Another step towards new wind farm flow control strategies
A team of researchers, led by TUDelft, has introduced OFF, a dynamic open-source model designed to better understand and optimize wind farm performance. Unlike traditional steady-state models, OFF captures the time-varying dynamics of wind turbine wakes, enabling more realistic simulations of flow control strategies.
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 on 11 June 2025 on Wind Energy Science.
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 (USA), 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.
As a matter of fact, 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.
This paper has been supported by the SUDOCO project, an EU-funded Horizon Europe initiative developing an open-source, data-driven “Control Room of the Future” for real-time optimization of offshore wind farms.
To read the full article: click here.
