Articles | Volume 8, issue 7
https://doi.org/10.5194/wes-8-1153-2023
© 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-8-1153-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2023
Research article |
| 17 Jul 2023
| 17 Jul 2023
Impact of wind profiles on ground-generation airborne wind energy system performance
Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Martin Dörenkämper
Fraunhofer Institute for Wind Energy Systems (IWES), Oldenburg, Germany
Jochem De Schutter
Systems Control and Optimization Laboratory IMTEK, University of Freiburg, Freiburg, Germany
Curran Crawford
Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Related authors
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.
Johanna Borowski, Sandra Schwegmann, Kerstin Avila, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-117, https://doi.org/10.5194/wes-2025-117, 2025
Preprint under review for WES
Short summary
Short summary
Assessing the wind resource and mitigating its 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 to mitigate the impact of inter-annual variability in heterogeneous and complex terrain.
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.
Rad Haghi and Curran Crawford
Wind Energ. Sci., 9, 2039–2062, https://doi.org/10.5194/wes-9-2039-2024, https://doi.org/10.5194/wes-9-2039-2024, 2024
Short summary
Short summary
This journal paper focuses on developing surrogate models for predicting the damage equivalent load (DEL) on wind turbines without needing extensive aeroelastic simulations. The study emphasizes the development of a sequential machine learning architecture for this purpose. The study also explores implementing simplified wake models and transfer learning to enhance the models' prediction capabilities in various wind conditions.
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.
Patrick Connolly and Curran Crawford
Wind Energ. Sci., 8, 725–746, https://doi.org/10.5194/wes-8-725-2023, https://doi.org/10.5194/wes-8-725-2023, 2023
Short summary
Short summary
Mobile offshore wind energy systems are a potential way of producing green fuels from the untapped wind resource that lies far offshore. Herein, computational models of two such systems were developed and verified. The models are able to predict the power output of each system based on wind condition inputs. Results show that both systems have merits and that, contrary to existing results, unmoored floating wind turbines may produce as much power as fixed ones, given the right conditions.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
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.
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.
Rad Haghi and Curran Crawford
Wind Energ. Sci., 7, 1289–1304, https://doi.org/10.5194/wes-7-1289-2022, https://doi.org/10.5194/wes-7-1289-2022, 2022
Short summary
Short summary
Based on the IEC standards, a limited number of simulations is sufficient to calculate the extreme and fatigue loads on a wind turbine. However, this means inaccuracy in the output statistics. This paper aims to build a surrogate model on blade element momentum aerodynamic model simulation output employing non-intrusive polynomial chaos expansion. The surrogate model is then used in a large number of Monte Carlo simulations to provide an accurate statistical estimate of the loads.
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.
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022, https://doi.org/10.5194/wes-7-1093-2022, 2022
Short summary
Short summary
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects, reducing the power yield of the parks.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
Short summary
Short summary
A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Kamran Shirzadeh, Horia Hangan, Curran Crawford, and Pooyan Hashemi Tari
Wind Energ. Sci., 6, 477–489, https://doi.org/10.5194/wes-6-477-2021, https://doi.org/10.5194/wes-6-477-2021, 2021
Short summary
Short summary
Wind energy systems work coherently in atmospheric flows which are gusty. This causes highly variable power productions and high fatigue loads on the system, which together hold back further growth of the wind energy market. This study demonstrates an alternative experimental procedure to investigate some extreme wind condition effects on wind turbines based on the IEC standard. This experiment can be improved upon and used to develop new control concepts, mitigating the effect of gusts.
Kamran Shirzadeh, Horia Hangan, and Curran Crawford
Wind Energ. Sci., 5, 1755–1770, https://doi.org/10.5194/wes-5-1755-2020, https://doi.org/10.5194/wes-5-1755-2020, 2020
Short summary
Short summary
The main goal of this study is to develop a physical simulation of some extreme wind conditions that are defined by the IEC standard. This has been performed by a hybrid numerical–experimental approach with a relevant scaling. Being able to simulate these dynamic flow fields can generate decisive results for future scholars working in the wind energy sector to make these wind energy systems more reliable and finally helps to accelerate the reduction of the cost of electricity.
Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, https://doi.org/10.5194/gmd-13-5053-2020, 2020
Short summary
Short summary
Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Cited articles
Archer, C. L., Colle, B. A., Veron, D. L., Veron, F., and Sienkiewicz, M. J.:
On the predominance of unstable atmospheric conditions in the marine boundary
layer offshore of the U.S. northeastern coast, J. Geophys. Res.-Atmos., 121, 8869–8885, https://doi.org/10.1002/2016JD024896, 2016. a
Argatov, I. and Silvennoinen, R.: Efficiency of Traction Power Conversion Based on Crosswind Motion, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer, Berlin, Heidelberg, 65–79,
https://doi.org/10.1007/978-3-642-39965-7_4, 2013. a
Argatov, I., Rautakorpi, P., and Silvennoinen, R.: Estimation of the mechanical energy output of the kite wind generator, Renew. Energy, 34, 1525–1532, https://doi.org/10.1016/j.renene.2008.11.001, 2009. a
Arya, P. and Holton, J.: Introduction to Micrometeorology, International
Geophysics, Elsevier Science, ISBN 9780120593545, 2001. a
European Wind Energy Association (EWEA): Wind energy-the facts: a guide to the technology, economics and future of wind power, Routledge, https://doi.org/10.4324/9781849773782, 2012. a
Aull, M., Stough, A., and Cohen, K.: Design Optimization and Sizing for Fly-Gen Airborne Wind Energy Systems, Automation, 1, 1–16,
https://doi.org/10.3390/automation1010001, 2020. a
Banta, R. M.: Stable-boundary-layer regimes from the perspective of the
low-level jet, Acta Geophys., 56, 58–87, https://doi.org/10.2478/s11600-007-0049-8,
2008. a
Bechtle, P., Schelbergen, M., Schmehl, R., Zillmann, U., and Watson, S.:
Airborne wind energy resource analysis, Renew. Energy, 141, 1103–1116,
https://doi.org/10.1016/j.renene.2019.03.118, 2019. a
Bronnenmeyer, T.: Optimal Control for Multi-Kite Emergency Trajectories,
MS thesis, University of Stuttgart, Stuttgart,
https://cdn.syscop.de/publications/Bronnenmeyer2018.pdf (last
access: 9 November 2022), 2018. a
Carl von Ossietzky Universität Oldenburg: EDDY,
https://www.uni-oldenburg.de/fk5/wr/hochleistungsrechnen/hpc-facilities/eddy/
(last access: 26 April 2023), 2018. a
Cherubini, A., Papini, A., Vertechy, R., and Fontana, M.: Airborne Wind
Energy Systems: A review of the technologies, Renew. Sustain. Energ. Rev., 51, 1461–1476, https://doi.org/10.1016/j.rser.2015.07.053, 2015. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
De Schutter, J., Leuthold, R., Bronnenmeyer, T., Paelinck, R., and Diehl, M.:
Optimal control of stacked multi-kite systems for utility-scale airborne wind
energy, in: 2019 IEEE 58th Conference on Decision and Control (CDC), 11–13 December 2019, Nice, France, 4865–4870, https://doi.org/10.1109/CDC40024.2019.9030026, 2019. a, b, c
Diehl, M.: Airborne Wind Energy: Basic Concepts and Physical Foundations, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer, Berlin, Heidelberg, 3–22, https://doi.org/10.1007/978-3-642-39965-7_1, 2013. 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 Sensing of Environment, 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Dörenkämper, M., Optis, M., Monahan, A., and Steinfeld, G.: On the
Offshore advection of Boundary-Layer Structures and the Influence
on Offshore Wind Conditions, Bound.-Lay. Meteorol., 155, 459–482,
https://doi.org/10.1007/s10546-015-0008-x, 2015. a
Dörenkämper, M., Stoevesandt, B., and Heinemann, D.: Derivation of an
offshore wind index for the German bight from high-resolution mesoscale
simulation data, Proceedings of DEWEK – German Offshore Wind Energy
Conference, p. 5, http://publica.fraunhofer.de/documents/N-484817.html (last access: 26 April 2023), 2017. 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, c
Echeverri, P., Fricke, T., Homsy, G., and Tucker, N.: The Energy Kite –
Selected Results From the Design, Development and Testing of
Makani's Airborne Wind Turbines – Part 1, Technical Report,
Makani Power,
https://storage.googleapis.com/x-prod.appspot.com/files/Makani_TheEnergyKiteReport_Part1.pdf
(last access: 9 November 2022), 2020. a
Eijkelhof, D. and Schmehl, R.: Six-degrees-of-freedom simulation model for
future multi-megawatt airborne wind energy systems, Renew. Energy, 196,
137–150, https://doi.org/10.1016/j.renene.2022.06.094, 2022. a, b
Eijkelhof, D., Rapp, S., Fasel, U., Gaunaa, M., and Schmehl, R.: Reference
Design and Simulation Framework of a Multi-Megawatt Airborne Wind Energy
System, J. Phys.: Conf. Ser., 1618, 032020, https://doi.org/10.1088/1742-6596/1618/3/032020, 2020. a, b, c
Ellis, G. and Ferraro, G.: The Social Acceptance of Wind Energy: Where we stand and the path ahead, EUR – Scientific and Technical Research Reports,
European Commission, https://doi.org/10.2789/696070, 2016. a
Emeis, S.: Wind energy meteorology: atmospheric physics for wind power
generation, Green Energy and Technology, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-30523-8, 2013. a
EnerKíte GmbH: EnerKíte – Technologie, http://www.enerkite.de/ (last access: 10 May 2023), 2022. a
Faggiani, P. and Schmehl, R.: Design and Economics of a Pumping Kite Wind Park, in: Airborne Wind Energy: Advances in Technology Development and Research, edited by: Schmehl, R., Springer, Singapore, 391–411,
https://doi.org/10.1007/978-981-10-1947-0_16, 2018. a, b
Fagiano, L. and Milanese, M.: Airborne Wind Energy: An overview, in: IEEE 2012 American Control Conference (ACC), 27–29 June 2012, Montreal, Canada, 3132–3143, https://doi.org/10.1109/ACC.2012.6314801, 2012. a
Fagiano, L., Quack, M., Bauer, F., Carnel, L., and Oland, E.: Autonomous
Airborne Wind Energy Systems: Accomplishments and Challenges, Annu. Rev.
