With increasing computational power, Large-Eddy Simulations (LES) are becoming more prevalent for high-fidelity wind farm analysis. Accurately representing wind turbines in these simulations requires knowledge of blade load distributions, often obtained from Blade Element Momentum (BEM) theory. However, BEM requires numerous geometric and aerodynamic parameters, which are frequently unavailable in industrial settings due to confidentiality. Consequently, industry-scale LES are often limited to uniform Actuator Disk (AD) models, which lack accuracy and cannot capture load distributions.

This thesis implements an Analytical Body Force Model (ABFM) that estimates blade load distributions from LES data using limited turbine information. Both one-way and two-way coupling strategies are explored. In the one-way approach, the ABFM supplements the AD model to compute blade forces without influencing the LES. In the two-way approach, the ABFM forces are actively fed back into the LES in a feedback loop, replacing the uniform AD model and improving simulation fidelity.

The transition to a sustainable energy system has accelerated the integration of renewable energy sources (RES), such as wind and solar power, into electricity markets and infrastructures. While these sources contribute significantly to the decarbonization of the energy sector, their inherent variability and limited predictability introduce operational challenges for maintaining system balance and ensuring cost-effective grid utilization. Additionally, the geographic distribution of RES, often located in remote areas, has exacerbated issues of grid congestion, leading to limitations in grid access, rising curtailment levels, and deferred renewable projects. In response to these challenges, Hybrid Power Plants (HPPs), which co-locate RES with Battery Energy Storage Systems (BESS), have emerged as a viable solution offering increased operational flexibility, improved forecast error mitigation, and enhanced economic performance, while utilizing one grid connection point.

This study evaluates the added economic and energetic value of co-locating a BESS with a RES under the Dutch electricity market structure. A multi-stage stochastic optimization framework is developed to simulate HPP participation across the day-ahead, intraday, and imbalance markets. The model incorporates power forecast uncertainty through scenario generation and reduction techniques, and captures the physical constraints of energy systems, including battery operations and capacity, RES capacity, and restricted grid connection capacity. Optimization is conducted on a rolling horizon to reflect the sequential nature of market decision-making.

Three configurations are analyzed: a standalone RES system, a standalone BESS, and a co-located HPP. These systems are evaluated based on simulated operations over four weeks each representing a season using real market prices and wind power data. The co-located HPP demonstrates superior performance in terms of both economic return and renewable energy utilization. By enabling time-shifting of generation, reducing curtailment, and participating more effectively in short-term markets, the HPP captures additional value that standalone systems cannot access. Moreover, the ability to operate flexibly within a fixed grid export limit allows the HPP to relieve grid congestion, using storage to shift energy dispatch in line with the needs of the electricity system, indicated by price signals, thereby reducing strain on network infrastructure.

A comprehensive analysis investigates the impact of key assumptions regarding price and power forecasts and operational parameters, such as battery size, technology characteristics, grid connection capacity, and reoptimization frequency. Results indicate that system performance is highly dependent on the quality of imbalance price forecasts and the ability to respond dynamically to power forecast updates and market prices. This has been demonstrated by a comparison between no foresight, perfect foresight and using the day-ahead clearing price as imbalance forecast. Moreover, allowing the HPP to withdraw energy from the grid, results in signficant economic gains, however, this comes at the cost of renewable energy utilization. Although the study excludes capital and degradation costs, the findings underscore the operational advantages of co-locating RES and BESS under uncertainty and grid limitations.

Overall, this research contributes to a deeper understanding of how flexible, market-responsive HPPs can support the transition to a resilient and economically efficient low-carbon power system. It highlights the importance of integrated modeling approaches for optimizing renewable dispatch strategies in evolving electricity markets, while also demonstrating how HPPs can contribute to better utilizing grid connection capacity and enabling greater renewable integration.

Wind energy is central to many international climate goals which promote the transition to carbon-free energy sources. Most commercial wind turbines at present are horizontal axis wind turbines (HAWTs). However, vertical axis wind turbines (VAWTs) can fill two gaps in wind energy: offshore floating wind energy and urban wind energy, in part due to their insensitivity to the incoming wind direction, low center of gravity, high power density, and low noise production. Historically, VAWTs have suffered from fatigue issues, low power efficiency, and difficulty in self-starting. Active variable blade pitch control can address these issues. It can be used to maximize the power efficiency, minimize blade load fluctuations to alleviate fatigue, and minimize torque fluctuations. Numerous studies on VAWT blade pitching have been done in the past, both numerical and experimental, and both with fixed and variable blade pitch. Numerical studies use models with varying accuracy. If pitch optimization is conducted, the most common objective is maximizing the power coefficient, C_p. However, blade pitch control can also be used to improve the loading characteristics on a VAWT.

