute of Flight Guidance, Technische Universität Braunschweig, Braunschweig, Germany
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Cited articles
Ahsbahs, T., Badger, M., Volker, P., Hansen, K. S., and Hasager, C. B.: Applications of satellite winds for the offshore wind farm site Anholt, Wind Energ. Sci., 3, 573–588, https://doi.org/10.5194/wes-3-573-2018, 2018. a
Ahsbahs, T., Nygaard, N., Newcombe, A., and Badger, M.: Wind Farm Wakes from
SAR and Doppler Radar, Remote Sensing, 12, 462, https://doi.org/10.3390/rs12030462, 2020. a, b
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.:
Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor
Data, J. Atmos. Ocean. Tech., 31, 765–787,
https://doi.org/10.1175/JTECH-D-13-00104.1, 2014. a
Akhtar, N., Geyer, B., Rockel, B., Sommer, P. S., and Schrum, C.: Accelerating
deployment of offshore wind energy alter wind climate and reduce future power
generation potentials, Sci. Rep., 11, 11826,
https://doi.org/10.1038/s41598-021-91283-3, 2021. a, b
Archer, C., Wu, S., ma, Y., and Jimenez, P.: Two Corrections for Turbulent
Kinetic Energy Generated by Wind Farms in the WRF Model, Mon. Weather
Rev., 148, 4823–4835, https://doi.org/10.1175/MWR-D-20-0097.1, 2020. a
Bastine, D., Wächter, M., Peinke, J., Trabucchi, D., and Kühn, M.:
Characterizing Wake Turbulence with Staring Lidar Measurements, Journal of Physics: Conference Series, 625,
012006, https://doi.org/10.1088/1742-6596/625/1/012006, 2015. a
Cañadillas, B.: Figures wes-2021-159 manuscript, figshare [data set],
https://doi.org/10.6084/m9.figshare.19747252.v1, 2022. a
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging
measurements of wake dynamics part I: one-dimensional scanning, Wind Energy,
13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Borraccino, A., Courtney, M., and Wagner, R.: Generic Methodology for Field
Calibration of Nacelle-Based Wind Lidars, Remote Sensing, 8, 6588–6596,
https://doi.org/10.3390/rs8110907, 2016. a
Brower, M. and Robinson, N.: The OpenWind deep-array wake model: development
and validation, Tech. rep., AWS Truepower, Albany, NY, USA, 2012. a
BSH: Flächenentwicklungsplan 2020 für die deutsche Nord- und Ostsee,
Tech. Rep. 7608, BSH – Bundesamt für Seeschiffahrt und Hydrographie,
https://www.bsh.de/DE/THEMEN/Offshore/Meeresfachplanung/Fortschreibung/_Anlagen/Downloads/FEP_2020_Flaechenentwicklungsplan_2020.pdf?__blob=publicationFile&v=6, last access: 1 April 2021. a
Cañadillas, B., Foreman, R., Barth, V., Siedersleben, S., Lampert, A.,
Platis, A., Djath, B., Schulz-Stellenfleth, J., Bange, J., Emeis, S., and
Neumann, T.: Offshore wind farm wake recovery: Airborne measurements and its
representation in engineering models, Wind Energy, 23, 1249–1265,
https://doi.org/10.1002/we.2484, 2020. a, b, c, d, e, f
Christiansen, M. and Hasager, C.: Wake effects of large offshore wind farms
identified from satellite SAR, Remote Sens. Environ., 98, 251–268,
https://doi.org/10.1016/j.rse.2005.07.009, 2005. a
Corsmeier, U., Hankers, R., and Wieser, A.: Airborne turbulence measurements in
the lower troposphere onboard the research aircraft Dornier 128-6, D-IBUF,
Meteorol. Z., 10, 315–329,
https://doi.org/10.1127/0941-2948/2001/0010-0315, 2001. a
Djath, B., Schulz-Stellenfleth, J., and Cañadillas, B.: Impact of