Control Robot. Autonom. Syst., 5, 603–631, https://doi.org/10.1146/annurev-control-042820-124658, 2022. a
Fechner, U. and Schmehl, R.: Model-Based Efficiency Analysis of Wind Power
Conversion by a Pumping Kite Power System, in: Airborne Wind Energy, Springer, Berlin, Heidelberg, 249–269, https://doi.org/10.1007/978-3-642-39965-7_14,
2013. a
Floors, R., Batchvarova, E., Gryning, S.-E., Hahmann, A. N., Peña, A., and Mikkelsen, T.: Atmospheric boundary layer wind profile at a flat coastal site – wind speed lidar measurements and mesoscale modeling results, Adv. Sci. Res., 6, 155–159, https://doi.org/10.5194/asr-6-155-2011, 2011. a, b, c
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gros, S., Zanon, M., and Diehl, M.: A relaxation strategy for the optimization of airborne wind energy systems, in: IEEE, 2013 European Control Conference (ECC), 17–19 July 2013, Zurich, Switzerland, 1011–1016, https://doi.org/10.23919/ECC.2013.6669670, 2013. a
Haas, T., Schutter, J. D., Diehl, M., and Meyers, J.: Wake characteristics of
pumping mode airborne wind energy systems, J. Phys.: Conf. Ser., 1256, 012016, https://doi.org/10.1088/1742-6596/1256/1/012016, 2019. 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
Hartigan, J. A. and Wong, M. A.: Algorithm AS 136: A K-Means Clustering
Algorithm, J. Roy. Stat. Soc. Ser. C, 28, 100–108, 1979. a
Heilmann, J. and Houle, C.: Economics of Pumping Kite Generators, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer, Berlin, Heidelberg, 271–284, https://doi.org/10.1007/978-3-642-39965-7_15, 2013. a
Hersbach, H. and Dick, D.: ERA5 reanalysis is in production,
http://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production (last access: 22 October 2019), 2016. a
Houska, B. and Diehl, M.: Optimal control for power generating kites, in: IEEE 2007 European Control Conference (ECC), 2–5 July 2007, Kos, Greece, 3560–3567, https://doi.org/10.23919/ECC.2007.7068861, 2007. a
HSL: HSL. A collection of Fortran codes for large scale scientific computation, http://www.hsl.rl.ac.uk/ (last access: 14 March 2022), 2020. a
Kruijff, M. and Ruiterkamp, R.: A Roadmap Towards Airborne Wind Energy in the
Utility Sector, in: Airborne Wind Energy: Advances in Technology Development
and Research, edited by: Schmehl, R., Springer, Singapore, 643–662,
https://doi.org/10.1007/978-981-10-1947-0_26, 2018. a
Leuthold, R., De Schutter, J., Malz, E., Licitra, G., Gros, S., and Diehl, M.: Operational Regions of a Multi-Kite AWE System, in: 2018 European Control
Conference (ECC), 12–15 June 2018, Limassol, Cyprus, 52–57, https://doi.org/10.23919/ECC.2018.8550199, 2018. a, b, c
Licitra, G.: Identification and optimization of an airborne wind energy system, PhD thesis, University of Freiburg, Freiburg, https://doi.org/10.6094/UNIFR/16226, 2018. a, b
Licitra, G., Koenemann, J., Bürger, A., Williams, P., Ruiterkamp, R., and
Diehl, M.: Performance assessment of a rigid wing Airborne Wind Energy
pumping system, Energy, 173, 569–585, https://doi.org/10.1016/j.energy.2019.02.064,
2019. a, b
Lloyd, S.: Least squares quantization in PCM, IEEE T. Inform. Theory, 28, 129–137, https://doi.org/10.1109/TIT.1982.1056489, 1982. a
Loyd, M. L.: Crosswind kite power (for large-scale wind power production),
J. Energy, 4, 106–111, https://doi.org/10.2514/3.48021, 1980. a
Luchsinger, R. H.: Pumping Cycle Kite Power, in: Airborne Wind Energy, chap. 3, Green Energy and Technology, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer, Berlin, Heidelberg, 47–64,
