Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1097-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-5-1097-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2020
Research article |
| 24 Aug 2020
| 24 Aug 2020
Clustering wind profile shapes to estimate airborne wind energy production
Mark Schelbergen
CORRESPONDING AUTHOR
Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Peter C. Kalverla
Meteorology and Air Quality Section, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Roland Schmehl
Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Simon J. Watson
Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
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Maaike Sickler, Bart Ummels, Michiel Zaaijer, Roland Schmehl, and Katherine Dykes
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Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
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Bedassa R. Cheneka, Simon J. Watson, and Sukanta Basu
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Wind power ramps have important characteristics for the planning and integration of wind power production into electricity. We present a new and simple algorithm that detects wind power ramp characteristics. The algorithm classifies wind power production into ramp-ups, ramp-downs, and no-ramps; and it can detect wind power ramp characteristics that show a temporal increasing (decreasing) power capacity.
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We have presented a methodology for including multiple wind profile shapes in a wind resource description that are identified using a data-driven approach. These shapes go beyond the height range for which conventional wind profile relationships are developed. Moreover, they include non-monotonic shapes such as low-level jets. We demonstrated this methodology for an on- and offshore reference location using DOWA data and efficiently estimated the annual energy production of a pumping AWE system.
We have presented a methodology for including multiple wind profile shapes in a wind resource...
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