In this study, individual active variable blade pitch control optimization is performed with multiple objectives, recognizing that there are multiple uses of pitch control for VAWTs. The goal of this study is to develop an optimal pitch function(s) that maximizes C_p and minimizes the fluctuations in rotor normal force and torque. The pitch angle is implemented as a third-order sinusoidal function. This is a numerical study, which uses the two-dimensional actuator cylinder (2D AC) model. The unified non-dominated sorting genetic algorithm III (U-NSGA-III) is used to perform pitch function optimization. The multiple objectives are 1) maximize C_p, 2) minimize rotor normal load fluctuations, and 3) minimize rotor torque fluctuations. The objectives are built up in successive optimization cases resulting in three optimization problems, a single-, two-, and three-objective optimization problem, respectively. Solutions are found which achieve all three objectives at the same time. Adding the second and third objectives changes the optimal solutions, and the multiple objectives are important to consider because only maximizing C_p leads to an increase in unfavorable normal load and torque fluctuations.

When only maximizing C_p is considered, the power coefficient can be increased by 17.3% over the base case (with zero pitch). The power coefficient can be increased by 10.3% over the base case without increasing the normal load fluctuations or the torque fluctuations. The normal load fluctuations can be reduced by 22.1% compared to the base case while maintaining the power coefficient and torque fluctuations. The torque fluctuations can be reduced by 13.9% compared to the base case while maintaining the power coefficient and normal load fluctuations. The optimal pitch functions which maximize C_p and/or minimize the torque fluctuations require high variations in the pitch angle throughout the rotor revolution. Meanwhile, optimal pitch functions which minimize the normal load fluctuations require fewer oscillations (they resemble more first-order sinusoids). For the majority, the optimal pitch functions pitch the blade outward in the upwind half and inward in the downwind half of the revolution, though adjusting the pitch angle around the transitions between the upwind and downwind halves can increase the power coefficient. Optimal pitch functions which minimize the normal load fluctuations reduce the angle of attack and loads throughout most of the revolution and shift the loading on the rotor toward the downwind half as compared to the upwind half. Minimizing the torque fluctuations requires pitch functions with relatively large amplitudes. The pitch functions spread out the blade loading and power generation across the revolution by reducing the loads and power generation in the middle of the upwind and downwind regions and increasing them around the transitions between the upwind and downwind regions. In some cases, the optimal pitch functions are transferable to other operating conditions and turbine designs. They can lead to an increase in C_p for a certain range of tip speed ratios, however, the optimal pitch functions depend on the tip speed ratio. The optimal pitch functions and their effectiveness are similar when the turbine has three compared to two blades. Implementing an optimal pitch function found in this study can increase the power output of VAWTs while not compromising their structural integrity and/or alleviate fatiguing load fluctuations while maintaining the power output. Optimal blade pitching can lead to a significant improvement in the operating performance of VAWTs.

As wind turbine rotors grow larger, compressibility effects near the blade tip become an emerging concern. This research experimentally investigated compressibility effects on the flow field and aerodynamic performance of the FFA-W3-211 wind turbine airfoil at Mach numbers within 0.5–0.65 and angles of attack from -4° to -11°. An experimental campaign was conducted at the TST-27 wind tunnel at TU Delft using Particle Image Velocimetry (PIV) and Schlieren techniques. Non-intrusive pressure field reconstruction and load determination methods are used to infer the aerodynamic loads from the flow field data.

Results show that compressibility strongly influences the mean flow field and aerodynamic loads of the FFA-W3-211 airfoil. Trends toward transonic flow and unsteady shock waves were identified with increasing absolute angle of attack and freestream Mach number. The results at α = -6° display a 30% reduction in the negative lift coefficient from cl = -0.43 to -0.31 and a 190% increase in drag coefficient from cd = -0.026 to -0.075, with increasing Mach number from M∞ = 0.5 to 0.65. For the same Mach number range, the lift coefficient results at α = -10° display a plateau around cl ≈ -0.65 to -0.67, and a 100% increase in drag coefficient, from cd = -0.085 to -0.174. At moderate angles of attack, the increased mean drag was influenced by the growth of trailing edge separation. Beyond M∞ = 0.6, the emergence of shock waves plays a greater role in inducing earlier separation and less efficient pressure recovery. The growth of the separation region decreases the mean negative lift for α ≤ -6°. For α ≥ -8°, conversely, the emergence of supersonic flow and lower pressures over the trailing edge counteract the effects of increased separation. For the phase-averaged analysis of the transonic buffet cycle at α = -10° and M∞ = 0.65, the phase-averaged lift coefficient varies periodically in a range of approximately ~22% of the time-averaged lift coefficient, while the phase-averaged drag coefficient varies up to ~80% of the time-averaged drag coefficient.

