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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 study presents an integrated methodology for evaluating operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs), incorporating vessel motion dynamics. By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, vessel motion effects during offshore operations are modeled. The approach is demonstrated in a case study at two wind farm sites, Marram Wind and Celtic Sea C. Three major component replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy yielded the highest O&M costs—94 kEUR/MW/year at Marram Wind and 97 kEUR/MW/year at Celtic Sea C—due to the extended MCR durations (90–180 days), leading to lower availability (90–94%). In contrast, the FTF and SHC strategies offered significantly lower costs and downtime. The SHC strategy was most cost-effective, reducing costs by up to 64% while achieving 97–98% availability. The integrated approach was found to be either more restrictive or more permissive depending on the specific sea states influencing the motion responses. This variability highlights the critical role of motion-based dynamics in promoting safe and efficient O&M practices, particularly for advancing FOWT operations.

The study presents a methodology for floating wind farms site selection with a Canary Islands case study. The frame combines geographical information systems (GIS) and multiple criteria decision methods (MCDMs). First, the problematic areas for the installation of the turbines are identified through a GIS database application. This tool generates thematic layers representing exclusion criteria. Then, at the second stage of the study, available maritime locations are analyzed and ranked using the analytical hierarchy process (AHP), based on technical, economic, and environmental aspects. AHP’s technique guarantee the elimination of the judgment’s subjectivity. The study also compared the solutions of the AHP technique with other methods, such as Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE), ELimination Et Choix Traduisant la Realité (ELECTRE III), Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) and Weighted Sum Algorithm (WSA(). The main result of this study is the creation of a realistic and objective overview of floating offshore wind farm site selection and the contribution to minimize the environmental impacts and to reduce the social conflicts between stakeholders.

This paper addresses subsea electric cable routing using the application of decision support systems combined with the experts’ knowledge. The methodology is successfully applied to a case study on the Spanish coast. The ranking method calculates the multiple criteria weights, and the weighted product method determines the most suitable space. The environmental criteria, with a weight of 61.4%, exceed the significance of other essential criteria in the study based on experts’ considerations. These rankings are input into the model to extract the suitable spaces to deploy the underwater cable. The final result accurately highlights an optimal route in alignment with the experts’ preferences.

As floating offshore wind progresses to commercial maturity, wake and array effects across a farm of floating offshore wind turbines (FOWTs) will become increasingly important. While wakes of land-based and bottom-fixed offshore wind turbines have been extensively studied, only recently has this topic become relevant for floating turbines. This work presents an investigation of the mutual interaction between the motions of floating wind turbines and wakes using FAST.Farm. While FAST.Farm has been extensively validated across a wide range of conditions, it has never been validated for FOWT applications. Hence, in the first part of this work, we validate FAST.Farm by comparing simulations of a single FOWT against high-fidelity results from large-eddy simulations available in the literature. The validation is based on wake meandering, mean wake deflection, and velocity deficit at different downstream locations. This validation showed that the original axisymmetric (polar) wake model of FAST.Farm overpredicts the vertical wake deflection induced by shaft tilt and floater pitch, while the new curled wake model is capable of properly capturing the vertical wake deflection. In the second part, we use FAST.Farm to analyze a small three-unit array of FOWTs with a spacing of 7 diameters across a wide range of environmental conditions. The same National Renewable Energy Laboratory 5 MW reference wind turbine atop the OC4-DeepCwind semisubmersible is adopted for the three FOWTs and for the validation against high-fidelity simulations. To assess the effect of the floating substructure, we compare the power production, tower-base moments, and blade-root moments obtained for the floating turbines with the results obtained in a fixed-bottom configuration. The main differences introduced by the floating substructure are the motions induced by the waves, the change in the natural frequencies of the tower caused by differences in the boundary condition at its base, and the larger vertical deflection of the wake deficit due to the mean pitch of the platform. The impact of these differences, as well as other minor effects, are analyzed in detail.

