journal article Open Access Mar 20, 2024

Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses

Wind Energy Science Vol. 9 No. 3 pp. 697-719 · Copernicus GmbH
View at Publisher Save 10.5194/wes-9-697-2024
Abstract
Abstract. As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.
Topics

No keywords indexed for this article. Browse by subject →

References
94
[1]
Akhtar, N. and Chatterjee, F.: Wind farm parametrization in COSMO5.0_clm15, World Data Center for Climate (WDCC) at DKRZ, https://doi.org/10.35089/WDCC/WindFarmPCOSMO5.0clm15, 2020. a
[2]
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, c, d, e, f 10.1038/s41598-021-91283-3
[3]
Akhtar, N., Geyer, B., and Schrum, C.: Impacts of accelerating deployment of offshore windfarms on near-surface climate, Sci. Rep., 12, 18307, https://doi.org/10.1038/s41598-022-22868-9, 2022. a 10.1038/s41598-022-22868-9
[4]
Ali, K., Schultz, D. M., Revell, A., Stallard, T., and Ouro, P.: Assessment of Five Wind-Farm Parameterizations in the Weather Research and Forecasting Model: A Case Study of Wind Farms in the North Sea, Mon. Weather Rev., 151, 2333–2359, https://doi.org/10.1175/MWR-D-23-0006.1, 2023. a, b, c, d, e 10.1175/mwr-d-23-0006.1
[5]
Antonini, E. G. and Caldeira, K.: Spatial constraints in large-scale expansion of wind power plants, P. Natl. Acad. Sci. USA, 118, e2103875118, https://doi.org/10.1073/pnas.2103875118, 2021. a, b 10.1073/pnas.2103875118
[6]
Archer, C. L., Wu, S., Ma, Y., and Jiménez, P. A.: 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 10.1175/mwr-d-20-0097.1
[7]
Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L. C., Hansen, M. H., Blasques, J. P. A. A., Gaunaa, M., and Natarajan, A.: The DTU 10-MW reference wind turbine, in: Danish wind power research 2013, https://orbit.dtu.dk/en/publications/the-dtu-10-mw-reference-wind-turbine (last access: 6 May 2022), 2013. a
[8]
Bento, N. and Fontes, M.: Emergence of floating offshore wind energy: Technology and industry, Renew. Sustain. Energ. Rev., 99, 66–82, https://doi.org/10.1016/j.rser.2018.09.035, 2019. a 10.1016/j.rser.2018.09.035
[9]
Borgers, R.: Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses, Zenodo [data set], https://doi.org/10.5281/zenodo.8348700, 2023. a 10.5194/wes-2023-33
[10]
Borrmann, R., Knud, R., Wallasch, A.-K., and Lüers, S.: Capacity densities of European offshore wind farms, Tech. rep., no. SP18004A1, Deutsche WindGuard GmbH, Varel, Germany, https://vasab.org/document/capacity-densities-of-european-offshore-wind-farms/ (last access: 2 February 2022), 2018. a, b, c
[11]
Bourassa, M. A., Meissner, T., Cerovecki, I., Chang, P. S., Dong, X., De Chiara, G., Donlon, C., Dukhovskoy, D. S., Elya, J., Fore, A., et al.: Remotely sensed winds and wind stresses for marine forecasting and ocean modeling, Front. Mar. Sci., 6, 443, https://doi.org/10.3389/fmars.2019.00443, 2019. a 10.3389/fmars.2019.00443
[12]
Brisson, E., Demuzere, M., and Van Lipzig, N.: Modelling strategies for performing convection-permitting climate simulations, Meteorol. Z., 25, 149–163, https://doi.org/10.1127/metz/2015/0598, 2015. a 10.1127/metz/2015/0598
[13]
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, 2020. a 10.1002/we.2484
[14]
Chatterjee, F., Allaerts, D., Blahak, U., Meyers, J., and van Lipzig, N.: Evaluation of a wind-farm parametrization in a regional climate model using large eddy simulations, Q. J. Roy. Meteorol. Soc., 142, 3152–3161, https://doi.org/10.1002/qj.2896, 2016. a, b, c, d 10.1002/qj.2896
[15]
Copernicus Marine Service: Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00183, 2022. a
[16]
Coquilla, R. V., Obermeier, J., and White, B. R.: Calibration procedures and uncertainty in wind power anemometers, Wind Eng., 31, 303–316, https://doi.org/10.1260/030952407783418720, 2007. a 10.1260/030952407783418720
[17]
Das Bundesamt für Seeschifffahrt und Hydrographie: FINO database, http://fino.bsh.de/ (last access: 10 January 2022), 2022. a
[18]
