Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2147-2024
© Author(s) 2024. 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-9-2147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Biospheric Theory and Modelling Group, Max Planck Institute of Biogeochemistry, Jena, Germany
Institute of Physics and Meteorology, University of Hohenheim, Stuttgart, Germany
Axel Kleidon
Biospheric Theory and Modelling Group, Max Planck Institute of Biogeochemistry, Jena, Germany
Nsilulu T. Mbungu
Research Institute of Sciences & Engineering (RISE), University of Sharjah, Sharjah, United Arab Emirates
Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa
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Pin-Hsin Hu, Christian H. Reick, Reiner Schnur, Axel Kleidon, and Martin Claussen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-111, https://doi.org/10.5194/gmd-2024-111, 2024
Revised manuscript under review for GMD
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We introduce the new plant functional diversity model JeDi-BACH, a novel tool that integrates the Jena Diversity Model (JeDi) within the land component of the ICON Earth System Model. JeDi-BACH captures a richer set of plant trait variations based on environmental filtering and functional tradeoffs without a priori knowledge of the vegetation types. JeDi-BACH represents a significant advancement in modeling the complex interactions between plant functional diversity and climate.
Yinglin Tian, Deyu Zhong, Sarosh Alam Ghausi, Guangqian Wang, and Axel Kleidon
Earth Syst. Dynam., 14, 1363–1374, https://doi.org/10.5194/esd-14-1363-2023, https://doi.org/10.5194/esd-14-1363-2023, 2023
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Downward longwave radiation (Rld) is critical for the surface energy budget, but its climatological variation on a global scale is not yet well understood physically. We use a semi-empirical equation derived by Brutsaert (1975) to identify the controlling role that atmospheric heat storage plays in spatiotemporal variations of Rld. Our work helps us to better understand aspects of climate variability, extreme events, and global warming by linking these to the mechanistic contributions of Rld.
Axel Kleidon
Earth Syst. Dynam., 14, 861–896, https://doi.org/10.5194/esd-14-861-2023, https://doi.org/10.5194/esd-14-861-2023, 2023
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The second law of thermodynamics has long intrigued scientists, but what role does it play in the Earth system? This review shows that its main role is that it shapes the conversion of sunlight into work. This work can then maintain the dynamics of the physical climate system, the biosphere, and human societies. The relevance of it is that apparently many processes work at their limits, directly or indirectly, so they become predictable by simple means.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Sarosh Alam Ghausi, Subimal Ghosh, and Axel Kleidon
Hydrol. Earth Syst. Sci., 26, 4431–4446, https://doi.org/10.5194/hess-26-4431-2022, https://doi.org/10.5194/hess-26-4431-2022, 2022
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The observed response of extreme precipitation to global warming remains unclear with significant regional variations. We show that a large part of this uncertainty can be removed when the imprint of clouds in surface temperatures is removed. We used a thermodynamic systems approach to remove the cloud radiative effect from temperatures. We then found that precipitation extremes intensified with global warming at positive rates which is consistent with physical arguments and model simulations.
Samuel Schroers, Olivier Eiff, Axel Kleidon, Ulrike Scherer, Jan Wienhöfer, and Erwin Zehe
Hydrol. Earth Syst. Sci., 26, 3125–3150, https://doi.org/10.5194/hess-26-3125-2022, https://doi.org/10.5194/hess-26-3125-2022, 2022
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In hydrology the formation of landform patterns is of special interest as changing forcings of the natural systems, such as climate or land use, will change these structures. In our study we developed a thermodynamic framework for surface runoff on hillslopes and highlight the differences of energy conversion patterns on two related spatial and temporal scales. The results indicate that surface runoff on hillslopes approaches a maximum power state.
Samuel Schroers, Olivier Eiff, Axel Kleidon, Jan Wienhöfer, and Erwin Zehe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-79, https://doi.org/10.5194/hess-2021-79, 2021
Manuscript not accepted for further review
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In this study we ask the basic question why surface runoff forms drainage networks and confluences at all and how structural macro form and micro topography is a result of thermodynamic laws. We find that on a macro level hillslopes should tend from negative exponential towards exponential forms and that on a micro level the formation of rills goes hand in hand with drainage network formation of river basins. We hypothesize that we can learn more about erosion processes if we extend this theory.
