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Wake effects inside a conventional fixed bottom wind farm decrease the power produced by the downwind turbines, hence decreasing the farm’s annual energy production (AEP). However, floating offshore wind turbines (FOWTs) have the ability to relocate their positions laterally through surge and sway motions. This flexibility provides a new degree of freedom (DOF) in the floating wind farm layout, which can be used to decrease the aerodynamic interactions inside the floating wind farm and hence decrease the wake losses. The lateral movement of FOWTs can be passively controlled by the mooring system design. The mooring system’s restoring characteristics allows the FOWT to only move within a specific area in the x-y plane known as the watch circle. Current state of the art mooring system designs are following oil and gas design basis where the floating platforms are not allowed to have large lateral displacements. In this work, we use full factorial design to analyse the effect of different mooring system design parameters on the ability of the floater to relocate its position. The analysis shows that each design parameter has a different way of affecting the FOWT’s response. The mooring lines’ headings control which wind directions cause the biggest displacements in the crosswind direction. The smaller the lines’ diameters the higher the displacements of the FOWT. Finally, the longer the line length the smaller the mooring system’s stiffness and hence the larger the FOWT’s displacement. The results of this study can be used as the basis for floating wind farm optimization, in which the wind turbines are allowed to passively relocate their positions according to the wind speed and wind direction.

Wake effects are a key challenge in the design and analysis of wind farms. For floating wind farms, the platforms offset under the aerodynamic loading of the turbine and are constrained by mooring systems that can vary significantly in allowable offsets. When considering wake steering, the crosswind offset of the turbine can counteract the lateral deflection of the wake. This work presents a tool to efficiently model the coupled impacts of wake steering and platform offsets for floating wind farms. The tool relies on the frequency-domain wind farm model RAFT and the steady-state wake model FLORIS. A verification with FAST.Farm is presented, then the tool is applied to a simple two-turbine case study. A range of mooring systems with increasing platform offsets and varied yaw misalignment angles are considered while comparing the impact on turbine power. Additional sensitivities to turbine spacing and mooring system orientation are explored. The results show that there is a least-optimal watch circle width for downwind turbine power production that varies with yaw misalignment angle and turbine spacing. Additionally, the turbine offsets under yaw-misaligned conditions vary significantly depending on mooring system orientation relative to the rotor plane, which in turn impacts the optimal misalignment angle. These results highlight the importance of including floating platform offsets and mooring systems in the evaluation of wake steering strategies for floating wind arrays.

As we cluster wind turbines in wind farms to gain energy from sites with high wind speeds, wake losses occur within the wind farm. Wake loss is a term used to describe the lower energy production of a downwind turbine that is totally or partially in the wake of an upwind turbine. To decrease wake losses inside the wind farm, the wind farm’s layout is optimized. However, a variety of factors constrain the wind farm layout optimization, such as the size of the lease area relative to the number of turbines to be placed, or the shape of the lease area. Therefore, many wind farms end up with a regular grid layout, such as the Horns Rev 1 wind farm in the North Sea. The ability of a floating offshore wind turbine (FOWT) to change its position based on the wind direction and its mooring system design presents an opportunity to further decrease wake losses in floating wind farms. In this work, we integrate the design of the FOWT mooring systems with the floating wind farm layout design with the goal of increasing the farm’s annual energy production. We use the Horns Rev 1 wind farm as a case study to demonstrate our method. The results show that allowing the FOWT to relocate can decrease wake losses up to 18%. Moreover, the newly developed mooring systems are less stiff and therefore allow larger motion of the FOWT; hence, the material cost of the mooring system decreases by an estimated 17%.

Wake losses inside a wind farm occur due to the aerodynamic interactions when a downwind turbine is in the wake of upwind turbines. The ability of floating offshore wind turbines (FOWTs) to relocate their positions in the horizontal plane introduces an opportunity to decrease the wake losses in a floating wind farm (FWF). Our goal is to use this ability to passively move the downwind FOWT out of the wake of upwind ones. Since the mooring system (MS) attached to a FOWT is responsible for its station keeping, the horizontal motions of the FOWT depend on the MS design. Hence, if we can design the MS to passively move the FOWT out of the wake, we can increase the FWF annual energy production (AEP). In this paper, we investigate if we can benefit from relocating FOWTs in a FWF and increase its AEP. In addition, we present a novel approach that considers the ability of a FOWT to relocate its position as a new degree of freedom (DoF) in the FWF layout design. This means we will have a self‐adjusting wind farm layout where the FOWTs passively re‐arrange themselves depending on the wind direction and the wind speed. Consequently, we will have a slightly different wind farm layout for every wind direction and every wind speed. To achieve this layout, we include the MS design as part of the FWF's layout design. In a self‐adjusting FWF layout, each FOWT is attached to a customized MS design allowing it to relocate its position in the best way possible according to the wind direction, to increase the overall AEP of the wind farm. The results of one case study show that the novel approach can increase the FWF's AEP by 1.6% when compared with a current state of the art optimized floating wind farm layout. Finally, we implemented our method as an open‐source python tool to be used and enhanced further within the wind energy community.