This work is part of the foundation for understanding the effects of compressibility in wind turbine airfoils and wind turbines. It identifies the complex interplay of shock waves, separation, and shock wave–boundary layer interaction (SWBLI) in the transonic regime. The results highlight and challenge current assumptions of incompressible flow for the design and operation of modern and future large-scale wind turbines that rely on incompressible aerodynamic polars.

The transition to a decarbonised energy system requires a cost-effective, reliable and sustainable supply of renewable hydrogen, particularly for industrial hubs like the Port of Rotterdam. Multiple supply pathways could potentially contribute to such an energy system, with each pathway characterised by different advantages and disadvantages. Therefore, it is crucial to consider the system-wide implications of various supply pathways in order to make balanced trade-offs. This study functions as an exploratory investigation into the relations between various system designs and their characteristics. It examines techno-economic trade-offs between local hydrogen production using offshore wind-powered electrolysis and cracking of imported ammonia. Key performance indicators for system affordability, security of supply and sustainability are defined in order to facilitate a comparison of the two supply pathways. Employing a quantitative system model, these indicators are evaluated across different system configurations and demand scenarios for the Port of Rotterdam.
This research highlights the complexity of the energy transition and the absence of a universally optimal hydrogen supply pathway. Local electrolysis offers lower operational costs and import independence but requires high capital investment. In contrast, ammonia imports ensure a stable supply with lower local capital expenditure but depend on a resilient supply chain and are highly sensitive to ammonia costs. Local production demands more space but has a lower carbon footprint, while imports require less space at the import site but result in higher emissions. Additionally, electrolysis competes with power demand in Rotterdam’s harbour-industrial complex and hinterland, adding strain to their decarbonisation efforts. Local production also requires multiple salt caverns for storage due to constrained extraction rates, along with significant overcapacity in wind power and electrolysis to ensure supply continuity. Conversely, import-dependent systems rely on large ammonia shipments, and as hydrogen demand increases, local production reaches clear supply limits, leading to greater import reliance.
Future research should expand model applicability to different locations and supply chains, enhance key performance indicator scope, and incorporate stakeholder-driven decision-making frameworks. Ultimately, an effective hydrogen strategy must balance affordability, security of supply and sustainability, navigating the trade-offs between supply pathways and their broader implications for the energy transition.

The recent growth of the size of wind farms highlights the need for a deeper understanding of the mesoscale phenomena in a stably stratified atmosphere, such as atmospheric gravity waves, as the effect on the power generation can be significant. This thesis is a numerical study of the effect of wind farm layout on atmospheric gravity wave excitation and the resulting feedback on wind farm performance. Wind farms in this study have varying power density, streamwise or spanwise turbine spacing, aspect ratio, hub height, rotor diameter, shape, or orientation with respect to the freestream, and are situated in a conventionally neutral boundary layer in offshore conditions. Moreover, the wind farms can be horizontally or vertically staggered. To investigate the atmospheric gravity wave excitation, the effect of wind farm layout on the Froude number and inversion Froude number governing the internal and interfacial waves respectively is studied using several high-fidelity Large Eddy Simulations. Then, AGW wavelength and wind farm efficiency are parametrically studied using a fast reduced-order model. Specifically, the non-local efficiency is considered, which is a measure of the global blockage effect induced by the atmospheric gravity waves. It is found that the length scale used in the Froude number must be adjusted to the wind farm, and is dependent on the turbine spacing and farm shape. The inversion Froude number is based on the phase speed of the interfacial waves. It is suggested that the phase speed must be based on shallow-water theory and deep-water theory for small and large turbine spacings respectively. In other words, for small turbine spacings the wind farm acts as an entity, while for large turbine spacings, the farm acts as a collection of individual turbines. Finally, the streamwise and spanwise turbine spacing (and consequently the power density), and the aspect ratio primarily govern the non-local efficiency.

Although the percentage share of renewable energy in the global energy landscape is increasing rapidly and consistently breaking records for annual installation, there is still a need for tripling up of renewable energy capacity by 2030.The costs and prices of renewable energy are declining year by year, this is one of the key reasons for the shrinking of profits for renewable power developers(RPDs). Different strategies can be adopted by the renewable developers to increase revenues like improving financing costs, making multiple revenue streams for individual renewable assets etc. This creates an opportunity for P2X technologies, which are becoming popular due to their ability to facilitate the integration of different energy sectors like electricity and hard-to-abate sectors.

