Preprints
https://doi.org/10.5194/wes-2022-48
https://doi.org/10.5194/wes-2022-48
 
02 Jun 2022
02 Jun 2022
Status: a revised version of this preprint is currently under review for the journal WES.

Predicting power ramps from joint distributions of future wind speeds

Thomas Muschinski1,2, Moritz N. Lang1, Georg J. Mayr2, Jakob W. Messner3, Achim Zeileis1, and Thorsten Simon1,4 Thomas Muschinski et al.
  • 1Department of Statistics, Universität Innsbruck, Innsbruck, Austria
  • 2Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
  • 3MeteoServe Wetterdienst GmbH, Innsbruck, Austria
  • 4Department of Mathematics, Universität Innsbruck, Innsbruck, Austria

Abstract. Power ramps are sudden changes in turbine power and must be accurately predicted to minimize costly imbalances in the electrical grid. Doing so requires reliable wind speed forecasts, which can be obtained from ensembles of physical numerical weather prediction (NWP) models through statistical postprocessing. Since the probability of a ramp event depends jointly on the wind speed distributions forecasted at multiple future times, these postprocessing methods must not only correct each individual forecast but also estimate the temporal dependencies among them. Typically though, crucial dependencies are adopted directly from the raw ensemble and the postprocessed forecast is limited to the tens of members computationally feasible for an NWP model.

We extend statistical postprocessing to include temporal dependencies using novel multivariate Gaussian regression models that forecast 24-dimensional distributions of next-day hourly wind speeds at three offshore wind farms. The continuous joint distribution forecast is postprocessed from an NWP ensemble using flexible generalized additive models for the components of its mean vector μ and for parameters defining the forecast error covariance matrix Σ. Modeling these parameters on predictors which characterize the empirical joint distribution of the NWP ensemble allows forecasts for each hour and their temporal dependencies to be adjusted in one step. Wind speed ensembles of any size can be simulated from the postprocessed joint distribution and transformed into power for computing high-resolution ramp predictions that outperform state-of-the-art reference methods.

Thomas Muschinski et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-48', Anonymous Referee #1, 30 Jun 2022
    • AC2: 'Reply on RC1', Thomas Muschinski, 17 Aug 2022
  • CC1: 'Comment on wes-2022-48', Jethro Browell, 01 Jul 2022
    • AC1: 'Reply on CC1', Thomas Muschinski, 07 Jul 2022
      • CC2: 'Reply on AC1', Jethro Browell, 07 Jul 2022
  • RC2: 'Comment on wes-2022-48', Anonymous Referee #2, 16 Jul 2022
    • AC3: 'Reply on RC2', Thomas Muschinski, 17 Aug 2022

Thomas Muschinski et al.

Thomas Muschinski et al.

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Short summary
The power generated by offshore wind farms can vary greatly within a couple hours and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.