Tech
Open-source tool predicts wind farm power fluctuations with greater short-term accuracy
Researchers from TU Delft, a partner of the SUDOCO project, in collaboration with the Université catholique de Louvain (Belgium) and the National Renewable Energy Laboratory of Golden (U.S.), have developed a new open-source wake modeling framework called “OFF,” enhancing existing models such as OnWARDS, FLORIDyn, and FLORIS.
OFF enables the approximation of wind farm flow control (WFFC) strategies under dynamically changing conditions.
Today, most models rely on simplified steady-state assumptions that overlook short-term variability and the transient behavior of turbine wakes, limiting their ability to capture the true dynamics of wind farm interactions.
OFF addresses this gap by incorporating time-dependent dynamics, and when tested with real-world data from the Hollandse Kust Noord wind farm in the Netherlands, it demonstrated improved accuracy in predicting power output and turbine interactions, particularly over short time scales of less than 20 minutes.
These findings highlight OFF’s potential to balance energy gains with reduced turbine wear, making it a valuable tool for both scientists and industry.
The work is presented in the study “A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies” published in Wind Energy Science.
The case study used a 24-hour wind direction time series based on field data, and subsets of the series were verified using Large-Eddy Simulation (LES). Results show that yaw movements strongly depend on the controller settings and indicate how to balance power gains with actuator usage.
Compared to LES, the dynamic wake model predicts short-term turbine power fluctuations more accurately than steady-state models, capturing high-frequency dynamics with better correlation and lower error.
By providing a transparent, accessible, and efficient platform, OFF empowers the wind energy community to accelerate the development of advanced control strategies and drive the transition toward more reliable and sustainable offshore wind power.
More information:
Marcus Becker et al, A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies, Wind Energy Science (2025). DOI: 10.5194/wes-10-1055-2025
Citation:
Open-source tool predicts wind farm power fluctuations with greater short-term accuracy (2025, October 8)
retrieved 8 October 2025
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