The design of a Hybrid power plant(HPP) is a complex problem that combines different assets to maximize the value of the power plant. Researchers at the Technical University of Denmark(DTU) are in the process of developing an open-source tool called HyDesign. According to desktop research, it was understood before HyDesign that no other open-source HPP sizing tool in the market could size and optimize the P2X designs. The purpose of this thesis study is to analyze how can RPDs improve their economic value by coupling with green ammonia(GNH3) production using HyDesign. The quantitative data is collected through the literature research and qualitative data is collected through attending various conferences and industrial events.This study models the most prominent and proven technology, which existed for more than a century now called the Haber Bosch process. Around 30+ industrial professionals were interviewed to entrepreneurially validate TUDelft's patented methodology of the Green Haber Bosch process. These interviews also provided reliable qualitative data for this study.

Since there is no reference, open-source green ammonia model, modelling checks were developed in this study to verify the functioning of the model and increase confidence in the reliability of the results. Site selection hypothesis was also made for hybrid renewable power plants. Evaluation results depict that the revenue of HPP+GNH3 is more than the HPP. However, various financial metrics were more for the HPP than HPP+GNH3, this is due to the huge technology costs of the GNH3 plants and the lack of economies of scale of such new plants.

Sensitivity analysis is performed in this study to analyse value addition and its source for improving various financial metrics of HPP+GNH3.Increasing RE resource capacity will improve financial metrics. Increasing electrolyser capacity or battery capacity will also improve a few of the financial metrics but not to the extent of increasing solar and wind capacities .

The study finds that building a large-scale HPP+GNH3 system is beneficial for RPDs and to society, which yields better financial benefits and more green ammonia production, which can be used in many hard-to-abate sectors.

Transitioning to renewable energy is essential for mitigating climate change and ensuring long-term energy security. Offshore wind farms offer a promising solution by harnessing strong and consistent wind resources at sea while minimizing land use conflicts. However, developing cost-effective offshore wind farm networks in the Baltic Sea—due to its unique geographical and environmental conditions—presents significant challenges.

This study addresses these challenges by developing a techno-economic optimization model to identify the most cost-effective offshore wind farm network configurations in the Baltic Sea Region. The model evaluates various configurations, including national (limited to domestic connections) and international networks (allowing cross-border connections), as well as single-stage (planning the entire network at once) and multi-stage (developing the network in phases) development plans. It optimizes the placement and capacities of wind farms, energy hubs, and export cables to minimize total system costs while adhering to technical and capacity constraints. Key geographical factors—such as water depth, ice cover, and proximity to ports—are integrated into the spatial data processing method to accurately assess infrastructure costs. The model also explores different network designs, including hub-and-spoke configurations that utilize energy hubs to consolidate and distribute power from multiple wind farms. The incorporation of single-stage and multi-stage optimization approaches allows for holistic and phased network developments, providing flexibility in planning and implementation.

The findings reveal that international networks are more cost-effective, totaling €28.5 billion, compared to national-only networks. This cost advantage is primarily due to enhanced flexibility in wind farm placement and power distribution. Specifically, national networks are 1.51% more expensive due to less optimized wind farm locations and localized grid overloading. Additionally, single-stage optimization outperforms multi-stage approaches by reducing costs by 1.71%, highlighting the benefits of a holistic planning strategy. While energy hubs can lower export cable costs by consolidating power transmission, they introduce higher overall expenses due to the additional costs associated with the hubs themselves. Specifically, the hub-and-spoke network is 1.15% more expensive than connecting wind farms directly to the onshore grid, suggesting a need for improved network optimization strategies. The study further identifies potential cost savings by positioning wind farms closer to shore in ice-covered areas when additional costs resulting from sea ice adaptation are minimized. This adjustment results in savings on export cables, as wind farms positioned closer to shore within these regions prove more economical due to their proximity and positioning in relatively shallow waters.

Ultimately, this research enhances the efficiency and economic viability of offshore wind infrastructure by optimizing design and cost estimation processes. The study provides actionable strategies for effective planning and investment in renewable energy infrastructure, offering valuable insights for policymakers and investors. By addressing the spatial, technical, and economic challenges associated with large-scale renewable energy deployment, the findings contribute significantly to academic knowledge and practical applications. The research emphasizes the importance of strategic long-term planning and international collaboration in developing sustainable and cost-efficient offshore wind energy infrastructures.

