Clustering Wind Profile Shapes to Estimate Airborne Wind Energy Production

Mark Schelbergen; Joep Breuer; Roland Schmehl

Abstract

Airborne Wind Energy (AWE) systems use tethered flying devices to access higher altitudes, typically up to 500 meter, where wind is generally stronger and more persistent. To estimate the Annual Energy Production (AEP) of AWE systems, the wind speed statistics close to the ground are typically extrapolated to higher altitudes, introducing substantial uncertainties. A methodology is developed for characterising a site’s wind resource using a set of wind profile shapes. It is demonstrated how this wind resource representation is used together with a performance model to do fast AEP calculations for flexible-kite pumping AWE systems.

Original language	English
Pages	93-93
Number of pages	1
Publication status	Published - 2019
Event	8th international Airborne Wind Energy Conference (AWEC 2019) - University of Strathclyde, Glasgow, United Kingdom Duration: 15 Oct 2019 → 16 Oct 2019 Conference number: 8 https://www.awec2019.com http://www.awec2019.com

Conference

Conference	8th international Airborne Wind Energy Conference (AWEC 2019)
Abbreviated title	AWEC 2019
Country/Territory	United Kingdom
City	Glasgow
Period	15/10/19 → 16/10/19
Internet address	https://www.awec2019.com http://www.awec2019.com

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Clustering wind profile shapes to estimate airborne wind energy production
Schelbergen, M., Kalverla, P. C., Schmehl, R. & Watson, S. J., 24 Aug 2020, In: Wind Energy Science. 5, 3, p. 1097-1120 24 p.
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title = "Clustering Wind Profile Shapes to Estimate Airborne Wind Energy Production",

abstract = "Airborne Wind Energy (AWE) systems use tethered flying devices to access higher altitudes, typically up to 500 meter, where wind is generally stronger and more persistent. To estimate the Annual Energy Production (AEP) of AWE systems, the wind speed statistics close to the ground are typically extrapolated to higher altitudes, introducing substantial uncertainties. A methodology is developed for characterising a site{\textquoteright}s wind resource using a set of wind profile shapes. It is demonstrated how this wind resource representation is used together with a performance model to do fast AEP calculations for flexible-kite pumping AWE systems. ",

author = "Mark Schelbergen and Joep Breuer and Roland Schmehl",

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year = "2019",

language = "English",

pages = "93--93",

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AU - Breuer, Joep

AU - Schmehl, Roland

N1 - Conference code: 8

PY - 2019

Y1 - 2019

N2 - Airborne Wind Energy (AWE) systems use tethered flying devices to access higher altitudes, typically up to 500 meter, where wind is generally stronger and more persistent. To estimate the Annual Energy Production (AEP) of AWE systems, the wind speed statistics close to the ground are typically extrapolated to higher altitudes, introducing substantial uncertainties. A methodology is developed for characterising a site’s wind resource using a set of wind profile shapes. It is demonstrated how this wind resource representation is used together with a performance model to do fast AEP calculations for flexible-kite pumping AWE systems.

AB - Airborne Wind Energy (AWE) systems use tethered flying devices to access higher altitudes, typically up to 500 meter, where wind is generally stronger and more persistent. To estimate the Annual Energy Production (AEP) of AWE systems, the wind speed statistics close to the ground are typically extrapolated to higher altitudes, introducing substantial uncertainties. A methodology is developed for characterising a site’s wind resource using a set of wind profile shapes. It is demonstrated how this wind resource representation is used together with a performance model to do fast AEP calculations for flexible-kite pumping AWE systems.

M3 - Abstract

SP - 93

EP - 93

T2 - 8th international Airborne Wind Energy Conference (AWEC 2019)

Y2 - 15 October 2019 through 16 October 2019

ER -

Clustering Wind Profile Shapes to Estimate Airborne Wind Energy Production

Abstract

Conference

Bibliographical note

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Research output

Clustering wind profile shapes to estimate airborne wind energy production

Cite this