Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-589-2023
© Author(s) 2023. 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-8-589-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Apr 2023
Research article |
| 26 Apr 2023
| 26 Apr 2023
An investigation of spatial wind direction variability and its consideration in engineering models
Anna von Brandis
Fraunhofer IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Gabriele Centurelli
ForWind – Center for Wind Energy Research, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
Jonas Schmidt
Fraunhofer IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Lukas Vollmer
Fraunhofer IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Bughsin' Djath
Institute of Coastal Systems, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany
Martin Dörenkämper
CORRESPONDING AUTHOR
Fraunhofer IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Related authors
No articles found.
Annika Gaiser, Gerald Steinfeld, Gabriele Centurelli, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-140, https://doi.org/10.5194/wes-2026-140, 2026
Preprint under review for WES
Short summary
Short summary
Wind farm wakes can reach far downstream, influencing the power production of other wind farms. In pursuit of improved wake recovery, we investigate how vertically staggered wind farms influence the flow field and wake recovery budget by using large-eddy simulations. Flow circulation forms inside the wind farm for certain setups and at one wake boundary, which influences wake recovery through advection and causes local speed-ups. The wake and power of a second, downstream wind farm is affected.
Astrid Lampert, Beatriz Cañadillas, Thomas Rausch, Lea Schmitt, Bughsin' Djath, Johannes Schulz-Stellenfleth, Andreas Platis, Kjell zum Berge, Ines Schäfer, Jens Bange, Lukas Gruchot, Thomas Neumann, Martin Dörenkämper, Bernhard Stoevesandt, Julia Gottschall, Lukas Vollmer, Stefan Emeis, Mares Barekzai, Simon Siedersleben, Martin Kühn, Gerald Steinfeld, Detlev Heinemann, Joachim Peinke, Hendrik Heißelmann, Jörge Schneemann, Gabriele Centurelli, Philipp Waldmann, and Konrad Bärfuss
Wind Energ. Sci., 11, 3057–3075, https://doi.org/10.5194/wes-11-3057-2026, https://doi.org/10.5194/wes-11-3057-2026, 2026
Short summary
Short summary
Two major aircraft measurement campaigns above the North Sea provide insights into modifications of the wind field and sea surface induced by wind farms. The aircraft performed transects at hub height upstream and downstream of wind farm clusters and identified different effects, e.g. how long it takes for the wind speed to recover after the wind farm, how changes across the coastline interact with wind energy, and if wind farms are well represented in numerical simulations.
Johanna Borowski, Kerstin Avila, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-129, https://doi.org/10.5194/wes-2026-129, 2026
Preprint under review for WES
Short summary
Short summary
This study introduces a methodology that integrates climate model ensembles into the measure-correlate-predict framework for site-specific wind resource assessments. Using ERA5 reanalysis data and tall tower measurements from European sites, the approach bias-corrects climate model data to local conditions and reproduces historical wind climates. Results indicate decreases in summer wind speeds (typically 0.1–0.4 m s−¹) and slight winter increases but with greater ensemble spread in this season.
Johannes Schulz-Stellenfleth and Bughsin Djath
Wind Energ. Sci., 11, 2323–2344, https://doi.org/10.5194/wes-11-2323-2026, https://doi.org/10.5194/wes-11-2323-2026, 2026
Short summary
Short summary
Data acquired by the European Sentinel-1A/Sentinel-B satellites are combined with a semi-empirical model to enable the easy inclusion of atmospheric offshore wind farm wakes in existing atmospheric model datasets. The model improves the agreement of data from an operational forecast centre with in situ measurements in the German Bight significantly.
Balthazar Arnoldus Maria Sengers, Lukas Vollmer, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-80, https://doi.org/10.5194/wes-2026-80, 2026
Revised manuscript under review for WES
Short summary
Short summary
This work compared parameterizations used in mesoscale models to estimate wind energy production against operational data from several offshore sites. Results showed differences of about five percent, with higher resolution reducing generated power due to stronger wakes. One approach best reproduced key aspects of wind farm performance. Overall, results underline known limitations, but confirm these models are useful for estimating wind farm performance.