https://doi.org/10.1007/978-3-642-39965-7_3, 2013. a, b
Lunney, E., Ban, M., Duic, N., and Foley, A.: A state-of-the-art review and
feasibility analysis of high altitude wind power in Northern Ireland, Renew. Sustain. Energ. Rev., 68, 899–911, https://doi.org/10.1016/j.rser.2016.08.014, 2017. a, b
Malz, E., Hedenus, F., Göransson, L., Verendel, V., and Gros, S.: Drag-mode
airborne wind energy vs. wind turbines: An analysis of power production,
variability and geography, Energy, 193, 116765, https://doi.org/10.1016/j.energy.2019.116765, 2020a. a
Malz, E., Verendel, V., and Gros, S.: Computing the power profiles for an
Airborne Wind Energy system based on large-scale wind data, Renew. Energy,
162, 766–778, https://doi.org/10.1016/j.renene.2020.06.056, 2020b. a
Molina-García, A., Fernández-Guillamón, A., Gómez-Lázaro, E.,
Honrubia-Escribano, A., and Bueso, M. C.: Vertical Wind Profile
Characterization and Identification of Patterns Based on a Shape Clustering
Algorithm, IEEE Access, 7, 30890–30904, https://doi.org/10.1109/ACCESS.2019.2902242, 2019. a
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure
Model for the Atmospheric Boundary Layer, J. Meteorol. Soc. Jpn. 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a
Noth, A. and Siegwart, R.: Design of Solar Powered Airplanes for Continuous
Flight, in: ETHZ lecture: “Aircraft and Spacecraft Systems: Design,
Modeling and Control”,
https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/asl-dam/documents/projects/Design_Skysailor.pdf (last access: 5 July 2023), 2006. a
Obukhov, A. M.: Turbulence in an atmosphere with a non-uniform temperature,
Bound.-Lay. Meteorol., 2, 7–29, https://doi.org/10.1007/BF00718085, 1971. a, b
offshorewind.biz: Ampyx Power Taking First Steps Towards MW-Scale Kite, https://www.offshorewind.biz/2018/12/04/ampyx-power-taking-first-steps-towards-mw-scale-kite/
(last access: 14 March 2022), 2018. a
Olauson, J.: ERA5: The new champion of wind power modelling?, Renew. Energy, 126, 322–331, https://doi.org/10.1016/j.renene.2018.03.056, 2018. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, https://doi.org/10.48550/arXiv.1201.0490, 2011. a, b, c
Peña, A., Gryning, S.-E., and Floors, R.: Lidar observations of marine
boundary-layer winds and heights: a preliminary study, Meteorol. Z., 24, 581–589, https://doi.org/10.1127/metz/2015/0636, 2015. a
Powers, J., Ahmadov, R., Barlage, M., Carson, L., Chen, M., Dudhia, J., Fast,
J., FitzGerald, K., Gaudet, B., Gill, D., Liu, J., Wang, W., and Werner, K.:
WRF Source Codes and Graphics Software Download Page,
https://www2.mmm.ucar.edu/wrf/users/download/get_source.html (last access: 9 May 2023), 2023. a
Ranneberg, M., Wölfle, D., Bormann, A., Rohde, P., Breipohl, F., and
Bastigkeit, I.: Fast Power Curve and Yield Estimation of Pumping Airborne
Wind Energy Systems, in: Airborne Wind Energy: Advances in Technology
Development and Research, edited by: Schmehl, R., Springer, Singapore, 623–641, https://doi.org/10.1007/978-981-10-1947-0_25, 2018. a, b
Salvação, N. and Guedes Soares, C.: Wind resource assessment offshore the Atlantic Iberian coast with the WRF model, Energy, 145, 276–287, https://doi.org/10.1016/j.energy.2017.12.101, 2018. a
Schelbergen, M., Kalverla, P. C., Schmehl, R., and Watson, S. J.: Clustering
wind profile shapes to estimate airborne wind energy production, Wind Energ.
