The field of floating offshore wind energy is rapidly advancing, offering a promising solution to
harness stronger and more consistent wind resources in deep-water regions, where conventional bottom-fixed turbines are not viable. Floating Offshore Wind Turbines (FOWTs), mounted on floating substructures, enable the deployment of wind turbines in deep offshore locations without the need for extensive foundations. This approach can significantly lower project costs, primarily by reducing substructure procurement expenses for comparable water depths. As a result, the installed capacity of FOWTs is projected to reach approximately 18.9 GW by 2030.

However, the design of cost-efficient FOWTs presents significant challenges due to the complex interactions between aerodynamic and hydrodynamic loads, as well as the coupling of various subsystems. One of the key hurdles is reducing the Levelized Cost of Energy (LCoE) to ensure the economic viability of Floating Offshore Wind Farms (FOWFs). Additionally, the flexibility of the floating substructure, which moves in six degrees of freedom (DOF), introduces complexities in the wake profile, such as enhanced wake meandering, which may affect the wake losses and the overall farm energy production.

Despite the potential impact of floater movements on energy yield, the specific effects on optimal wind farm layout design remain under-explored. Furthermore, the impact of these displacements on the LCoE needs to be examined to determine whether incorporating floater displacements into optimization frameworks leads to more accurate results or merely adds unnecessary complexity and computational cost.

This thesis evaluates the role of static floater displacements, specifically tilt and surge, in the optimization of FOWFs. Two distinct scenarios have been compared to assess how these displacements affect energy yield, the LCoE, and the optimal wind farm layout. The first scenario (Case A) disregards the static equilibrium displacements of the floating turbines, while the second scenario (Case B) incorporates these displacements into the analysis. To conduct the analysis, this thesis develops a wake modeling strategy focusing on wind-induced floater displacements, wherein the tilt and surge displacements of the floater are computed for each turbine using the PyWake framework, based on the effective wind speed experienced by its rotor. This wake modeling approach is then integrated into an OpenMDAO-based optimization framework to minimize the LCoE of the wind farm, which is used for the comparison between the two cases, to achieve the objectives of this study.

A case study of a 42-turbine sample wind farm in the North Sea, arranged in a rectangular gridded layout, was analyzed using the optimization framework. The results showed minimal differences in AEP, LCoE, and layout design between the scenarios that account for floater displacements (Case B) and those that neglect them (Case A). However, the computational time required for the optimization process increased by approximately fourfold for Case B. Case B resulted in a 0.25% reduction in AEP and a 0.162 €/MWh increase in LCoE, primarily due to changes in wake behavior caused by tilt and surge displacements. The optimal layout also shifted in orientation and spacing, with Case B yielding slightly lower power in the final configuration. Despite these effects, the overall impact on the LCoE was modest, suggesting that while incorporating floater displacements could potentially improve the accuracy of results for FOWFs, it might not justify the added computational costs for larger wind farms.

In recent years, the interest in deep-water wind energy projects has drastically increased, driven by the considerable wind resource in deep-water locations. In such locations, floating wind turbines are more economically attractive than bottom-fixed wind turbines. Nevertheless, the floating wind industry faces numerous challenges. One major challenge is the high investment costs involved in the manufacturing of the substructure. Additionally, it is important to maintain the wind turbines’ power output within the required standards for grid connection. Therefore, the design and optimization of substructures for floating wind turbines has to account for both capital costs and power quality. To investigate the existing research on the optimization of floating wind turbines, a literature review was conducted. Evidently, there is limited research investigating both cost and power quality optimization. Furthermore, the majority of the optimization techniques presented are accompanied by a computationally expensive substructure analysis. Moreover, in the early stage of the substructure design, a variety of different alternative scenarios are tested, requiring an extensive number of optimizations. Thus, the computational resources required by the optimization become a major concern. One promising approach to address this issue is substituting a computationally expensive analysis with a surrogate model. This solution could significantly increase the computational efficiency of the optimization process.