Johanna Borowski, Sandra Schwegmann, Kerstin Avila, and Martin Dörenkämper
Wind Energ. Sci., 11, 661–677, https://doi.org/10.5194/wes-11-661-2026, https://doi.org/10.5194/wes-11-661-2026, 2026
Short summary
Short summary
Assessing wind resources and mitigating the associated uncertainties are crucial to wind farm profitability. The study quantifies the uncertainty due to inter-annual variability, averaging 6.5 % and ranging from 1 % to 14 %, using long-term, quality-controlled wind measurements from tall met masts in terrain of varying complexity. Further, the results indicate that machine learning models are beneficial in mitigating the impact of inter-annual variability in heterogeneous and complex terrain.
Johannes Paulsen, Gerald Steinfeld, Martin Kühn, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-23, https://doi.org/10.5194/wes-2026-23, 2026
Revised manuscript under review for WES
Short summary
Short summary
We investigated the potential benefits in wake development and power production of a new turbine concept with a low specific rating and low induction and compared it to a reference design with same rated power. Our results show that the design can strongly increase the AEP and shows especially strong benefits during doldrum events and in strongly cluster wake affected areas. While the inner farm wake wakes are strongly decreased by the new concept, the outer farm wakes are slightly stronger.
Niklas Requate, Lukas Vollmer, and René Hofmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-284, https://doi.org/10.5194/wes-2025-284, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Wind turbines age unevenly within a farm because of site conditions and the influence of surrounding turbines. We present an approach to set lower power on under certain winds to reduce fatigue loads while keeping energy high. On a nine‑turbine example farm, this added two to five years of life with small energy losses of less than 1 % and increased net present value. The method is scalable to any wind farm and delivers simple lookup tables for usage in operational planning and control.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Sonja Steinbrück, Thorben Eilers, Lukas Vollmer, Kerstin Avila, and Gerald Steinfeld
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-146, https://doi.org/10.5194/wes-2024-146, 2024
Preprint withdrawn
Short summary
Short summary
This paper introduces an enhanced coupling between the LES code PALM and the aeroelastic code FAST, enabling detailed turbine output in temporally and spatially heterogeneous atmospheric flows while maintaining computational efficiency. A wind speed correction is added to reduce errors from force smearing on the numerical grid. Results were evaluated through comparisons between different model setups and turbine measurements, including assessments in a two-turbine wake situation.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 8, 1153–1178, https://doi.org/10.5194/wes-8-1153-2023, https://doi.org/10.5194/wes-8-1153-2023, 2023
Short summary
Short summary
This study investigates the performance of pumping-mode ground-generation airborne wind energy systems by determining power-optimal flight trajectories based on realistic, k-means clustered, vertical wind velocity profiles. These profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the kite's trajectory.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, https://doi.org/10.5194/wes-7-1847-2022, 2022
Short summary
Short summary
This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
Short summary
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.