This paper examines wake effects for floating wind farms with shared anchors. Three 20-turbine farms are examined: a grid-formation baseline with no shared anchors, a farm based on 3-line anchors, and a farm based on 6-line anchors, governed by a wind turbine spacing of 8 rotor diameters. The IEA 15 MW reference turbine on the UMaine semisubmersible platform was used with a taut mooring system in deep- water depths representative of U.S. west coast lease areas. A steady-state wake model showed that, when evaluating a sweep of wind headings, the baseline design had the lowest wake losses, with a value of 11.7 %, followed by the 6-line at 12.9% and the 3-line at 13.8%. Dynamic simulations were run in FAST.Farm to analyse the effects of wakes on mean anchor loads for wind headings that showed significant wake losses. The baseline farm showed the largest anchor load reductions due to wake effects (up to 16%), followed by the 3-line farm (up to 8%), and the 6-line farm, which showed relatively consistent load magnitudes on the 6-line anchors across all headings.

This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.

Offshore wind farm layout design techniques and methodologies currently focus on fixed bottom offshore wind turbines. As more floating wind farm (FWFs) are planned, new methodologies for FWF layout design and optimization are required to consider the different attributes between fixed bottom and floating offshore wind turbines (FOWTs). One main difference is the ability of FOWTs to move in the horizontal plane (surge and sway motions). In our work in [1], we showed that the motions of the FOWTs in the horizontal plane represent an opportunity which can be used to increase the FWF’s annual energy production (AEP). We can passively control the motions of the FOWTs in a FWF according to the incoming wind direction, moving the downwind turbines out of the wake of upwind ones. Since the horizontal motions of a FOWT are governed by the mooring system design (MSD) attached to it, this passive control can be done by designing a customized mooring system (MS) for every FOWT in the farm. In this work we build on our work in [1], as we carry out a sensitivity analysis to check the effect of the farm size and the wind rose on the methodology we introduced. The results show that the percentage increase of energy production due to passively relocating the FOWT is sensitive to the FWF size. Very small FWFs will gain less energy by relocating the FOWTs. Moreover, we show that even with a more uni-directional wind rose relocating the FOWTs in a FWF remains beneficial.

The EU Horizon 2020 project COREWIND (COst REduction and increase performance of floating WIND technology) has developed two floating platforms for the new International Energy Agency (IEA) Wind 15 MW reference wind turbine. One design – “WindCrete” – is a spar floater, and the other – “Activefloat” – is a semi-submersible floater; both designs are made of concrete. In this work the design of the floaters is introduced with their aero–hydro–servo-elastic numerical models, and the responses of both floaters in both static and dynamic simulations are investigated. The static displacements and natural frequencies are simulated and discussed. Additionally, the effects of the mean wave drift forces and second-order difference-frequency wave forces on the systems' responses are presented. The increase in the turbine's power capacity to 15 MW in IEA Wind model leads to an increase in inertial forces and aerodynamic thrust force when compared to similar floating platforms coupled to the Technical University of Denmark (DTU) 10 MW reference model. The goal of this work is to investigate the floaters' responses for different load cases. The results in this paper suggest that at mild wave loads the motion responses of the 15 MW floating offshore wind turbines (FOWTs) are dominated by low-frequency forces. Therefore, motions are dominated by the wind forces and second-order wave forces rather than the first-order wave forces. After assessing and understanding the models' responses, the two 15 MW FOWT numerical reference models are publicly available to be used in the research and development of floating wind energy.

One of the main differences between floating offshore wind turbines (FOWTs) and fixed-bottom turbines is the angular and translational motions of FOWTs. When it comes to planning a floating wind farm (FWF), the translational motions introduce an additional layer of complexity to the FWF layout. The ability of a FOWT to relocate its position represents an opportunity to mitigate wake losses within an FWF. By passively relocating downwind turbines out of the wake generated by upwind turbines, we can reduce wake-induced energy losses and enhance overall energy production. The translational movements of FOWTs are governed by the mooring system attached to it. The way a FOWT relocates its position changes if the design of the mooring system attached to it changes. Additionally, the translational motion of a FOWT attached to a given mooring system is different for different wind directions. Hence, we can tailor a mooring system design for a FOWT to passively control its motions according to the wind direction. In this work, we present a new self-adjusting FWF layout design and assess its performance using both static and dynamic methods. The results show that relocating the FOWTs in an FWF can increase the energy production by 3 % using a steady-state wake model and 1.4 % using a dynamic wake model at a wind speed of 10 m s-1. Moreover, we compare the fatigue and ultimate loads of the mooring systems of the self-adjusting FWF layout design to the mooring systems in a current state-of-the-art FWF baseline design. The comparison shows that with smaller mooring system diameters, the self-adjusting FWF design has similar fatigue damage compared to the baseline design with bigger mooring system diameters at rated wind speed. Finally, the ultimate loads on the mooring systems of the self-adjusting FWF design are lower than those on the mooring systems of the baseline design.

To achieve cost-effective deployment of floating offshore wind farms, it is necessary to reduce mooring costs of Floating Offshore Wind Turbines (FOWTs). Beyond the cost, in terms of environmental impact, the seabed disrupted area due to mooring lines should be mitigated with care. The objective of this paper is to shed light on design parameters for cost- effective and low-footprint mooring configurations for FOWTs using coupled dynamic analyses. A design space is explored for mooring configurations with different pretension ratios, laid down length ratios and clump weight sizes. Ultimate and fatigue load cases are simulated in OpenFast to compute the floater motions, mooring line tensions and fatigue damage. With constant pretension ratio of 0.15 and adding clump weights of 40t, mooring line length, mooring footprint and peak tension can be reduced by 14%, 15% and 9% respectively, while maximum surge and fatigue damage increase by 25% and 12% respectively. This paper will serve as a basis for further work on mooring design in the EU H2020 funded project COREWIND and provide a practical reference for the mooring system design for FOWTs.