Axel Kleidon and Lee M. Miller
Geosci. Model Dev., 13, 4993–5005, https://doi.org/10.5194/gmd-13-4993-2020, https://doi.org/10.5194/gmd-13-4993-2020, 2020
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When winds are used as renewable energy by more and more wind turbines, one needs to account for the effect of wind turbines on the atmospheric flow. The Kinetic Energy Budget of the Atmosphere (KEBA) model provides a simple, physics-based approach to account for this effect very well when compared to much more detailed numerical simulations with an atmospheric model. KEBA should be useful to derive lower, more realistic wind energy resource potentials of different regions.
Annu Panwar, Maik Renner, and Axel Kleidon
Hydrol. Earth Syst. Sci., 24, 4923–4942, https://doi.org/10.5194/hess-24-4923-2020, https://doi.org/10.5194/hess-24-4923-2020, 2020
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Here we examine the effect of evaporative cooling across different vegetation types. Evaporation cools surface temperature significantly in short vegetation. In the forest, the high aerodynamic conductance explains 56 % of the reduced surface temperature. Therefore, the main cooling agent in the forest is the high aerodynamic conductance and not evaporation. Additionally, we propose the diurnal variation in surface temperature as being a potential indicator of evaporation in short vegetation.
Cited articles
Abkar, M., Sharifi, A., and Porté-Agel, F.: Large-eddy simulation of the diurnal variation of wake flows in a finite-size wind farm, J. Phys. Conf. Ser., 625, 012031, https://doi.org/10.1088/1742-6596/625/1/012031, 2015. a
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm during a diurnal cycle, J. Turbul., 17, 420–441, https://doi.org/10.1080/14685248.2015.1127379, 2016. a, b, c
Agora Energiewende, Agora Verkehrswende, Technical University of Denmark, and Max Planck Institute of Biogeochemistry: Making the Most of Offshore Wind: Re-Evaluating the Potential of Offshore Wind in the German North Sea, Tech. Rep. 176/01-S-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: 10 September 2024), 2020. a, b, c
Ahsbahs, T., Nygaard, N., Newcombe, A., and Badger, M.: Wind Farm Wakes from SAR and Doppler Radar, Remote Sens.-Basel, 12, 462–484, https://doi.org/10.3390/rs12030462, 2020. a
Aitken, M. L., Kosović, B., Mirocha, J. D., and Lundquist, J. K.: Large eddy simulation of wind turbine wake dynamics in the stable boundary layer using the Weather Research and Forecasting Model, J. Renew. Sustain. Ener., 6, 033137-1–033137-13, https://doi.org/10.1063/1.4885111, 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.-UK, 11, 11826, https://doi.org/10.1038/s41598-021-91283-3, 2021. a
AMS: Free atmosphere – Glossary of Meteorology, https://glossary.ametsoc.org/wiki/Free_atmosphere (last access: 27 March 2024), 2024. a
Archer, C. L. and Jacobson, M. Z.: Evaluation of global wind power, J. Geophys. Res.-Atmos., 110, D12110, https://doi.org/10.1029/2004JD005462, 2005. a, b
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, b
Blahak, U. and Wetter-Jetzt: A Simple Parameterization of Drag Forces Induced by Large Wind Farms for Numerical Weather Prediction Models, https://api.semanticscholar.org/CorpusID:55966737 (last access: 9 July 2023), 2010. a
Blanco, M. I.: The economics of wind energy, Renewable and Sustainable Energy Reviews, 13, 1372–1382, https://doi.org/10.1016/j.rser.2008.09.004, 2009. a, b, c
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
Boettcher, M., Hoffmann, P., Lenhart, H.-J., Schlünzen, K. H., and Schoetter, R.: Influence of large offshore wind farms on North German climate, Meteorol. Z., 24, 465–480, https://doi.org/10.1127/metz/2015/0652, 2015. a