atmospheric stability on X-band and C-band synthetic aperture radar imagery
of offshore windpark wakes, J. Renew. Sustain. Energ., 10, 043301,
https://doi.org/10.1063/1.5020437, 2018. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the New European Wind Atlas – Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a, b
Emeis, S.: A simple analytical wind park model considering atmospheric
stability, Wind Energy, 13, 459–469, https://doi.org/10.1002/we.367, 2010. a
Emeis, S.: Wind Energy Meteorology:Atmospheric Physics for Wind Power
Generation, Springer International Publishing, 2 edn.,
https://doi.org/10.1007/978-3-319-72859-9, 2018. a
Fischler, M. A. and Bolles, R.: Random sample consensus: a paradigm for model
fitting with applications to image analysis and automated cartography,
Communications of the ACM, 24, 381–395, https://doi.org/10.1145/358669.358692,
1981. a
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K.,
Michalakes, J., and Barstad, I.: Local and mesoscale impacts of wind farms as
parameterized in a mesoscale nwp model, Mon. Weather Rev., 140, 3017–3038,
https://doi.org/10.1175/MWR-D-11-00352.1, 2012. a, b, c, d
Franke, K.: Summary of classification of remote sensing device, type: Leosphere
WINDCUBE, techreport PP18030.A1, Deutsche WindGuard Consulting GmbH, Varel,
DE, 2018. a
Frühmann, R., Neumann, T., and Decker, H.: Platform based infrared sea
surface temperature measurement: experiences from a one year trial in the
North Sea, in: Deutsche Windenergie-Konferenz (DEWEK), 2018. a
Goit, J., Yamaguchi, A., and Ishihara, T.: Measurement and Prediction of Wind
Fields at an Offshore Site by Scanning Doppler LiDAR and WRF, Atmosphere, 11,
1–20, https://doi.org/10.3390/atmos11050442, 2020. a
Gómez Arranz, P. and Courtney, M.: WP1 – Literature Review: Scanning
Lidar For Wind Turbine Power Performance Testing, 2021. a
Gottschall, J. and Dörenkämper, M.: Understanding and mitigating the impact of data gaps on offshore wind resource estimates, Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, 2021. a, b
Gottschall, J., Catalano, E., Dörenkämper, M., and Witha, B.: The NEWA Ferry
Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea,
Remote Sensing, 10, 1620, https://doi.org/10.3390/rs10101620, 2018. a, b
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, 2020. a, b
Herges, T. G., Maniaci, D. C., Naughton, B. T., Mikkelsen, T., and
Sjöholm, M.: High resolution wind turbine wake measurements with a
scanning lidar, in: Journal of Physics Conference Series, vol. 854 of Journal of Physics Conference Series, 854, 012021,
https://doi.org/10.1088/1742-6596/854/1/012021, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Huang, H.-P., Giannakopoulou, E.-M., and Nhili, R.: WRF Model Methodology for
Offshore Wind Energy Applications, Adv. Meteorol., 2014, 1687–9309,
https://doi.org/10.1155/2014/319819, 2014. a
IEC-61400-12-1: Wind energy generation systems – Part 12-1: Power performance
measurements of electricity producing wind turbines, International Standard
IEC TC 29110-1:2016, International Electrotechnical Commission, Geneve,
Switzerland, https://webstore.iec.ch/publication/26603 (last access: 10 December 2021), 2017. a, b
Iungo, G. V. and Porté-Agel, F.: Volumetric Lidar Scanning of Wind Turbine
Wakes under Convective and Neutral Atmospheric Stability Regimes, J.
