Atmospheric Flow Loads and Power for Wind Energy – FLOW

Simulations of a large wind farm in different atmospheric boundary layer heights.
The development of the wind energy industry has given place to an increasing capability to build larger wind turbines. The scale of the upcoming generation of rotors is so large that they will face an increasing range of atmospheric events that belong to the uppermost regions of the boundary layer.
Details
- Period: 2023-01-01 – 2026-12-31
- Budget: 6,518,479 SEK
- Funder: EU – Horizon Europe, Internal funding
- Type of funding: Project founding
Description
The development of the wind energy industry has given place to an increasing capability to build larger wind turbines. The scale of the upcoming generation of rotors is so large that they will face an increasing range of atmospheric events that belong to the uppermost regions of the boundary layer. Some of these are categorized as extreme events, due to the decremental effect on the wind energy production and the performance of the wind parks. Despite the name, these can appear with certain regularity in the particular conditions of Northern Europe and more specifically, of the Baltic Sea. Examples of these are low-level jets and shallow boundary layers. Other events such as blockage and park wakes are related to the increase density of wind farms, and individual turbines therein, which also have a decreasing effect on the energy production and even more so when coupled with the atmospheric phenomena. Flow is an EU-project starting 2023 with a consortium containing DTU (coordinator), Uppsala university and leading academic institutions and industrial forerunners.
The main objectivs in the project are following:
-Develop new engineering models parametrizing wind physics and turbulence at higher altitudes.
-Predict, throughout models, the production and loads of modern GW-scale and 400-m tall wind energy systems with high confidence both onshore and offshore.
-Validate the models with an unprecedented amount of data, reduce the errors of the predictions, and quantify the uncertainty.
For the Swedish part;
Project leader, Professor Stefan Ivanell
Grant, 651848 Euro