Periodic Reporting for period 1 - SUDOCO (Sustainable resilient data-enabled offshore wind farm and control co-design)
Período documentado: 2023-10-01 hasta 2025-01-31
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.
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.
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.
We have four objectives for SUDOCO. We herewith provide a short summary on the status of these project objectives.
Technical objective: Farm-wide flow control aimed at yield versus structural load balancing
• Started to develop advanced control frameworks to balance energy yield and structural load across large offshore wind farms. The load models and steady-state wake control technologies are now in place.
• High-fidelity large-eddy simulations created a versatile database for baseline scenarios, supporting steady state and later dynamic control strategies under varying atmospheric conditions.
• Innovative techniques like wake mixing, dynamic yaw control, and modular wind farm control platforms demonstrated measurable improvements in energy yield.
Economic objective: Cost of valued energy (COVE) reduction by balancing wind farm costs with yield
• The project introduced models to optimize control strategies that balance energy production costs with efficiency.
• Preliminary results indicate cost reductions through more efficient flow control, translating into enhanced economic viability for offshore wind farms.
Environmental objective: Farm-wide system optimization for environmental impact/gains
• Initial results indicate that enhanced flow control strategies demonstrated measurable environmental benefits.
• Wind farm simulations provided data on optimizing layouts and operational strategies to minimize environmental impact while maximizing gains in energy yield.
Social objective: Ensuring uninterrupted supply of energy by retaining high level of (cyber)security
• Initial steps were undertaken to implement high levels of cybersecurity within wind farm control systems, ensuring resilience against potential disruptions.
• Planned future work includes robust integration of cybersecurity strategies within the wind farm's operational framework to safeguard uninterrupted energy supply.
Technical objective: Farm-wide flow control aimed at yield versus structural load balancing
• Started to develop advanced control frameworks to balance energy yield and structural load across large offshore wind farms. The load models and steady-state wake control technologies are now in place.
• High-fidelity large-eddy simulations created a versatile database for baseline scenarios, supporting steady state and later dynamic control strategies under varying atmospheric conditions.
• Innovative techniques like wake mixing, dynamic yaw control, and modular wind farm control platforms demonstrated measurable improvements in energy yield.
Economic objective: Cost of valued energy (COVE) reduction by balancing wind farm costs with yield
• The project introduced models to optimize control strategies that balance energy production costs with efficiency.
• Preliminary results indicate cost reductions through more efficient flow control, translating into enhanced economic viability for offshore wind farms.
Environmental objective: Farm-wide system optimization for environmental impact/gains
• Initial results indicate that enhanced flow control strategies demonstrated measurable environmental benefits.
• Wind farm simulations provided data on optimizing layouts and operational strategies to minimize environmental impact while maximizing gains in energy yield.
Social objective: Ensuring uninterrupted supply of energy by retaining high level of (cyber)security
• Initial steps were undertaken to implement high levels of cybersecurity within wind farm control systems, ensuring resilience against potential disruptions.
• Planned future work includes robust integration of cybersecurity strategies within the wind farm's operational framework to safeguard uninterrupted energy supply.
At several points within the project we already go beyond the current state-of-the-art. We demonstrated measurable improvements in energy yield using techniques like wake mixing, dynamic yaw control, and combinations thereof. Moreover, we released initial versions of load surrogate models, structural health monitoring tools, and co-design algorithms. We developed life cycle assessment tools (DETECT) to quantify offshore wind farms’ carbon footprints. We expanded the open-source FLASC toolkit for model validation and early tests indicate strong alignment between simulated and measured wake losses.