Brown, A., Beiter, P., Heimiller, D., Davidson, C., Denholm, P., Melius, J., Lopez, A., Hettinger, D., Mulcahy, D., and Porro, G.: Estimating Renewable Energy Economic Potential in the United States. Methodology and Initial Results, Tech. rep., OSTI.GOV, https://doi.org/10.2172/1215323, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Capps, S. B. and Zender, C. S.: Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting, J. Geophys. Res.-Atmos., 115, D09101, https://doi.org/10.1029/2009JD012679, 2010. 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
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
Cory, K. and Schwabe, P.: Wind Levelized Cost of Energy: A Comparison of Technical and Financing Input Variables, OSTI.GOV, https://doi.org/10.2172/966296, 2009. a
Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schlömer, S., and von Stechow, C.: IPCC special report on renewable energy sources and climate change mitigation, Prepared By Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, ISBN 978-1-107-02340-6 (hardback), ISBN 978-1-107-60710-1 (paperback), 2011. a
Enevoldsen, P., Permien, F.-H., Bakhtaoui, I., von Krauland, A.-K., Jacobson, M. Z., Xydis, G., Sovacool, B. K., Valentine, S. V., Luecht, D., and Oxley, G.: How much wind power potential does europe have? Examining european wind power potential with an enhanced socio-technical atlas, Energ. Policy, 132, 1092–1100, https://doi.org/10.1016/j.enpol.2019.06.064, 2019. a, b
Eurek, K., Sullivan, P., Gleason, M., Hettinger, D., Heimiller, D., and Lopez, A.: An improved global wind resource estimate for integrated assessment models, Energ. Econ., 64, 552–567, https://doi.org/10.1016/j.eneco.2016.11.015, 2017. a, b, c, d
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, 2021. a, b, c
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
Fitch, A. C., Lundquist, J. K., and Olson, J. B.: Mesoscale Influences of Wind Farms throughout a Diurnal Cycle, Mon. Weather Rev., 141, 2173–2198, https://doi.org/10.1175/mwr-d-12-00185.1, 2013a. a, b, c, d
Fitch, A. C., Olson, J. B., and Lundquist, J. K.: Parameterization of Wind Farms in Climate Models, J. Climate, 26, 6439–6458, https://doi.org/10.1175/JCLI-D-12-00376.1, 2013b. a, b
Fraedrich, K., Kirk, E., Luksch, U., Lunkeit, F., and Jansen, H.: The planet simulator: towards a user friendly model, Meteorol. Z., 14, 299–304, https://doi.org/10.1127/0941-2948/2005/0043, 2005. a
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., and Thøgersen, M.: Analytical modelling of wind speed deficit in large offshore wind farms, Wind Energy, 9, 39–53, 2006. a
GEA: Global Energy Assessment: Toward a Sustainable Future, Cambridge University Press, https://doi.org/10.1017/CBO9780511793677, 2012. a
Gustavson, M. R.: Limits to Wind Power Utilization, Science, 204, 13–17, https://doi.org/10.1126/science.204.4388.13, 1979. a, b, c
Hasager, C., Vincent, P., Badger, J., Badger, M., Bella, A. D., 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, 2015. 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.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hoogwijk, M., de Vries, B., and Turkenburg, W.: Assessment of the global and regional geographical, technical and economic potential of onshore wind energy, Energ. Econ., 26, 889–919, https://doi.org/10.1016/j.eneco.2004.04.016, 2004. a, b, c, d
IEA: World energy outlook 2021 – analysis, https://www.iea.org/reports/world-energy-outlook-2021 (last access: 6 July 2023), 2021. a
Jacobson, M. Z.: GATOR-GCMM: A global- through urban-scale air pollution and weather forecast model: 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow, J. Geophys. Res.-Atmos., 106, 5385–5401, https://doi.org/10.1029/2000JD900560, 2001. a
Jacobson, M. Z. and Delucchi, M. A.: Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials, Energ. Policy, 39, 1154–1169, https://doi.org/10.1016/j.enpol.2010.11.040, 2011. a
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster efficiency, in: European wind energy association conference and exhibition, vol. 1, A. Raguzzi Rome, Italy, pp. 407–410, 1986. a