Cited articles
Agora Energiewende: Making the Most of Offshore Wind: Re-Evaluating the
Potential of Offshore Wind in the German North Sea. Re-Evaluating the
Potential of Offshore Wind in the German North Sea, Tech. Rep. 176/01-S-2020/EN 36-2020-EN, Agora Energiewende, Agora Verkehrswende,
Technical University of Denmark and Max-Planck-Institute for Biogeochemistry, https://static.agora-energiewende.de/fileadmin/Projekte/2019/Offshore_Potentials/176_A-EW_A-VW_Offshore-Potentials_Publication_WEB.pdf (last access: 11 April 2023), 2020. a
Ahsbahs, T., Nygaard, N. G., Newcombe, A., and Badger, M.: Wind Farm Wakes from SAR and Doppler Radar, Remote Sens., 12, 462, https://doi.org/10.3390/rs12030462,
2020. a, b
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a
Bott, A.: Synoptische Meteorologie, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-662-48195-0, 2016. a, b
Cañadillas, B., Foreman, R., Barth, V., Siedersleben, S. K., 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
Cañadillas, B., Beckenbauer, M., Trujillo, J. J., Dörenkämper, M., Foreman, R., Neumann, T., and Lampert, A.: Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling, Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, 2022. a
Centurelli, G., Vollmer, L., Schmidt, J., Dörenkämper, M., Schröder, M., Lukassen, L., and Peinke, J.: Evaluating Global Blockage engineering parametrizations with LES, J. Phys.: Conf. Ser., 1934, 012021,
https://doi.org/10.1088/1742-6596/1934/1/012021, 2021. a
Christiansen, M. B. and Hasager, C. B.: 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
Crespo, A., Hernández, J., and Frandsen, S.: Survey of modelling methods
for wind turbine wakes and wind farms, Wind Energy, 2, 1–24,
https://doi.org/10.1002/(sici)1099-1824(199901/03)2:1<1::aid-we16>3.3.co;2-z, 1999. a
Djath, B. and Schulz-Stellenfleth, J.: Wind speed deficits downstream offshore wind parks – A new automised estimation technique based on satellite synthetic aperture radar data, Meteorol. Z., 28, 499–515,
https://doi.org/10.1127/metz/2019/0992, 2019. 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, b
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, in: 2nd Edn., Springer International Publishing, Cham, Switzerland, https://doi.org/10.1007/978-3-319-72859-9, 2018. a, b, c
Eriksson, O., Breton, S.-P., Nilsson, K., and Ivanell, S.: Impact of Wind Veer and the Coriolis Force for an Idealized Farm to Farm Interaction Case,
Appl. Sci., 9, 922, https://doi.org/10.3390/app9050922, 2019. a, b
European Commission: Communication from the commission to the European
Parliament, the European Council, the European Economic and Social Committee
and the Committee of the Regions - The European Green Deal, Technical report,
European Commission, 24 pp.,
https://eur-lex.europa.eu/resource.html?uri=cellar:b828d165-1c22-11ea-8c1f-01aa75ed71a1.0002.02/DOC_1&format=PDF
(last access: 24 December 2021), 2019. a
Farrell, A., King, J., Draxl, C., Mudafort, R., Hamilton, N., Bay, C. J., Fleming, P., and Simley, E.: Design and analysis of a wake model for spatially heterogeneous flow, Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, 2021. a
Fischereit, J., Brown, R., Larsén, X. G., Badger, J., and Hawkes, G.:
Review of Mesoscale Wind-Farm Parametrizations and Their Applications,
Bound.-Lay. Meteorol., 182, 175–224, https://doi.org/10.1007/s10546-021-00652-y, 2022a. a
Fischereit, J., Schaldemose Hansen, K., Larsén, X. G., van der Laan, M. P., Réthoré, P.-E., and Murcia Leon, J. P.: Comparing and validating
intra-farm and farm-to-farm wakes across different mesoscale and
high-resolution wake models, Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, 2022b. a
Gadde, S. N. and Stevens, R. J.: Effect of Coriolis force on a wind farm wake, J. Phys.: Conf. Ser., 1256, 012026, https://doi.org/10.1088/1742-6596/1256/1/012026, 2019. a, b, c
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
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and
