Imagine a wind farm as a team of giant fans where the first ones block the wind for the ones behind them. This project creates a smart brain for the farm that tilts the turbines to steer the wind and avoid these blockages. It doesn't just chase the most power, but balances energy production with the wear and tear on the machines to make the whole operation cheaper.
Current wind farm controls only maximize energy yield, ignoring the cost of turbine wear and fluctuating market prices. This leads to higher long-term operational costs and shorter equipment lifespans.
An open-source, data-driven control tool chain and a 'Control Room of the Future' for managing wind farm output and structural loads.
If you are an operator dealing with high maintenance costs and fluctuating electricity prices — this project developed a Control Room of the Future that targets a 10% reduction in the cost of valued energy (COVE).
If you are a manufacturer dealing with premature component wear in floating farms — this project developed hybrid physics and data-driven models that balance energy yield against structural loads to extend turbine lifetime.
If you are a grid manager dealing with the unpredictability of wind power — this project developed a data-enabled control tool chain to determine the best destination for electrons, whether to the grid, storage, or hydrogen production.
The project targets a 10% reduction in the Cost of Valued Energy (COVE) by optimizing for energy security, storage, electricity prices, and component wear rather than just maximum yield.
Yes, the tool chain is validated using data from 69 11-MW turbines at the Hollandse Kust Noord wind farm and experiments in Europe's largest boundary layer wind tunnel.
Based on available project data, the project is developing open-source technology for the wind farm control and co-design tools.
It uses a data-driven integrated control tool chain and dynamic yaw control to manage both bottom-fixed and floating offshore wind farms.
The project period runs from 2023-10-01 to 2027-09-30, with current progress including the establishment of load models and steady-state wake control.
The consortium is well-balanced for commercial translation, featuring a 33% industry ratio with 3 industrial partners and 2 SMEs. Led by TU Delft, the group spans 6 countries, combining the academic depth of 5 universities with practical industrial application, ensuring the tools are grounded in real-world operational needs.
Contact the Technical University of Delft (TU Delft) regarding the SUDOCO project.
Contact us to explore how to integrate SUDOCO's open-source control tools into your wind farm operations.