The ever-increasing need for improving the energy yield in wind farms while minimising fatigue loads has created the need for exploring new concepts in the wind sector. One is the notion of wind farms with turbines of varying hub heights, often called vertically- staggered (VS) configurations. Multiple studies have demonstrated the potential benefits of such arrangements, yet the effect of erecting larger-scale wind turbines into an already existing control wind farm has not been explored. This concept has gained remarkable industrial attention, particularly in the German onshore market, as it simplifies the grid connection and land acquisition, among other benefits. Understanding the implications of vertical staggering on turbine performance is therefore essential. In addition, fast and accurate modelling of the inflow conditions modulated by the atmospheric boundary layer (ABL), is crucial in predicting the flow properties and energy yield in wind farms. A novel steady-state, RANS-based inflow model has recently been proposed, yet it lacks detailed validation. The present research aims to assess the reliability of that model in large wind farm flow simulations and subsequently apply it to identify flow patterns and power production characteristics in VS configurations representative of the German onshore wind standards through numerical simulations. RANS modelling combined with the actuator disk method in the PyWakeEllipSys software was employed to demonstrate that despite the inability of that inflow model to yield accurate prediction of the turbulence intensity, the computed velocity field is in fair agreement with LES results in conventionally neutral boundary layer conditions. This is evident, especially for deeper ABL cases. This study argues for the complexity of the flow caused by the collocation of different turbine scales in the investigated VS configurations. It is also highlighted that rotor overlap negatively impacts the power production of both turbine types in VS wind farms. Hence, it is suggested that optimal performance in VS setups can be achieved by minimising the rotor vertical overlap through appropriate adjustments of the hub height.

The global energy consumption relies heavily on fossil fuels, which are projected to be depleted by 2050. This makes it necessary to optimize renewable sources such as wind energy. However, wind energy faces challenges due to the unpredictability and turbulence of wind patterns. Traditional wind turbines are limited by their reactive approach to turbulence. To address this issue, the integration of lidar technology in wind turbines provides a predictive advantage, enhancing the control of wind parks by offering insights into incoming wind
disturbances and improving feedback mechanisms. A new method for adjusting ground-based lidar turbulence intensity measurements is investigated in this master’s thesis, aiming to reduce measurement uncertainties. A novel turbulence intensity equation, developed using perturbation theory, serves as the foundation for an adjustment model that has been validated against existing standards. This adjustment method was tested in both virtual environments and on an actual wind site in the Netherlands, demonstrating its effectiveness in reducing uncertainties associated with lidar data. The study also utilizes the findings of a joint-industry project to refine data precision and compares various adjustment methods, ultimately showing that the perturbation-based adjustment improves the alignment of lidar measurements with traditional met mast data. Furthermore, the thesis explores the acceptance of lidar technology through a model based on the technology acceptance
framework, revealing that reducing measurement uncertainties positively impacts the perceived usefulness and adoption of lidar systems. This research aims to enhance the accuracy and reliability of wind energy assessments using ground-based lidar, paving the way for broader adoption in the industry. Future work should focus on expanding the dataset, incorporating advanced machine learning techniques, and extending the turbulence
intensity equation to accommodate complex terrains, further reducing uncertainties in lidar measurements.

This thesis presents a thorough exploration of mid-term storage strategies aimed at enhancing flexibility in hybrid offshore power systems that integrate wind and solar energy. A multi-criteria analysis was employed to identify the most suitable storage technologies for medium-term applications, resulting in the selection of Compressed Air Energy Storage (CAES), Lithium-ion batteries, and Lead-acid batteries as the top three candidates.
A specific study area, already equipped with offshore wind turbines, was selected for this research. Weather data were obtained and analyzed to reduce power mismatches and their associated costs. A multi-objective optimization approach was applied, focusing on minimizing the hourly loss of load and total capital costs associated with the hybrid renewable energy system.
In the base case scenario examined, which comprised 100% wind power, CAES emerged as the storage technology with the lowest total capital cost. However, by varying the proportions of offshore wind and solar energy, it was found that adjusting the mix to 80% offshore wind and 20% solar led to a significant reduction in the annual loss of load, thus reducing the total capital cost of the renewable hybrid system.

The demand for hydrogen is expected to increase significantly in the coming years due to its critical role in decarbonizing industrial processes. Offshore wind presents a viable energy source for powering electrolyzers, though it leads to intermittent production. While underground salt caverns are acknowledged as a promising solution for large-scale hydrogen storage, the technology still faces challenges regarding integration with fluctuating renewable energy sources.

This research, conducted in collaboration with Vattenfall, introduces a framework for modeling hydrogen storage in salt caverns, linked to hydrogen production from offshore wind farms. The framework features a technical model that addresses the thermodynamic changes within the cavern during hydrogen injection and withdrawal.

