Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Worsnop, Rochelle P.; Scheuerer, Michael; Hamill, Thomas M.; Lundquist, Julie K.

doi:https://doi.org/10.5194/wes-3-371-2018

Articles | Volume 3, issue 1

https://doi.org/10.5194/wes-3-371-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-3-371-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 3, issue 1

Research article

|

14 Jun 2018

Research article |

| 14 Jun 2018

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Rochelle P. Worsnop, Michael Scheuerer, Thomas M. Hamill, and Julie K. Lundquist

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Final revised paper (published on 14 Jun 2018)
Preprint (discussion started on 14 Feb 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'wes-2018-13 review', Jakob W. Messner, 14 Mar 2018
RC2: 'Review (WES-2018-13)', Elliot Simon, 22 Mar 2018
AC1: 'Authors' response to reviewers', Rochelle Worsnop, 16 Apr 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Rochelle Worsnop on behalf of the Authors (16 Apr 2018) Author's response Manuscript

ED: Publish subject to technical corrections (23 May 2018) by Jakob Mann

ED: Publish as is (29 May 2018) by Joachim Peinke (Chief editor)

AR by Rochelle Worsnop on behalf of the Authors (30 May 2018) Manuscript

Short summary

This paper uses four statistical methods to generate probabilistic wind speed and power ramp forecasts from the High Resolution Rapid Refresh model. The results show that these methods can provide necessary uncertainty information of power ramp forecasts. These probabilistic forecasts can aid in decisions regarding power production and grid integration of wind power.