Sustainable resilient data-enabled offshore wind farm and control co-design

Wind energy is crucial for realizing climate neutrality, energy independence, and energy security. With the increased penetration of renewables in the electricity grid, there is a strong need for control technology to determine the number of electrons to produce and their destination (e.g., grid, storage, hydrogen) for maximum value to the energy system. Whereas the technology available on the market exclusively maximizes the energy yield, the future lies with optimization for cost of valued energy (COVE), which considers energy security, storage, fluctuating electricity prices, turbine component wear, and turbine lifetime. Therefore, SUDOCO will develop open-source technology for resilient, and data-enabled offshore wind farm control and co-design. We will use key aspects and insights in wind farm operation to develop the Control Room of the Future that will allow operators to control wind farm output and loads by minimizing the COVE. SUDOCO will combine novel dynamic control algorithms, hybrid physics-based and data-driven models for the design of physical farms considering their governing control laws, which are safeguarded against adversarial threats and third-party fraud. Our data driven integrated control tool chain will be trained, validated and optimized using several high-fidelity datasets. These include data from full-scale dedicated wake steering control experiments performed at the 69 11-MW turbines in the Hollandse Kust Noord wind farm. We will also perform unprecedented wind farm control experiments with scaled wind turbine models in the largest boundary layer wind tunnel in Europe.

SUDOCO will target a COVE reduction of 10%, and includes means to minimize environmental impact as well as address the variability of the wind resource. SUDOCO will deliver the first intelligent integrated wind farm control and design solution for reliable, safe, and cost- effective operation of large bottom-fixed and floating offshore wind farms.