ParkCast

Minute-scale forecasting of the performance of offshore wind farms based on lidar data assimilation

Content

The ParkCast project aims to develop, optimize and evaluate new methods for short term forecasts of the performance of offshore wind farms. The power forecasts focus on the time range up to 60 minutes with high temporal resolution. The aim is to significantly improve the temporal resolution and forecasting quality of the parking performance in the above-mentioned time period and thus make a contribution to grid stability and supply security. To this end, long-range lidar measurement data are assimilated into a high-resolution, local weather model using new methods based on machine learning (ML). Physical and advanced ML-based prediction models are then used for the power prediction and validated in real time for the alpha ventus offshore wind farm as part of an online test phase.

Organisation

Duration:
November 2018 to October 2021

Project coordination:
Stuttgarter Lehrstuhl für Windenergie (SWE) - Universität Stuttgart

Finance:
funded by german BMWi
with 1.136.939 €

 

 

Project partners

-Stuttgarter Lehrstuhl für Windenergie (SWE) - Universität Stuttgart
-Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg (ZSW)

Content at SWE

-Durchführung einer mehrjährigen long-range Lidar Messkampagne auf alpha ventus
-Vorhersage der Windgeschwindigkeit und Windrichtung aus Lidardaten
-Untersuchung des Einflusses von atmosphärischen Bedingungen auf die Vorhersage und den Vorhersagehorizont
-Untersuchung und Entwicklung adaptiver Messstrategien zur Windrichtungsanpassung und Optimierung der Reichweite
- Vorhersage der Parkleistung mittels vereinfachtem Parkleistungsmodell

Further information about the group

Contact at SWE

This image shows Ines Würth
Dipl.-Ing.

Ines Würth

Research Associate

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