Articles | Volume 20
https://doi.org/10.5194/asr-20-81-2023
https://doi.org/10.5194/asr-20-81-2023
17 Jul 2023
 | 17 Jul 2023

Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services

Lukas Holicki, Manuel Dröse, Gregor Schürmann, and Marcus Letzel

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Short summary
We present a wind power forecasting procedure, that consists of a physics-based component generated at a central server, and a data-based component generated on-site the wind power plant (WPP). It provides blackout-robust data transmission to grid operators and high forecast reliability, especially in the very-short term horizon. This endeavor aims at employing WPPs for support in exceptional or critical grid situations, where short term decision making is most relevant.