This new framework was expanded with an economic model and used to evaluate the optimal configuration for a standalone green hydrogen production and storage system. The analysis demonstrates that, up to a high security of supply threshold, increasing the storage capacity is the most effective strategy. Beyond this threshold, optimizing the configuration by increasing the number of stack changes or slightly enhancing the overall production capacity proves more beneficial than merely expanding storage. Additionally, the study explores two case study variations to assess the impacts of integrating flexible demand and line packing into the system.

The study concludes that the thermodynamic modeling of caverns significantly improves the accuracy of storage capacity estimations. It also highlights that optimal system design extends beyond adjusting storage capacity, necessitating comprehensive component optimization to strike a cost-effective balance for required supply securities. Furthermore, integrating demand flexibility enhances security of supply, enabling systems with high flexibility to achieve 100% security of supply with smaller storage capacities. However, the inclusion of line-packing does not improve economic performance. The insights underscore the need for strategic system design in hydrogen production and storage systems.

The decommissioning of offshore wind farms (OWFs) presents a significant challenge due to the environmental impact associated with the process, particularly in terms of greenhouse gas (GHG) emissions. This research aims to develop a model for quantifying and analysing the GHG emissions associated with large-scale OWF decommissioning. It examines key variables, including vessel types, decommissioning activities, transport strategies, and weather conditions, to assess their impact on total emissions.

Using a GHG inventory-based approach, the research applies both deterministic and probabilistic methods to assess emissions across various decommissioning scenarios. The model integrates operational logistics with emission factors, providing a flexible framework that adapts to different OWF projects. The research specifically examines the decommissioning of OWF Lincs Limited.

The results of this study highlight the potential for emissions reduction through optimised vessel strategies, transport methods, and campaign scheduling. While operational activities dominate emissions, the research underscores the importance of addressing external factors, such as weather and campaign timing, to minimise environmental impact. The developed model offers valuable insights to stakeholders aiming to implement effective GHG emission reduction strategies in future OWF decommissioning projects.

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.

This research evaluates the economic feasibility of adding offshore solar farms (OSFs) to planned offshore wind farms (OWFs) in Europe, identifying feasible ranges of OSF and subsidy levels to reach economic feasible projects. Ultimately, this leads to conditions to reach economically feasible projects for adding OSFs to OWFs. Using shared infrastructure, such as the power export infrastructure (PEI), it reduces the need for offshore grid expansion and lowers initial OSF investment costs. This study addresses a gap by examining the economic feasibility of this integration, specifically through an economic added value (EAV) analysis, combined with various OSF cost scenarios through a net present value (NPV) assessment.

First, a literature review explores the economic and technical benefits, focusing on shared OWF infrastructure and identifying relevant stakeholders, when adding an OSF. A techno-economic model is developed to calculate the economic added value based on increased OSF revenues and the added value subsidy (AVS), which reflects cost savings from using shared infrastructure. Subsequently, the model is applied to two OWF case studies, a wind farm site in the Netherlands and wind farm site in Italy, where economic feasibility is evaluated.

This study concludes that the addition of an OSF to an OWF at the Italian site is economically feasible, when an optimistic OSF cost scenario is used. With less optimistic OSF cost scenarios, the the addition of an OSF to an OWF would require higher AVS from increasing shared infrastructure costs to become feasible. The integration of OSFs shows greater economic potential in southern Europe, particularly in regions such as Italy, where abundant solar resources and a lower wind potential contribute to a higher EAV per unit of OSF capacity because of substantial growth of the PEI capacity factor. Locations with lower initial OWF capacity factors, greater increases in additional OSF capacity factors, and high shared infrastructure costs lead to faster NPV improvement and reduced dependence on subsidies, representing economically favourable conditions for adding OSFs to OWFs.

The escalating demand for green hydrogen as a sustainable energy carrier has sparked significant interest in offshore wind-to-hydrogen systems, which hold the promise of expediting the transition towards renewable energy sources. The objective of this research is to provide insight in the techno-economic feasibility of semi-centralised electrolysis in an offshore wind farm. The semi-centralised offshore wind-to-hydrogen configuration will be compared with centralised and decentralised offshore wind-to-hydrogen to potentially reduce the levelised cost of hydrogen (LCOH) in future wind-to-hydrogen production designs.

This research was conducted in collaboration with Vattenfall, a leading player in offshore wind energy within Europe, who recognizes the potential of green hydrogen as a key driver in the ongoing energy transition. Vattenfall provided access to an in-house wind farm layout optimisation model to create optimised wind farm layouts as well as site specific data for the case study. This model and data allowed a narrowed focus on the hydrogen aspects of the wind-to-hydrogen configurations.