Dhirendra, D.: Uncertainty Assessment Fugro OCEANOR SEAWATCH Wind LiDAR Buoy at RWE Meteomast IJmuiden, Tech. rep., ECOFYS, https://offshorewind.rvo.nl/file/download/45051422 (last access: 15 February 2022), 2014. a
[19]
Dirksen, M., Wijnant, I., Siebesma, P., Baas, P., and Natalie, T.: Validation of wind farm parameterisation in Weather Forecast Model HARMONIE-AROME – Analysis of 2019, Tech. rep., WINS50 report, TU Delft, https://www.wins50.nl/downloads/dirksen_etal_validationreport.pdf (last access: 1 September 2022), 2022. a, b, c, d, e, f
[20]
Doms, G. and Baldauf, M.: A description of the nonhydrostatic regional COSMO-Model Part I: dynamics and numerics, Tech. rep., COSMO documentation, Deutscher Wetterdienst, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_5.00_I, 2013. a, b
[21]
Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D., Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vogel, P.: A description of the nonhydrostatic regional COSMO-Model Part II: physical parametrization, Tech. rep., COSMO documentation, Deutscher Wetterdienst, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_5.00_II, 2013. a
[22]
Duncan, J., Wijnant, I., and Knoop, S.: DOWA validation against offshore mast and LiDAR measurements, Tech. rep., TNO report 2019 R10062, KNMI – Royal Netherlands Meteorological Institute, https://www.dutchoffshorewindatlas.nl/binaries/dowa/ (last access: 1 September 2021), 2019. a
[23]
EMODnet: Wind Farms (Polygons), EMODnet Human Activities [data set], https://emodnet.ec.europa.eu/en/human-activities#humanactivities-data-products (last access: 21 January 2022), 2022. a, b
[24]
Figa-Saldaña, J., Wilson, J. J., Attema, E., Gelsthorpe, R., Drinkwater, M. R., and Stoffelen, A.: The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers, Can. J. Remote Sens., 28, 404–412, https://doi.org/10.5589/m02-035, 2002. a 10.5589/m02-035
[25]
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, b, c 10.1007/s10546-021-00652-y
[26]
Fischereit, J., Larsén, X. G., and Hahmann, A. N.: Climatic Impacts of Wind-Wave-Wake Interactions in Offshore Wind Farms, Front. Energ. Res., 10, 881459, https://doi.org/10.3389/fenrg.2022.881459, 2022b. a, b 10.3389/fenrg.2022.881459
[27]
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, 2022c. a, b, c 10.5194/wes-7-1069-2022
[28]
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 10.1175/mwr-d-11-00352.1
[29]
Friis Pedersen, T., Dahlberg, J.-Å., and Busche, P.: ACCUWIND – Classification of five cup anemometers according to IEC 61400-12-1, no. 1556(EN) in Denmark, Forskningscenter Risoe, Risoe-R, ISBN 87-550-3516-7, 2006. a, b
[30]
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G. E., Abbas, N. J., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: IEA wind TCP task 37: definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., no. NREL/TP-5000-75698, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/1603478, 2020. a 10.2172/1603478
[31]
Garcia-Santiago, O. M., Badger, J., Hahmann, A. N., and da Costa, G. L.: Evaluation of two mesoscale wind farm parametrisations with offshore tall masts, J. Phys.: Conf. Ser., 2265, 022038, https://doi.org/10.1088/1742-6596/2265/2/022038, 2022. a 10.1088/1742-6596/2265/2/022038
[32]
Gelsthorpe, R., Schied, E., and Wilson, J.: ASCAT-Metop's advanced scatterometer, ESA Bulletin, 102, 19–27, 2000. a
[33]
Geyer, B., Weisse, R., Bisling, P., and Winterfeldt, J.: Climatology of North Sea wind energy derived from a model hindcast for 1958–2012, J. Wind Eng. Indust. Aerodynam., 147, 18–29, https://doi.org/10.1016/j.jweia.2015.09.005, 2015. a, b, c, d 10.1016/j.jweia.2015.09.005
[34]
Grachev, A. A., Andreas, E. L., Fairall, C. W., Guest, P. S., and Persson, P. O. G.: The critical Richardson number and limits of applicability of local similarity theory in the stable boundary layer, Bound.-Lay. Meteorol., 147, 51–82, https://doi.org/10.1007/s10546-012-9771-0, 2013. a 10.1007/s10546-012-9771-0
[35]
