Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-211-2019
https://doi.org/10.5194/wes-4-211-2019
Research article
 | 
08 May 2019
Research article |  | 08 May 2019

Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

Andrés Santiago Padrón, Jared Thomas, Andrew P. J. Stanley, Juan J. Alonso, and Andrew Ning

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Santiago Padrón on behalf of the Authors (15 Apr 2018)  Manuscript 
ED: Referee Nomination & Report Request started (30 Apr 2018) by Johan Meyers
RR by Anonymous Referee #1 (01 May 2018)
RR by Anonymous Referee #2 (02 May 2018)
ED: Reconsider after major revisions (16 May 2018) by Johan Meyers
AR by Santiago Padrón on behalf of the Authors (23 Aug 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (11 Sep 2018) by Johan Meyers
RR by Anonymous Referee #3 (26 Oct 2018)
ED: Reconsider after major revisions (04 Nov 2018) by Johan Meyers
AR by Santiago Padrón on behalf of the Authors (19 Feb 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (19 Feb 2019) by Johan Meyers
RR by Anonymous Referee #3 (04 Mar 2019)
ED: Publish subject to minor revisions (review by editor) (01 Apr 2019) by Johan Meyers
AR by Santiago Padrón on behalf of the Authors (10 Apr 2019)  Author's response   Manuscript 
ED: Publish as is (11 Apr 2019) by Johan Meyers
ED: Publish as is (13 Apr 2019) by Joachim Peinke (Chief editor)
AR by Santiago Padrón on behalf of the Authors (23 Apr 2019)
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Short summary
We propose the use of a new method to efficiently compute the annual energy production (AEP) of a wind farm by properly handling the uncertainties in the wind direction and wind speed. We apply the new ideas to the layout optimization of a large wind farm. We show significant computational savings by reducing the number of simulations required to accurately compute and optimize the AEP of different wind farms.
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