Atmos. Ocean. Tech., 31, 2035–2048,
https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a
Käsler, Y., Rahm, S., Simmet, R., and Kühn, M.: Wake Measurements of a
Multi-MW Wind Turbine with Coherent Long-Range Pulsed Doppler Wind Lidar,
J. Atmos. Ocean. Tech., 27, 1529–1532,
https://doi.org/10.1175/2010JTECHA1483.1, 2010. a
Kibona, T. E.: Application of WRF mesoscale model for prediction of wind energy
resources in Tanzania, Scientific African, 7, e00302,
https://doi.org/10.1016/j.sciaf.2020.e00302, 2020. a
Krishnamurthy, R., Reuder, J., Svardal, B., Fernando, H., and Jakobsen, J.:
Offshore Wind Turbine Wake characteristics using Scanning Doppler Lidar,
Energy Procedia, 137, 428–442,
https://doi.org/10.1016/j.egypro.2017.10.367, 2017. a, b
Lampert, A., Bärfuss, K., Platis, A., Siedersleben, S., Djath, B., Cañadillas, B., Hunger, R., Hankers, R., Bitter, M., Feuerle, T., Schulz, H., Rausch, T., Angermann, M., Schwithal, A., Bange, J., Schulz-Stellenfleth, J., Neumann, T., and Emeis, S.: In situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight, Earth Syst. Sci. Data, 12, 935–946, https://doi.org/10.5194/essd-12-935-2020, 2020. a, b, c
Larsén, X. G. and Fischereit, J.: A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets, Geosci. Model Dev., 14, 3141–3158, https://doi.org/10.5194/gmd-14-3141-2021, 2021. a, b
Lee, J. and Zhao, F.: Global wind report 2021, Tech. rep., Global Wind Energy
Council (GWEC), Brussels, Belgium, 2021. a
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a, b
METEK-GmbH: Doppler Lidar Stream Line XR, Brochure,
https://metek.de/de/wp-content/uploads/sites/6/2016/01/20150410_DataSheet_StreamLineXR.pdf (last access: 7 February 2022),
2021. a
NCAR Users Page: WRF Model User’s Page, WRF Version 4.0.1,
https://doi.org/10.5065/D6MK6B4K, 2021. a
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017. a
Nygaard, N. and Newcombe, A.: Wake behind an offshore wind farm observed with
dual-Doppler radars, Journal of Physics: Conference Series, 1037, 072008,
https://doi.org/10.1088/1742-6596/1037/7/072008, 2018. a
Patrick, V., Hahmann, A. N., and Badger, J.: Wake Effects of Large Offshore
Wind Farms – a study of the Mesoscale Atmosphere, Ph.D. thesis, DTU Wind
Energy, Denmark, 2014. a
Platis, A., Bange, J., Bärfuss, K., Cañadillas, B., Hundhausen, M.,
Djath, B., Lampert, A., Schulz-Stellenfleth, J., Siedersleben, S., Neumann,
T., and Emeis, S.: Long-range modifications of the wind field by offshore
wind parks – results of the project WIPAFF, Meteorol.
Z., 29, 355–376, https://doi.org/10.1127/metz/2020/1023,
2020. a
Platis, A., Hundhausen, M., Mauz, M., Siedersleben, S., Lampert, A., Emeis, S.,
and Bange, J.: The Role of Atmospheric Stability and Turbulence in Offshore
Wind-Farm Wakes in the German Bight, Bound.-Lay. Meteorol., 182,
1573–1472, https://doi.org/10.1007/s10546-021-00668-4, 2021. a
Pryor, S. C., Shepherd, T. J., Barthelmie, R. J., Hahmann, A. N., and Volker,
P.: Wind Farm Wakes Simulated Using WRF, J. Phys.: Conf. Ser., 1256,
012025, https://doi.org/10.1088/1742-6596/1256/1/012025, 2019. a
Rettenmeier, A., Schlipf, D., Würth, I., and Cheng, P. W.: Power Performance
Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar
Scanner, J. Atmos. Ocean. Tech., 31, 2029–2034,
https://doi.org/10.1175/JTECH-D-13-00154.1, 2014. a
Rott, A., Schneemann, J., Theuer, F., Trujillo Quintero, J. J., and Kühn, M.: Alignment of scanning lidars in offshore wind farms, Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, 2022. a
Schneemann, J., Rott, A., Dörenkämper, M., Steinfeld, G., and Kühn, M.: Cluster wakes impact on a far-distant offshore wind farm's power, Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, 2020. a, b
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
Siedersleben, S. K., Lundquist, J. K., Platis, A., Bange, J., Bärfuss, K.,
Lampert, A., Cañadillas, B., Neumann, T., and Emeis, S.:
Micrometeorological impacts of offshore wind farms as seen in observations
and simulations, Environ. Res. Lett., 13, 1–12,
https://doi.org/10.1088/1748-9326/aaea0b, 2018a. a, b
Siedersleben, S. K., Platis, A., Lundquist, J. K., Lampert, A., Bärfuss,
K., Canadillas, B., Djath, B., Schulz-Stellenfleth, J., Bange, J., Neumann,
T., and Emeis, S.: Evaluation of a Wind Farm Parametrization for Mesoscale
Atmospheric Flow Models with Aircraft Measurements, Meteorol.
