Observer-based power forecast of individual  and aggregated offshore wind turbines

Theuer, Frauke; Rott, Andreas; Schneemann, Jörge; von Bremen, Lueder; Kühn, Martin

doi:https://doi.org/10.5194/wes-7-2099-2022

Articles | Volume 7, issue 5

https://doi.org/10.5194/wes-7-2099-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-7-2099-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 7, issue 5

Research article

|

24 Oct 2022

Research article |

| 24 Oct 2022

Observer-based power forecast of individual and aggregated offshore wind turbines

Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn

Download

Final revised paper (published on 24 Oct 2022)
Preprint (discussion started on 12 May 2022)

Interactive discussion

Status: closed

RC1: 'Comment on wes-2022-31', Anonymous Referee #1, 13 Jun 2022

See the attachment

Citation: https://doi.org/10.5194/wes-2022-31-RC1
RC2:
'Comment on wes-2022-31', Anonymous Referee #2, 09 Aug 2022
The authors present a study where LIDAR data were used in combination with SCADA Data to perform 5-minute probabilistic forecast for a windfarm in the North Sea. This topic and the findings presented are relevant for the readers of WES. The introduction and the methods applied are clearly presented, and the figures are well suited to support the findings of this research.

Detailed comments:

Line 92: Please describe briefly what is meant by "synchronized in time", such that the reader may follow your argument without having to study the paper referenced.

Figure 1: The Windpark "Hohe See", next to Global Tech I has been in operation since mid August 2019; your measurement campaign ended in June 2019. Have you seen any influence of Hohe See in your data? In any case, it would be appropriate to indicate Hohe See in Figure 1a as well (e.g. using grey dots) and clarify the impact (or the lack thereof).

Chapter 3.1 (199ff): This information is clearly important, but it should be presented in the Methods section, not as Results.

Line 245: I believe you want to refer to Figure 3, not 2.

Table 1&2: "normalized power" means P/P_r, I assume? Please clarify.

Discussion: I was wondering if you could comment on the expected skill of the presented method for e.g. a 10 minute forecast. Is the range of the lidar the limiting factor, or the simple propagation technique?
Citation: https://doi.org/10.5194/wes-2022-31-RC2
AC1: 'Comment on wes-2022-31', Frauke Theuer, 06 Sep 2022

Please find our author's response attached.

Citation: https://doi.org/10.5194/wes-2022-31-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Frauke Theuer on behalf of the Authors (06 Sep 2022) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (17 Sep 2022) by Sara C. Pryor

AR by Frauke Theuer on behalf of the Authors (22 Sep 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (30 Sep 2022) by Sara C. Pryor

ED: Publish as is (01 Oct 2022) by Nicolaos A. Cutululis (Chief editor)

AR by Frauke Theuer on behalf of the Authors (06 Oct 2022) Manuscript

Short summary

Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.