Kleidon, A.: Physical limits of wind energy within the atmosphere and its use as renewable energy: From the theoretical basis to practical implications, Meteorol. Z., 30, 203–225, https://doi.org/10.1127/metz/2021/1062, 2021. a, b
Kleidon, A. and Miller, L. M.: The Kinetic Energy Budget of the Atmosphere (KEBA) model 1.0: a simple yet physical approach for estimating regional wind energy resource potentials that includes the kinetic energy removal effect by wind turbines, Geosci. Model Dev., 13, 4993–5005, https://doi.org/10.5194/gmd-13-4993-2020, 2020. a, b, c, d, e, f, g, h, i, j, k
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
Lu, X., McElroy, M. B., and Kiviluoma, J.: Global potential for wind-generated electricity, P. Natl. Acad. Sci. USA, 106, 10933–10938, https://doi.org/10.1073/pnas.0904101106, 2009. a, b
Lundquist, J. K., Takle, E. S., Boquet, M., Kosovic, B., Rhodes, M. E., Rajewski, D., Doorenbos, R., Irvin, S., Aitken, M. L., Friedrich, K., Quelet, P. T., Rana, J., Martin, C. S., Vanderwende, B., and Worsnop, R.: Lidar observations of interacting wind turbine wakes in an onshore wind farm, in: EWEA meeting proceedings, EWEA – European Wind Energy Agency, 10–13, https://www.nrgsystems.com/assets/resources/Lidar-observations-of-interacting-wind-turbine-wakes-Whitepaper.pdf (last access: 27 January 2023), 2014. a
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, Nature Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2018. a
Lütkehus, I., Salecker, H., and Adlunger, K.: Potenzial der Windenergie an Land: Studie zur Ermittlung des Bundesweiten Flächen- und Leistungspotenzials der Windenergienutzung an Land, Tech. rep., UBA – Umweltbundesamt, https://www.umweltbundesamt.de/sites/default/files/medien/378/publikationen/potenzial_der_windenergie.pdf (last access: 14 April 2023), 2013. a
Maas, O. and Raasch, S.: Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight, Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, 2022. a
Manwell, J. F., McGowan, J. G., and Rogers, A. L.: Wind energy explained, 2 edn., John Wiley & Sons, Nashville, TN, https://doi.org/10.1002/9781119994367, 2010. a
Marvel, K., Kravitz, B., and Caldeira, K.: Geophysical limits to global wind power, Nat. Clim. Change, 3, 118–121, https://doi.org/10.1038/nclimate1683, 2012. a, b, c, d
McKenna, R., Pfenninger, S., Heinrichs, H., Schmidt, J., Staffell, I., Bauer, C., Gruber, K., Hahmann, A. N., Jansen, M., Klingler, M., Landwehr, N., Larsén, X. G., Lilliestam, J., Pickering, B., Robinius, M., Tröndle, T., Turkovska, O., Wehrle, S., Weinand, J. M., and Wohland, J.: High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs, Renew. Energ., 182, 659–684, https://doi.org/10.1016/j.renene.2021.10.027, 2022. a, b
Méchali, M., Barthelmie, R., Frandsen, S., Jensen, L., and Réthoré, P.-E.: Wake effects at Horns Rev and their influence on energy production, EWEA – European Wind Energy Association, https://api.semanticscholar.org/CorpusID:14985777 (last access: 15 December 2021), 2006. a
Miller, L., Brunsell, N. A., Mechem, D. B., Gans, F., Monaghan, A. J., Vautard, R., Keith, D. W., and Kleidon, A.: Two methods for estimating limits to large-scale wind power generation, P. Natl. Acad. Sci. USA, 112, 11169–11174, https://doi.org/10.1073/pnas.1408251112, 2015. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae
Miller, L. M. and Keith, D. W.: Observation-based solar and wind power capacity factors and power densities, Environ. Res. Lett., 13, 104008, https://doi.org/10.1088/1748-9326/aae102, 2018. a, b
Miller, L. M., Gans, F., and Kleidon, A.: Estimating maximum global land surface wind power extractability and associated climatic consequences, Earth Syst. Dynam., 2, 1–12, https://doi.org/10.5194/esd-2-1-2011, 2011. a, b, c, d
Minz, J., Kleidon, A., and Mbungu, N. T.: Supplementary material and KEBA Model used in the evaluation of Kansas wind energy potential, EDMOND [code and data set], https://doi.org/10.17617/3.78, 2024. a