Z., 27, 401–415, https://doi.org/10.1127/metz/2018/0900, 2018b. a, b, c
Siedersleben, S. K., Platis, A., Lundquist, J. K., Djath, B., Lampert, A., Bärfuss, K., Cañadillas, B., Schulz-Stellenfleth, J., Bange, J., Neumann, T., and Emeis, S.: Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8.1), Geosci. Model Dev., 13, 249–268, https://doi.org/10.5194/gmd-13-249-2020, 2020. a
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W.,
Power, J., Duda, M., Barker, D., and Huang, X.-Y.: A description of the
advanced research WRF version 3, Technical Report, 162 pages
NCAR/TN-556+STR, NCAR - National Center for Atmospheric Research, Boulder,
Colorado, USA, https://doi.org/10.5065/1dfh-6p97, 2019. a
Smalikho, I. N., Banakh, V. A., Pichugina, Y. L., Brewer, W. A., Banta, R. M.,
Lundquist, J. K., and Kelley, N. D.: Lidar Investigation of Atmosphere Effect
on a Wind Turbine Wake, J. Atmos. Ocean. Tech., 30,
2554–2570, https://doi.org/10.1175/JTECH-D-12-00108.1, 2013. a
Sørensen, T. L., Thøgersen, M. L., and Nielsen, P. M.: Adapting and
calibration of existing wake models to meet the conditions inside offshore
wind farms, EMD International A/S, 2008. a
Trujillo, J., Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and
ranging measurements of wake dynamics part I: one-dimensional scanning, Wind
Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Volker, P. J. H., Badger, J., Hahmann, A. N., and Ott, S.: The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015.
a
Wagner, R., Courtney, M. S., Pedersen, T. F., and Davoust, S.: Uncertainty of
power curve measurement with a two-beam nacelle-mounted lidar, Wind Energy,
19, 1269–1287, https://doi.org/10.1002/we.1897, 2016. a
Wang, H. and Barthelmie, R.: Wind turbine wake detection with a single Doppler
wind lidar, Journal of Physics: Conference Series, 625, 012017,
https://doi.org/10.1088/1742-6596/625/1/012017, 2015. a
Werner, C.: Lidar – Range-Resolved Optical Remote Sensing of the Atmosphere,
chap. Doppler Wind Lidar, pp. 325–354, Springer-Verlag New York, 2005. a
WRF Users Page: WRF Model Physics Options and References,
https://www2.mmm.ucar.edu/wrf/users/phys_references.html
(last access: 13 July 2020), 2020. a
Zhan, L., Letizia, S., and Valerio Iungo, G.: LiDAR measurements for an onshore
wind farm: Wake variability for different incoming wind speeds and
atmospheric stability regimes, Wind Energy, 23, 501–527,
https://doi.org/10.1002/we.2430, 2020. a
Short summary
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations...
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