This thesis investigates the trade-off between substructure capital cost and power quality optimization. To achieve that, a multi-objective optimization workflow is developed for the case of a semi-submersible substructure. In parallel with this task, the optimization’s analysis blocks requiring most of the computational resources are identified and substituted with a surrogate model. Two different surrogate model implementation approaches have been examined. The first approach substitutes the optimization’s substructure analysis tool (NREL’s RAFT) entirely. This approach is called the ”End-to-End” approach. The second approach substitutes a specific analysis block of RAFT that computes the floater’s hydrodynamic properties. This analysis block requires most of RAFT’s computational resources. This approach is defined as the ”PyHAMS” approach.

The thesis outcome shows that improving the quality of power output comes with the drawback of increasing the substructure’s cost. The Pareto front between these two competing criteria is dominated by designs of long pontoons and reduced chain thickness. This is the most economical approach towards reduced cost and improved power quality. These Pareto designs are mainly driven by the boundaries of the design variables. Additionally, the Pareto designs are affected by constraints on the maximum pitch, maximum surge, and the existence of vertical loads on the mooring line’s anchor. Regarding the surrogate model-based optimizations, there is a dramatic reduction in the computational resources required to conduct the optimization. Among the two surrogate model approaches, the ”PyHAMS” approach achieves a better accuracy at mimicking the original optimization workflow than the ”End-to-End”. Instead, the ”End-to-End” approach is the most computationally efficient one to conduct the optimization. Nevertheless, when factoring in the computational resources to produce the training dataset, the ”PyHAMS” approach is the most computationally efficient one. This is the outcome of utilizing a reduced training dataset compared to one utilized by the ”End-to- End” approach.

Renewable forms of energy, and especially wind energy, have become an integral part of the energy infrastructure around the globe. However, the constant increase of installed wind capacity has put a strain on the selection of potential onshore locations for wind turbine installation. As a result, the wind industry is focusing on offshore applications, mainly with the use of bottom-fixed sub-structures. Nevertheless, research community has started shifting their interest in floating wind turbine designs to harvest the abundant wind potential of far-offshore locations, sited in water depths where monopiles and jacket sub-structures are not economically feasible. Despite the progress on the design of such floating units, there is still no consensus upon their optimum installation and maintenance strategies, activities which comprise one third of the lifetime cost of a floating wind farm.

The aim of this research is to gain understanding on the optimum installation and maintenance strategies for semi-submersible and spar buoy floating units based on their cost and duration. Towards this direction, four different installation strategies are investigated: semi-submersible installation with wind turbine assembly at quayside, semi-submersible installation with wind turbine assembly at wind farm site, spar buoy installation with wind turbine assembly in a single lift with a heavy lift vessel (HLV) and spar buoy installation with wind turbine assembly with multiple lifts with a crane barge. Furthermore, based on the type of the required maintenance activities, the onsite minor and major repairs of wind turbines with the use of a crew transfer vessel (CTV) or a walk-to-work (W2W) vessel are examined, as well as the onsite or offsite replacement of major wind turbine components for wind turbines installed on both semi-submersible and spar buoy floating sub-structures. In the context of this thesis, a deterministic installation and O&M model was developed. This model relies on various input data, including weather time series for near-shore and far-offshore locations, vessels and facilities costs, personnel and components costs and an electricity price for the calculation of the lost revenue due to downtime.

The analysis of the installation and maintenance strategies is based both on a base case scenario and a sensitivity analysis of the main factors influencing the cost and duration of offshore activities: distance from shore/port and weather limits of activities. The results show that semi-submersible installation with wind turbine assembly at quayside is the most cost-effective and fastest of all implemented strategies, followed by the spar buoy installation with HLV. Furthermore, the use of a CTV for onsite repairs is more cost-effective, albeit slower, approach than the W2W one, for all examined distances and weather limits. Finally, the offsite major replacement strategy for semi-submersibles is the most cost-effective of the examined strategies. No definitive conclusion can be drawn on the best major component replacement strategy for spar buoys, as the cost difference between the onsite and offsite approach is small. When the cost difference of different installation and maintenance strategies is low, the final choice depends on the volatile vessel rates and the electricity market prices.