The technical examination explores crucial elements such as the conversion of wind energy into hydrogen through electrolysis, hydrogen transmission and variances in offshore substations and hydrogen wind turbines, to understand the technical differences between the different offshore wind-to-hydrogen configurations. Additionally, by analysing the hydrogen production process and comparing the scale of hydrogen production in offshore substations or hydrogen wind turbines, the study exhibits the technical feasibility of a wind-to-hydrogen farm with numerous semi-centralised monopile hydrogen substations in comparison with wind-to-hydrogen farms consisting of a single centralised jacket hydrogen substation or decentralised hydrogen wind turbines.

To enable a quantitative comparison of the different offshore wind-to-hydrogen setups in the economic analysis, the LCOH for each configuration was modelled. This process involved creating wind farm layouts and calculating the associated cost for a variety of offshore substations using Vattenfall's optimisation model. Moreover, aspects such as hydrogen production, the dimensions and cost of hydrogen pipelines, and the weight and expense of offshore hydrogen facilities were modelled to estimate the costs associated with hydrogen production and transmission for each configuration.

In the economic analysis, a detailed case study is conducted. The research investigates cost drivers, including wind farm expenses, hydrogen substation investments, and energy transmission infrastructure costs. The results reveal the economic viability of the semi-centralised configuration. The findings highlight the importance of considering monopile load capacity and substructure costs in determining the optimal number of hydrogen substations for semi-centralised configurations. However, the decentralised configuration exhibits a 5\% lower LCOH compared to the centralised and semi-centralised configurations due to the lack of additional substructures and high voltage electrical equipment.

In conclusion, this research contributes comprehensive insights into the techno-economic feasibility of semi-centralised offshore wind-to-hydrogen configurations. The findings highlight the potential of semi-centralised configurations and call for further research and optimisations. Unlocking the potential of semi-centralised offshore wind-to-hydrogen configurations can drive the transition toward sustainable and renewable energy sources.

The replacement of conventional generation units with variable renewable energy sources could have a negative impact on the balance of supply and demand of electricity. Moreover, since the variable renewable energy sources are inverter based and therefore decoupled from the grid, the overall grid inertia will decrease. To solve this problem, the traditional mechanical frequency response can be re- placed by synthetic inertial response and fast frequency response (FFR) alternatives from wind turbines. However, there is uncertainty about the order of magnitude of these ancillary services and how these ancillary services will affect the ultimate and fatigue loads on the wind turbine. The aim of the study is to gain an understanding of the extent to which an offshore wind turbine can provide synthetic inertia and fast frequency response and how this affects the fatigue and ultimate loads on the wind turbine.

Simulations were performed in FASTTool to study the response of the IEA 15MW offshore wind turbine using the baseline controller, synthetic inertia controller and FFR controller. The moments at the blade roots and tower base were used to perform a fatigue and ultimate load analysis. Three different grid frequency data sets were used for the grid frequency inputs of the synthetic inertia controller and FFR controller. In addition, the response of the wind turbine was studied at two different response sizes for both the synthetic inertia controller and the FFR controller.

This study showed a significant increase in fatigue damage equivalent load and maximum stresses due to an increase in the response size of the synthetic inertia and FFR controller. The results therefore showed that the extent to which an offshore wind turbine can provide synthetic inertia and FFR depends mainly on the magnitude of the controller response and not on the size of the wind turbine.

Green hydrogen is expected to play a crucial role in the energy transition as it can be utilised for both storage purposes and as fuel for hard-to-abate sectors. One of the possible configurations for producing green hydrogen is by means of a central offshore hydrogen production platform powered by offshore wind energy. Producing hydrogen offshore facilitates the possibility to eliminate parts of the expensive electrical infrastructure present in conventional offshore wind farms and could result in lower losses.

In collaboration with Vattenfall, this study provides a thorough examination of centralised offshore hydrogen production. The study addresses both the techno-economic feasibility of the centralised configuration and the effects of aggregating the power supplied by individual offshore wind turbines.

In Part 2, the study presents a comprehensive techno-economic analysis of different electrolysis technologies, the optimal hydrogen production unit capacity, and compressor output pressure. The outcomes reveal that an optimal levelised cost of hydrogen can be achieved by reducing the hydrogen production unit capacity compared to the offshore wind farm capacity. Moreover, increasing the hydrogen pressure above the minimum pressure required to overcome the pipeline pressure drop, reduces the levelised cost of hydrogen further. The study reveals a small preference for hydrogen production based on proton exchange membrane electrolysis, in comparison to hydrogen production based on alkaline electrolysis.