The energy produced from renewable sources (solar, wind, hydro, geothermal, and biomass) provides direct access to clean and safe energy. Offshore wind energy, generated through wind farms, has traditionally relied on fixed structures, whereas innovative floating structures have been commercially applied since 2017. This study investigates offshore areas in Montenegro suitable for wind farm construction. Research on average annual wind speeds has successfully identified a surface area deemed suitable for constructing a wind farm in the Montenegrin part of the Adriatic Sea. Analysis of available bathymetric databases has pinpointed technical solutions for the supporting structures of wind turbines required to construct an offshore wind farm. Applying an assessment method to the defined surface of Montenegrin waters, seven blocks have been identified as suitable for wind farm construction. The research results indicate that wind farms can be built in Montenegrin waters with a technical potential corresponding to a total capacity of 2299.794 MW, which includes 2034.48 MW for floating structures, 126.759 MW for fixed structures, and 138.555 MW for jacket-fixed structures.

An approach is developed for floating wind farm installation by creating a model that allows the planning of the main logistics and installation aspects of a floating wind farm. The method aims to preserve the coherency between logistic methods and project performance, as floating wind farm installation is a complex problem, due to the number of components that impose specific constraints in areas such as transportation and manufacturing. The planning tool is developed based on the knowledge about how to use the various approaches and on the analysis of different logistical solutions. A techno-economic view of the logistics and installation involved in the floating wind farm design is the main objective of this study. A case study for the CENTEC TLP concept implemented in Spanish and Irish waters is performed. Case study findings reveal the major aspects that affect the design and logistics factors. The tool is developed for the main types of floating wind platforms and is applicable for the preliminary design of wind farms worldwide. The tool suggests a strategy of design and logistic aspects dependent on a few inputs such as the number of components for installation, distances and the maximum number of turbines. These findings are especially important for the new floating wind farms being positioned further offshore.

This study presents an integrated methodology for evaluating operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs), incorporating vessel motion dynamics. By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, vessel motion effects during offshore operations are modeled. The approach is demonstrated in a case study at two wind farm sites, Marram Wind and Celtic Sea C. Three major component replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy yielded the highest O&M costs—94 kEUR/MW/year at Marram Wind and 97 kEUR/MW/year at Celtic Sea C—due to the extended MCR durations (90–180 days), leading to lower availability (90–94%). In contrast, the FTF and SHC strategies offered significantly lower costs and downtime. The SHC strategy was most cost-effective, reducing costs by up to 64% while achieving 97–98% availability. The integrated approach was found to be either more restrictive or more permissive depending on the specific sea states influencing the motion responses. This variability highlights the critical role of motion-based dynamics in promoting safe and efficient O&M practices, particularly for advancing FOWT operations.

By optimizing the positions of wind turbines in a wind farm, the power loss caused by wake effects can be reduced maximally. A new methodology of layout optimization is proposed utilizing a full-field wake model that integrates the near-field and far-field wake models after modifications, and a random search (RS) algorithm improved with a scale factor for acceleration in high-density layouts. The methodology is applied to a floating wind farm composed of modular platforms, which have a novel configuration and the ability to face toward the wind direction. The applicability and efficiency of the methodology and the improved RS algorithm are validated. The power production of optimized layouts shows a flat crest with an increased number of wind turbines. There is a layout with maximal output power in the wind farm. The real optimal layout should be determined in consideration of both output power and cost. Two sizes of platforms with different number of modules are compared in the application. The wind farm with smaller platforms produces more power. For comparison, a pattern search (PS) algorithm is also implemented in the application. The improved RS algorithm shows outperformance compared with the original RS and the PS algorithm.

Floating offshore wind turbines (FOWTs) have become a promising solution for harnessing wind energy in deeper seas. However, the complex interplay between FOWT layout, mooring line patterns, and wake effects significantly influences the overall performance of a floating offshore wind farm (FOWF). This paper proposes a novel optimization methodology that integrates mooring line constraints into the FOWF layout optimization process. The wake-induced power deficit is considered, whereas the vortices are neglected. The new method considers the constraint areas for each FOWT, which are defined based on both mooring line buffer zones and wind turbine buffer zones. By defining constraint areas, the optimization process ensures that FOWTs are optimally positioned while avoiding interference and collisions. By carefully considering the buffer zones, the power potential of FOWFs with three-line, four-line, and six-line mooring configurations can be improved by 122%, 100%, and 78%, respectively. Then, a genetic algorithm is employed to optimize the FOWT positions and mooring line angles simultaneously. The effectiveness of the proposed method is demonstrated through a case study in Guangdong, resulting in a significant 5% increase in power output potential compared to conventional approaches. This research contributes to the advancement of FOWT layout optimization and provides valuable insights for the design and deployment of future FOWFs.