Sci., 5, 1097–1120, https://doi.org/10.5194/wes-5-1097-2020, 2020. a, b, c, d
Schmehl, R., Noom, M., and van der Vlugt, R.: Traction Power Generation with
Tethered Wings, in: Airborne Wind Energy, chap. 2, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer, Berlin, Heidelberg, 23–45,
https://doi.org/10.1007/978-3-642-39965-7_2, 2013. a, b, c
Sempreviva, A. M. and Gryning, S.-E.: Humidity fluctuations in the marine
boundary layer measured at a coastal site with an infrared humidity sensor,
Bound.-Lay. Meteorol., 77, 331–352, https://doi.org/10.1007/BF00123531, 1996. a, b
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Duda, M., Huang,
X., Wang, W., and Powers, J.: A description of the advanced research WRF
version 3, Tech. Rep. NCAR/TN−475+STR, NCAR – National Center for
Atmospheric Research, Boulder, Colorado, USA, https://doi.org/10.5065/D68S4MVH, 2008. a, b
Skamarock, W. C., Klemp, J. B., Dudhia, J., and Gill, D. O.: A Description of
the Advanced Research WRF Model Version 4.3, Technical Report, UCAR,
https://doi.org/10.5065/1dfh-6p97, 2021. a, b
Sommerfeld, M.: Onshore & offshore WRF generated wind data, Zenodo [data], version 1, https://doi.org/10.5281/zenodo.4292507, 2020 a
Sommerfeld, M., Dörenk¨amper, M., De Schutter, J., and Crawford, C.: Scaling effects of fixed-wing ground-generation airborne wind energy systems, Wind Ener. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, 2022. a, b, c
Stull, R.: An Introduction to Boundary Layer Meteorology, in: Atmospheric and
Oceanographic Sciences Library, Springer Netherlands,
https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Terink, E. J., Breukels, J., Schmehl, R., and Ockels, W. J.: Flight Dynamics
and Stability of a Tethered Inflatable Kiteplane, J. Aircraft, 48, 503–513, https://doi.org/10.2514/1.C031108, 2011.
a
Vander Lind, D.: Analysis and Flight Test Validation of High Performance
AirborneWind Turbines, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer, Berlin, Heidelberg, 473–490, https://doi.org/10.1007/978-3-642-39965-7_28, 2013. a
van der Vlugt, R., Peschel, J., and Schmehl, R.: Design and Experimental
Characterization of a Pumping Kite Power System, in: Airborne Wind Energy,
edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer, Berlin, Heidelberg, 403–425, https://doi.org/10.1007/978-3-642-39965-7_23, 2013. a, b
van Hagen, L., Petrick, K., Wilhelm, S., and Schmehl, R.: Life-Cycle
Assessment of a Multi-Megawatt Airborne Wind Energy System, Energies, 16, 1750, https://doi.org/10.3390/en16041750, 2023. a, b
Van Sark, W. G., Van der Velde, H. C., Coelingh, J. P., and Bierbooms, W. A.:
Do we really need rotor equivalent wind speed?, Wind Energy, 22, 745–763,
https://doi.org/10.1002/we.2319, 2019. a
Vermillion, C., Cobb, M., Fagiano, L., Leuthold, R., Diehl, M., Smith, R. S.,
Wood, T. A., Rapp, S., Schmehl, R., Olinger, D., and Demetriou, M.:
Electricity in the air: Insights from two decades of advanced control
research and experimental flight testing of airborne wind energy systems,
Annu. Rev. Control, 52, 330–357, https://doi.org/10.1016/j.arcontrol.2021.03.002, 2021. a
Waechter, A. and Laird, C.: Ipopt (Interior Point OPTimizer),
https://github.com/coin-or/Ipopt (last access: 14 March 2022), 2016. a
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine
power collection, Environ. Res. Lett., 7, 014005,
https://doi.org/10.1088/1748-9326/7/1/014005, 2012. a
Witha, B., Hahmann, A., Sile, T., Dörenkämper, M., Ezber, Y.,
Bustamante, E., Gonzalez-Rouco, J., Leroy, G., and Navarro, J.: Report on WRF
model sensitivity studies and specifications for the mesoscale wind atlas
production runs: Deliverable D4.3, vol. D4.3, NEWA – New European Wind Atlas, Zenodo, https://doi.org/10.5281/zenodo.2682604, 2019. a
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.
This study investigates the performance of pumping-mode ground-generation airborne wind energy...
Altmetrics
Final-revised paper
Preprint