In Part 2, the focus is shifted towards hydrogen production solely, influenced by power fluctuations. By using historical offshore wind turbine power output data, it was found that aggregating the power output significantly reduces the normalised power output fluctuations, compared to a decentralised offshore configuration. However, the reduced power fluctuations did not result in a higher hydrogen yield, attributed to the inter-array cable losses of the centralised configuration. Nevertheless, a significant reduction in the number of switches of operational mode and the number of turn-offs of the stacks was observed. Reducing the number of switches and turn-offs will reduce the process of stack degradation.

The study concludes that centralised offshore hydrogen production facilitates the possibility to produce hydrogen at a competitive levelised cost of hydrogen. Furthermore, the reduction of the power fluctuations will benefit this configuration due to the deceleration of the stack's degradation process.

Solar and wind will displace fossil fuels as the main source of energy in a net-zero world. However, renewable energy sources provide intermittent power, hence an energy carrier that enables the balancing of demand and supply is needed. Here green hydrogen comes in place.
In addition, the demand for green hydrogen is expected to rise in the coming decades.
Given the geographical wind resource availability combined with supportive policy, the supply of green hydrogen can be delivered through offshore electrolysis. Moreover, in addition to higher yield, going further offshore creates a stronger case for hydrogen export as power export would become impracticable.
The energy industry is investigating what kind of typology and system configuration is most suitable for offshore green hydrogen production. This report provides insight into the performance of a single wind turbine integrated electrolysis system. For which the effect of different control strategies, electrolyser dimensioning and electrolyser capacities are analysed.

The study comprehends the requirements for transforming a single wind turbine into decentralised hydrogen producing wind turbine. This configuration is designed for an offshore application, but is not restricted to an offshore location. This novel system is equipped with a power conversion system, to power the electrolyser and auxiliaries. Seawater lift with water treatment for desalination and demineralisation. And ultimately, the electrolyser with accompanying balance of system.
This study selects a PEM electrolyser with three parallel modules of 5 MW capacity each. The individual components are separately modelled and combined to complete the system into a mathematical model. Furthermore, a power management strategy is included. The power management strategy determines which, how much and how many modules are powered. The enhanced results are measured by key performance indicators, which are total annual yield, number of start-stop events and power fluctuation within a module. These KPI's are inspired by the drivers of Shell to get the lowest levelised cost of hydrogen. More hydrogen production drives down the costs and the two other KPI's ensures less degradation on the modules, therefore extending the longevity.

This report shows that the use of a power management strategy improves the annual yield, but is also capable of minimising the effect of degradation caused by start-stop events and power fluctuation. The equal power division strategy increases the annual yield by at least 2%. The total number of power fluctuations can be significantly reduced by using the segmented start strategy. Finally, a strategy is developed to enhance the longevity of the system and diminish the degradation of the modules.
Moreover, independent of the strategy, a higher annual yield is achieved by decreasing the size of the modules with the same total electrolyser capacity. However, only when it is operated on the right setpoint.
Furthermore, over-sizing of the electrolyser capacity results in under-utilisation of the electrolyser. While under-sizing the electrolysis capacity leads to an increase in performance as the system is utilised more efficiently. The results are tested and strengthened in a sensitivity study, where other wind conditions and design parameters are applied to the system.

Floating offshore wind turbines (FOWTs) unlock the potential to harness energy from wind in deeper waters. Despite their potential, the major obstacle to large-scale commercial deployment remains the floating substructure's high cost. Multidisciplinary design, analysis and optimisation techniques are commonly employed to improve their cost-competitiveness, but existing models often use simplified engineering models. This approach risks neglecting design considerations typical of structures subjected to complex aero-hydro-servo-elastic loads.

To address the need for incorporating higher-fidelity analysis methods, an optimisation framework is developed, integrating OpenFAST to simulate the response of the FOWT system under various environmental and operational conditions. Leveraging the flexibility of the Python framework and the wealth of output data contained within the OpenFAST simulation results, this holistic approach offers opportunities to explore novel and cost-effective platform designs with high reliability.

To demonstrate its effectiveness, the optimisation framework is utilised to achieve a significant reduction of the DeepCWind platform's structural mass. Among the considered constraints, the platform pitch motion was critical, reaching maxima for design load cases characterised by the most extreme wind and waves. Although the inclusion of a structural model for the platform comes at considerable computational expense, it enhances the framework's value, as structural integrity can be verified.

The recommended use of the optimisation framework is for in-depth studies, following preliminary design explorations conducted with cheaper models. Future efforts should focus on extending the structural and hydrodynamic model to improve the framework's versatility, and make it applicable to a wider range of platform concepts and turbine sizes.