Project

Accelerating the digital innovation in the PtX energy sector and its related sectors requires considering all stakeholders in the development of digital ecosystem solutions for efficient sector coupling in PtX value chains.
The project will investigate the potential and possibilities of purchasing electricity from large-scale offshore wind and energy islands for use in a regional ecosystem with sector coupling solutions, PtX production and infrastructure. The project will build knowledge, uncover commercial opportunities and screen for business potential and skills needed to build and run the ecosystem.

Floating offshore wind turbines (FOWT) is a new technology, which is still in its developing stage. FOWT could be the solution in order to increase the possible construction areas, as they are more suitable for deeper waters. But the downside is that a floating foundation introduces additional dynamics to the system, which could lead to complex constructions and thereby decrease their cost/effectiveness. If the FOWT control systems take these dynamics into account it could minimize the impact of these and thereby increase the advancement

of FOWTs. Therefore in this project it is sought to develop a physically scaled model of a real wind turbine, which is able to be controlled similar to real wind turbine systems, this includes generator torque control and blade pitching control. The physical model must be constructed in order to test and verify these controlling methods. In this project the scaled nacelle of a wind turbine is designed and constructed, together with the power electronics. It is a 1:35 scaled model of the NREL 5 MW reference wind turbine. Furthermore, blades are designed and constructed in order to match the scaled thrust force of the reference wind turbine. The dynamic models of the subsystems of the wind turbine are developed and controllers for them are designed. The controller's impact is simulated in simulink models of the subsystems.

The project aims to develop wind farm models based on data and artificial intelligence algorithms. The model and data will support the design of intelligent control algorithms for wind farms. This modeling method is used to solve the problem that existing models cannot be used for actual wind farm control. The model uses machine learning models to learn high-fidelity model data to improve the performance of low-fidelity models. So as to achieve the balance between the fidelity required by the control algorithms and the computational cost. The wind farm control algorithm based on this model aims to improve the power production and turbine life of the total farm by intelligent wake redirection. The wind power industry will also benefit from the development of artificial intelligence algorithms. Reinforcement learning is used to design intelligently optimized controllers for wind farms.

The main objective of this project is to provide a detailed feasibility study of the economy, maturity and technical challenges in changing diesel gen-sets of the offshore service fleets with a hybrid battery and fuel cell powered units. Currently, these ships are supplied with 2-4 high velocity 4-stroke diesel motors with the size 1-6MW that consume low-sulfur diesel oil. These ships normally sail with 14 day return cycles and therefore they should be equipped with energy stored for at least 14 days.

This project enables Danish participation in IEA Wind Task 44: Farm Flow Control. The focus is on control strategies to mitigate wake effects in wind farms. The purpose of IEA Wind Task 44 is to coordinate international research in the field of wind field control inside wind farms. The technology used for this task covers a wide range, but focuses primarily on control algorithms and strategies and how they are transferred to real-world operational improvements.

The intention is to bring together ongoing research results as well as best industry practice, create an overview of control strategies and algorithms and investigate how uncertainties affect the performance and potential for implementation of wind farm control.
The result is guidance for the wind industry and researchers on the current control algorithms, requirements, barriers to adoption, future directions and expected benefits of wind farm control.

To transfer energy from collected offshore wind farms over a long distance, HVDC transmission is preferred over HVAC in terms of efficiency and economy. Several multi-stage configurations have been proposed in the literature. However, the multi-stage configuration generally results in a large size due to a large number of conversion stages, relatively high cost, and low efficiency and power density. Also, the independent control of several converters and communication among the sources make the system complex. To overcome these disadvantages, multi-port modular DC/DC topologies have been suggested. Multiport converters are highly non-linear MIMO systems with many control variables. Also, the coupling between the control variables makes modeling and control system design complicated. Despite such complexity, advanced control techniques have not been comprehensively studied. Moreover, most controller design work on multiport converters has not considered the uncertainties of the converter model. In this Ph.D. study, a robust controller is implemented for multi-port modular DC/DC converter for offshore wind farms application.

This project aims to suppress the oscillation motion of floating offshore wind turbines and to improve the structural safety margin of the turbines. The tension leg platform has good vertical stiffness, but insufficient horizontal stiffness and are prone to yawing motion. By establishing a vibration isolation system to resist and dissipate wave impact and wind load impact. The excitation and damage caused by external loads to the wind turbine can be effectively mitigated. The response of the wind turbine is analyzed based on the wave load spectrum and the response curve of the floating platform is calculated using numerical simulation as a basis for designing the hybrid vibration isolation system. A suitable control strategy is selected to first dissipate the waves by controlling the actuators and then dissipate the energy using hybrid vibration isolation. Simulations and experimental studies are used to select the appropriate dynamic parameters for the vibration isolation system to achieve the desired response of the wind turbine. The life state analysis of key components such as tension legs is carried out. The performance degradation characteristics and laws of wind turbines under low-frequency cyclic waves are studied to ensure their safe operation.

HVDC offshore wind farms with MVDC power collection have recently aroused researchers' interest as these systems offer lower losses and fabrication expenses. Numerous potential MVDC converters could be used in the power collection stage of offshore wind farms; however, when it comes to the technology level, these DC/DC converters are still immature since no substantial studies concerning their control exist. Thus, this Ph.D. project aims to address the research gap to enhance the performance as well as the efficiency of an MVDC converter. The novel switching and control technique proposed in this project together with the significant features of wide bandgap switches provide the condition based on which the MVDC converter could operate at higher switching frequencies than what is already possible. Hence, the controlled MVDC converter will be smaller in size and lighter in weight compared to the conventional ones which reduces the LCOE and provides better possibilities for modularity.

The aim is to obtain knowledge of nonlinear loads which could cause unwanted dynamics or stability issues for the DC-microgrid. This leads to the investigation of the nonlinear loads: Constant Power Loads and Reversible Solid Oxide Cells.
The design of a suitable DC-DC converters and control systems are investigated to fulfill performance requirements and mitigate stability issues.

This project includes modelling, designing and testing of a 150 kW solid-oxide electrolysis (SOE) system for renewable hydrogen production. The produced hydrogen can be used as a component for future green electro-fuels like ammonia or methanol.

The SOC stacks will be operated by the novel AC:DC control method which enables dynamic hydrogen production due to fluctuating electricity production from wind turbines.

The AC:DC method requires bi-directional power flow of the stacks and dedicated power electronic converters will therefore be developed in this project as well.

When the project is successfully completed, the consortium will have demonstrated manufacturing and operation of a power-to-X plant with AC:DC operation technology. This is an important milestone on the path for megawatt production.