Gupta, T. and Baidya Roy, S.: Recovery processes in a large offshore wind farm, Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, 2021. a 10.5194/wes-6-1089-2021
[36]
Hahmann, A. N., Vincent<span id="page717"/>, C. L., Peña, A., Lange, J., and Hasager, C. B.: Wind climate estimation using WRF model output: method and model sensitivities over the sea, Int. J. Climatol., 35, 3422–3439, https://doi.org/10.1002/joc.4217, 2015. a 10.1002/joc.4217
[37]
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, Al., 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. Soci., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a 10.1002/qj.3803
[38]
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.: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2022. a
[39]
IPCC: Summary for Policymakers, in: Climate Change 2022: Mitigation of Climate Change, Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Shukla, P., Skea, J., Slade, R., Khourdajie, A. A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., Belkacemi, M., Hasija, A., Lisboa, G., Luz, S., and Malley, J., Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157926.001, 2022. a 10.1017/9781009157926.001
[40]
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW reference wind turbine for offshore system development, Tech. rep., no. NREL/TP-500-38060, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/947422, 2009. a 10.2172/947422
[41]
Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219–2235, https://doi.org/10.5194/amt-14-2219-2021, 2021. a 10.5194/amt-14-2219-2021
[42]
Komusanac, I., Brindley, G., Fraile, D., and Ramirez, L.: Wind energy in Europe: 2020 Statistics and the outlook for 2021–2025, Tech. rep., WindEurope, Brussels, Belgium, https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2020-statistics-and-the-outlook-for-2021 (last access: 10 January 2022), 2020. a
[43]
Komusanac, I., Brindley, G., Fraile, D., and Ramirez, L.: Wind energy in Europe: 2021 Statistics and the outlook for 2022–2026, Tech. rep., WindEurope, Brussels, Belgium, https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2021-statistics-and-the-outlook-for-2022 (last access: 10 January 2022), 2021. a, b
[44]
Koninklijk Nederlands Meteorologisch Instituut: KNMI data platform, https://dataplatform.knmi.nl/group/wind (last access: 25 February 2022), 2022. a
[46]
Leiding, T., Tinz, B., Gates, L., Rosenhagen, G., Herklotz, K., and Senet, C.: Standardisierung und vergleichende Analyse der meteorologischen FINO-Messdaten (FINO123): Forschungsvorhaben FINO-Wind: Abschlussbericht: 12/2012–04/2016, Deutscher Wetterdienst, https://www.dwd.de/DE/klimaumwelt/klimaforschung/klimaueberwachung/finowind/finodoku/abschlussbericht_pdf.pdf?__blob=publicationFile&v=3 (last access: 1 October 2021), 2016. a, b
[47]
Li, D., Geyer, B., and Bisling, P.: A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea, Appl. Energy, 179, 575–589, https://doi.org/10.1016/j.apenergy.2016.07.010, 2016. a 10.1016/j.apenergy.2016.07.010
[48]
Lu, H. and Porté-Agel, F.: On the impact of wind farms on a convective atmospheric boundary layer, Bound.-Lay. Meteorol., 157, 81–96, 2015. a 10.1007/s10546-015-0049-1
[49]
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development, Nat. Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2019. a 10.1038/s41560-018-0281-2
[50]
Matte, D., Laprise, R., Thériault, J. M., and Lucas-Picher, P.: Spatial spin-up of fine scales in a regional climate model simulation driven by low-resolution boundary conditions, Clim. Dynam., 49, 563–574, 2017. a 10.1007/s00382-016-3358-2

Showing 50 of 94 references

Metrics
10
Citations
94
References
Details
Published
Mar 20, 2024
Vol/Issue
9(3)
Pages
697-719
License
View
Cite This Article
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, et al. (2024). Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses. Wind Energy Science, 9(3), 697-719. https://doi.org/10.5194/wes-9-697-2024
Related

You May Also Like