Sahlée, E.: Looking for an offshore low-level jet champion among recent
reanalyses: a tight race over the Baltic Sea, Energies, 13, 3670,
doi10.3390/en13143670, 2020. a
Hasager, C. B., Madsen, P. H., Giebel, G., Réthoré, P.-E., Hansen,
K. S., Badger, J., Peña, A., Volker, P., Badger, M., Karagali, I.,
Cutulusis, N., Maule, P., Schepers, G., Wiggelinkhuizen, E. J., Cantero, E.,
Waldl, I., Anaya-Lara, O., Attya, A. B., Svendsen, H., Palomares, A., Palma,
J., Gomes, V. C., Gottschall, J., Wolken-Möhlmann, G., Bastigkeit, I.,
Beck, H., Trujillo, J. J., Barthelmie, R., Sieros, G., Chaviaropoulos, T.,
Vincent, P., Husson, R., and Propathopoulos, J.: Design tool for offshore
wind farm cluster planning, in: Proceedings of the EWEA Annual Event and
Exhibition, Paris, France, https://orbit.dtu.dk/en/publications/design-tool-for-offshore-wind-farm-cluster-planning (last access: 20 April 2023), 2015a. a
Hasager, C. B., Vincent, P., Badger, J., Badger, M., Di Bella, A., Peña,
A., Husson, R., and Volker, P.: Using Satellite SAR to Characterize the Wind
Flow around Offshore Wind Farms, Energies, 8, 5413–5439, https://doi.org/10.3390/en8065413, 2015b. a
Hasager, C. B., Hahmann, A. N., Ahsbahs, T., Karagali, I., Sīle, T., Badger,
M., and Mann, J.: Europe's offshore winds assessed with synthetic aperture
radar, ASCAT and WRF, Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, 2020. a
Hersbach, H., Stoffelen, A., and de Haan, S.: An improved C-band scatterometer ocean geophysical model function: CMOD5, J. Geophys. Res., 112, C03006, https://doi.org/10.1029/2006JC003743, 2007. a
Hofstätter, M., Chimani, B., Lexer, A., and Blöschl, G.: A new
classification scheme of European cyclone tracks with relevance to precipitation, Water Resour. Res., 52, 7086–7104, https://doi.org/10.1002/2016WR019146, 2016. a
Jacobsen, S., Lehner, S., Hieronimus, J., Schneemann, J., and Kühn, M.: Joint Offshore Wind Field Monitoring with Spaceborne SAR and Platform-Based Doppler LiDAR Measurements, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch., XL-7/W3, 959–966, https://doi.org/10.5194/isprsarchives-XL-7-W3-959-2015, 2015. a
Jimenez, B., Durante, F., Lange, B., Kreutzer, T., and Tambke, J.: Offshore
wind resource assessment with WAsP and MM5: comparative study for the German Bight, Wind Energy, 10, 121–134, https://doi.org/10.1002/we.212, 2007. a
Kalverla, P. C., Holtslag, A. A., Ronda, R. J., and Steeneveld, G.-J.: Quality of wind characteristics in recent wind atlases over the North Sea, Q. J. Roy. Meteorol. Soc., 146, 1498–1515, https://doi.org/10.1002/qj.3748, 2020. a
Lanzilao, L. and Meyers, J.: A new wake-merging method for wind-farm power
prediction in the presence of heterogeneous background velocity fields, Wind
Energy, 25, 237–259, https://doi.org/10.1002/we.2669, 2022. a, b
Li, X. and Lehner, S.: Observation of TerraSAR-X for Studies on Offshore Wind
Turbine Wake in Near and Far Fields, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 6, 1757–1768, https://doi.org/10.1109/JSTARS.2013.2263577, 2013. a
Luzia, G., Hahmann, A. N., and Koivisto, M. J.: Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over northern Europe, Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, 2022. a
Machefaux, E., Larsen, G. C., and Leon, J. P. M.: Engineering models for
merging wakes in wind farm optimization applications, J. Phys.: Conf. Ser.,
625, 012037, https://doi.org/10.1088/1742-6596/625/1/012037, 2015. a
Met Office: Cartopy: a cartographic python library with a matplotlib
interface, Exeter, Devon, http://scitools.org.uk/cartopy (last access: 1 November 2021), 2010–2015. a
NEWA: New European Wind Atlas Website and Database, https://map.neweuropeanwindatlas.eu/, last access: 21 April 2023. a
Niayifar, A. and Porté-Agel, F.: A new analytical model for wind farm
power prediction, J. Phys.: Conf. Ser., 625, 012039,
https://doi.org/10.1088/1742-6596/625/1/012039, 2015. a
NREL: FLORIS. Version 2.2.0, https://github.com/NREL/floris (last access: 1 November 2021), 2020. a