Mirocha, J. D., Rajewski, D. A., Marjanovic, N., Lundquist, J. K., Kosović, B., Draxl, C., and Churchfield, M. J.: Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model, J. Renew. Sustain. Ener., 7, 043143, https://doi.org/10.1063/1.4928873, 2015. a
Nygaard, N. G. and Newcombe, A. C.: Wake behind an offshore wind farm observed with dual-Doppler radars, J. Phys. Conf. Ser., 1037, 072008, https://doi.org/10.1088/1742-6596/1037/7/072008, 2018. 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
Pedersen, J. G., Svensson, E., Poulsen, L., and Nygaard, N. G.: Turbulence Optimized Park model with Gaussian wake profile, J. Phys. Conf. Ser., 2265, 022063, https://doi.org/10.1088/1742-6596/2265/2/022063, 2022. a
Peixoto, J. P. and Oort, A. H.: Physics of climate, Springer, ISBN 978-0-88318-712-8, 1992. 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.-UK, 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018. a
Prakash, G., Anuta, H., Gielen, D., Gorini, R., Wagner, N., and Gallina, G.: Future of wind:Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), Tech. Rep., ISBN 978-92-926-155-3, International Renewable Energy Agency, https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf (last access: 2 January 2022), 2019. a, b
Ragheb, M.: Chapter 25 – Economics of Wind Power Generation, in: Wind Energy Engineering, edited by: Letcher, T. M., Academic Press, https://doi.org/10.1016/B978-0-12-809451-8.00025-4, pp. 537–555, 2017. a, b, c, d
Rajewski, D. A., Takle, E. S., Lundquist, J. K., Oncley, S., Prueger, J. H., Horst, T. W., Rhodes, M. E., Pfeiffer, R., Hatfield, J. L., Spoth, K. K., and Doorenbos, R. K.: Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm, B. Am. Meteorol. Soc., 94, 655–672, https://doi.org/10.1175/bams-d-11-00240.1, 2013. a
Ruijgrok, E. C. M., Bulder, B. H., and van Druten, E. J.: Cost Evaluation of North Sea Offshore Wind, Wittveen+Bos, https://northseawindpowerhub.eu/sites/northseawindpowerhub.eu/files/media/document/Cost-Evaluation-of-North-Sea-Offshore-Wind-1.pdf (last access: 7 November 2024), 2019. a
Schallenberg-Rodriguez, J.: A methodological review to estimate techno-economical wind energy production, Renewable and Sustainable Energy Reviews, 21, 272–287, https://doi.org/10.1016/j.rser.2012.12.032, 2013. a, b
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
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, 2018. a
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., Huang, X.-Y., and Duda, M.: A Description of the Advanced Research WRF Version 3, Tech. rep., UCAR, https://doi.org/10.5065/D68S4MVH, 2008. a
Staffell, I. and Pfenninger, S.: Using bias-corrected reanalysis to simulate current and future wind power output, Energy, 114, 1224–1239, https://doi.org/10.1016/j.energy.2016.08, 2016. a, b, c
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, b, c, d
Wang, C. and Prinn, R. G.: Potential climatic impacts and reliability of very large-scale wind farms, Atmos. Chem. Phys., 10, 2053–2061, https://doi.org/10.5194/acp-10-2053-2010, 2010. a, b, c, d
Wang, C. and Prinn, R. G.: Potential climatic impacts and reliability of large-scale offshore wind farms, Environ. Res. Lett., 6, 025101, https://doi.org/10.1088/1748-9326/6/2/025101, 2011. a, b, c, d
Wiser, R., Jenni, K., Seel, J., Baker, E., Hand, M., Lantz, E., and Smith, A.: Expert elicitation survey on future wind energy costs, Nature Energy, 1, 16135, https://doi.org/10.1038/nenergy.2016.135, 2016. a, b
Wu, Y.-T. and Porté-Agel, F.: Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm, Renew. Energ., 75, 945–955, https://doi.org/10.1016/j.renene.2014.06.019, 2015. a
Short summary
Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.
Estimates of power output from regional wind turbine deployments in energy scenarios assume that...
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