Preprints
https://doi.org/10.5194/wes-2022-31
https://doi.org/10.5194/wes-2022-31
 
12 May 2022
12 May 2022
Status: this preprint is currently under review for the journal WES.

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

Frauke Theuer1, Andreas Rott1, Jörge Schneemann1, Lueder von Bremen2, and Martin Kühn1 Frauke Theuer et al.
  • 1ForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
  • 2German Aerospace Center (DLR) - Institute of Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg

Abstract. Due to the increasing share of wind energy in the power system, minute-scale wind power forecasts have gained importance. Remote sensing-based approaches have proven to be a promising alternative to statistical methods and thus need to be further developed towards an operational use, aiming to increase their forecast availability and skill. Therefore, the contribution of this paper is to extend lidar-based forecasts to a methodology for observer-based probabilistic power forecasts of individual wind turbines and aggregated wind farm power. To do so, lidar-based forecasts are combined with SCADA-based forecasts that advect wind vectors derived from wind turbine operational data. After a calibration, forecasts of individual turbines are aggregated to a probabilistic power forecast of turbine subsets by means of a copula approach. We found that combining the lidar- and SCADA-based forecasts significantly improved both forecast skill and forecast availability of a 5-minute ahead probabilistic power forecast at an offshore wind farm. Calibration further increased the forecast skill. Calibrated observer-based forecasts outperformed the benchmark persistence for unstable atmospheric conditions. The aggregation of probabilistic forecasts of turbine subsets revealed the potential of the copula approach. We discuss the skill, robustness and dependency on atmospheric conditions of the individual forecasts, the value of the observer-based forecast, its calibration and aggregation and more generally the value of minute-scale power forecasts of offshore wind. In conclusion, combining different data sources to an observer-based forecast is beneficial in all regarded cases. For an operational use one should distinguish between and adapt to atmospheric stability.

Frauke Theuer et al.

Status: open (until 23 Jun 2022)

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Frauke Theuer et al.

Frauke Theuer et al.

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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.