Nygaard, N. G. and Hansen, S. D.: Wake effects between two neighbouring wind
farms, J. Phys.: Conf. Ser., 753, 032020, https://doi.org/10.1088/1742-6596/753/3/032020, 2016. a
Nygaard, N. G., Steen, S. T., Poulsen, L., and Pedersen, J. G.: Modelling
cluster wakes and wind farm blockage, J. Phys.: Conf. Ser., 1618, 062072,
https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a
Peña, A. and Rathmann, O.: Atmospheric stability-dependent infinite
wind-farm models and the wake-decay coefficient, Wind Energy, 17, 1269–1285, https://doi.org/10.1002/we.1632, 2014. a
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K.,
Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath,
B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far
field behind offshore wind farms, Sci. Rep., 8, 2163,
https://doi.org/10.1038/s41598-018-20389-y, 2018. a, b, c, d
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
Portabella, M.: Toward an optimal inversion method for synthetic aperture radar wind retrieval, J. Geophys. Res., 107, 3086, https://doi.org/10.1029/2001JC000925, 2002. a
Schmidt, J.: Farm Optimization and eXtended yield Evaluation Software (v0.3.4-doi), Zenodo [code], https://doi.org/10.5281/zenodo.7852145, 2023. a, b
Schmidt, J. and Stoevesandt, B.: Wind farm layout optimisation using wakes from computational fluid dynamics simulations, in: EWEA conference proceedings, 10–13 March 2014, Barcelona, Spain, https://doi.org/10.13140/2.1.2544.3847, 2014. a
Schmidt, J. and Stoevesandt, B.: Wind farm layout optimisation in complex
terrain with CFD wakes, in: EWEA conference proceedings, 17–20 November 2015, Paris, France, https://www.ewea.org/annual2015/conference/submit-an-abstract/pdf/287736207932.pdf (last access: 20 April 2023), 2015.
a
Schmidt, J. and Vollmer, L.: Industrial Wake Models, in: Handbook of Wind
Energy Aerodynamics, edited by: Stoevesandt, B., Schepers, G., Fuglsang, P.,
and Yuping, S., Springer International Publishing, Cham, 1–28,
https://doi.org/10.1007/978-3-030-05455-7_49-1, 2020. a
Schmidt, J., Requate, N., and Vollmer, L.: Wind Farm Yield and Lifetime
Optimization by Smart Steering of Wakes, J. Phys.: Conf. Ser., 1934, 012020, https://doi.org/10.1088/1742-6596/1934/1/012020, 2021. a
Schmidt, J., Requate, N., Vollmer, L., and Meyer, T.: Adaptive time-series
based wind farm optimization, in: Wind Energy Science Conference 2021, 25–28 May 2021, Hannover, Germany, 2022. a
Schneemann, J., Hieronimus, J., Jacobsen, S., Lehner, S., and Kühn, M.:
Offshore wind farm flow measured by complementary remote sensing techniques:
radar satellite TerraSAR-X and lidar windscanners, J. Phys.: Conf. Ser., 625,
012015, https://doi.org/10.1088/1742-6596/625/1/012015, 2015. a
Siedersleben, S. K., Platis, A., Lundquist, J. K., Lampert, A., Bärfuss, K., Cañadillas, 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, 2018. a
SPD, Bündnis 90/Die Grünen, and FDP: Mehr Fortschritt wagen, Bündnis für Freiheit, Gerechtigkeit und Nachhaltigkeit – Koalitionsvertrag zwischen SPD, Bündnis 90/Die Grünen und FDP,
https://www.bundesregierung.de/breg-de/service/gesetzesvorhaben/koalitionsvertrag-2021-1990800
(last access: 27 January 2022), 2021. a
van Bebber, W. J.: Die Zugstrassen der barometrischen Minima, Meteorol. Z.,
8, 361–366, 1891. a
van der Laan, M. P. and Sørensen, N. N.: Why the Coriolis force turns a
wind farm wake clockwise in the Northern Hemisphere, Wind Energy Science, 2,
285–294, https://doi.org/10.5194/wes-2-285-2017, 2017. a, b
van der Laan, M. P., Hansen, K. S., Sørensen, N. N., and Réthoré,
P.-E.: Predicting wind farm wake interaction with RANS: an investigation of
the Coriolis force, J. Phys.: Conf. Ser., 625, 012026,
https://doi.org/10.1088/1742-6596/625/1/012026, 2015. a
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
We propose that considering large-scale wind direction changes in the computation of wind farm...
Altmetrics
Final-revised paper
Preprint