The developments in offshore wind offer new opportunities for the powering offshore oilfields in general, and offshore water injection processes in specific. On the other hand, building on the strength of the oil industry could enable a faster development of offshore floating wind turbine technologies (for use in deep and ultra-deep water oilfields), access to capital, political connections, global reach, and state of the art technical capabilities. Both the use of water injection to enhance oil recovery in suitable reservoirs and use of offshore wind technology to harness power are proven to be commercially and technically viable, each on its own. However, the integration of both systems has not been adequately investigated. This thesis investigates the potential of autonomous stand-alone wind-powered intermittent (fully wind powered, Scenario A) and cyclic water injection (wind and gas powered, Scenario B) schemes in offshore oilfields, specifically in heterogeneous layered oil reservoirs. The results obtained from analytical evaluations and numerical simulations of a 3D synthetic model demonstrates strong oil recovery performance, economic, and environmental feasibility, under modelled reservoir and economic conditions. Improved oil recovery is achieved by improved sweep of low permeable layers and previously poor swept areas. It is evident that reservoir performance favors the more intensive schemes (higher ratio off-injection period per cycle to the on-injection period per cycle and longer off-injection duration) with higher injection rates. Furthermore, a sensitivity analysis for offshore oil field characteristics (distance separating injectors and host platform, reservoir heterogeneity, reservoir symmetry, vertical transmissibility, rock wettability, reservoir pressure, capillary pressure, and intermittent injection initiation time) is conducted. Wind-powered intermittent and cyclic injection schemes are economically feasible mainly in heterogeneous layered reservoirs. Offshore sites with superb wind power provides the highest internal rate of return (IRR) for the fully-wind powered scenario. Offshore Locations with relatively lower wind resource (down to good level of wind power potential) and favourable wind variability patterns can still achieve a higher net present value (NPV), yet at a lower IRR. As larger and more costly wind power systems is required to enhance the oil recovery. The economic and environmental benefits of wind-powered injection schemes is attributed to a higher energy efficiency (in terms of the number of crude oil barrels recovered per MWh), as well as a significant reduction in greenhouse gas emissions, fuel costs, and power transmission costs. Both fully and partially wind-powered schemes are considered more economically favourable under higher oil price environment, lower weighted average cost of capital, longer distances separating host platform and injection wells, higher carbon tax and more stringent environmental conditions.

This master’s thesis project related inflow conditions to atypical fatigue loading of wind turbines. The research was fully based on experimental data obtained in another project. First an expected fatigue loading was defined based on the mean wind speed and the turbulence intensity. Differences between expected and measured fatigue loading were determined and analyzed using regression and clustering. The most important results regard the out-of-plane bending moment measured at the blade root and the side-to-side bending moment measured at the tower bottom. In addition to the mean wind speed and the turbulence intensity, the out-of-plane bending moment is found to correlate with wind shear. The side-to-side bending moment, on the other hand, correlates with wind veer and with different wind directions. In this case, the clustering result performs better in estimating the difference between expected and measured fatigue loading than the linear regression model.

The offshore wind energy market is highly dynamic and one of the major challenges is to become more cost competitive against other sources of energy. The support structure of an offshore wind turbine shows a high potential for further cost reduction. Jules Dock is investigating in the replacement of a steel tower on a monopile foundation by a Glass Fiber Reinforced Plastic (GFRP) tower, which has a higher specific strength and better corrosion resistance compared to steel. This would enable lower transportation and installation costs and a potentially longer lifetime. Currently Jules Dock assesses the feasibility of a GFRP tower versus a steel tower using an aero-elastic simulation tool. However, such a tool is computationally demanding and requires detailed turbine data, which are often not available in a preliminary design phase. For this reason, a research project has been set-up to develop a parametric design model which can effectively be used without detailed turbine data and aero-elastic simulations, to identify the design driving constraints for preliminary designs and mass optimization purposes. The parametric design model analyses the natural frequency constraint and Ultimate Limit State (ULS). The natural frequency of the support structure is intended to be in the soft-soft region, which will result in the lowest mass possible as has been found in previous research. The natural frequency of the support structure is analyzed with a finite beam element model, in which the Rotor Nacelle Assembly (RNA) is modelled as a point mass. The interaction with soil is also included using a three spring model. For the ULS, the yield and buckling capacity of the support structure are analyzed. For the steel monopile and transition piece, dedicated design standards have been used. For the composite tower, however, these design standards are not existing and therefore analytical solutions from composite tube applications have been used and verified for this purpose. FEM analysis showed that the yield capacity can be determined exactly. The buckling capacity approximation in the parametric design model showed conservative results compared to a dedicated buckling analysis tool. In the parametric design model the maximum loading for the ULS constraints has been analyzed using a quasi-static load analysis method which includes turbulence and dynamic wave loading effects. This method has been compared with the time-domain simulation tool Phatas, which is integrated in Focus6. This comparison showed that also the tower top bending moment should have been included. If this is taken into account, the integrated load analysis method appears to approach the maximum loading for a very stiff tower quite accurately compared with the time-domain simulations. For flexible towers, however, the loads are less accurately approached, since dynamic interactions between wind and wave loading have appeared to be relevant, especially for load cases with a wave excitation frequency close to the natural frequency of the support structure. In the quasi-static load analysis method these dynamic effects are only taken into account for the submerged part of the support structure, but the time-domain simulations have shown that these effects will increase the maximum loading on the support structure above water level as well. For mass optimization and identification of the design driving constraints of this support structure, a genetic algorithm has been developed. Several optimization runs have been made, resulting in multiple feasible solutions with only small differences in support structure mass. While the buckling constraint drives the design of the monopile, transition piece and top of the tower, the lower part of the tower is design driven by the yield constraint. It has been concluded that the loading analysis method in the developed parametric design model should include the tower top bending moment, since this leads to a mass increase of up to 20% of the support structure mass. Next to that, it has been observed that the optimization algorithm tends to converge to solutions with a natural frequency to be as low as possible, such that the dynamic amplification of the wave loading is minimized. For these solutions, the loading analysis method in the parametric design model may have underestimated the maximum loading on the flexible support structure. This needs to be further investigated.

Wind turbine placement in a wind farm can be optimized to limit power losses due to wakes and improve the economic value of the plant. Seeing as wind farms are increasing in number and size, fast methods to generate good quality layouts can be beneficial for designers. Wind Farm Layout Optimization (WFLO) consists in finding a layout that maximizes the Annual Energy Production (AEP) of a wind farm. The procedure is driven by an algorithm that generates possible layouts and by a framework that evaluates their AEP. If the traditional method to assess AEP is adopted, layout optimization is computationally demanding due to the impact of each turbine's position on the productivity of the surrounding ones. Indeed, for any change in the layout, this inter-dependency forces designers to calculate wake effects and the wind resource-average energy production of the wind farm. This thesis proposes an approach to reduce the computational load of WFLO by pre-computing the power losses. Indeed, the approach avoids recalculating the expected power loss among turbines during the optimization procedure. This optimization strategy employs a novel approach, called Pre-Averaged Model (PAM), that expresses the expected power loss of a wake source at representative points around it. Firstly, the wind farm is discretized, and fictitious turbines are placed at each given spot. Secondly, PAM calculates the expected power loss caused by each fictitious turbine for the surrounding ones. Discontinuities introduced by wind resource discretization and top-hat wake deficit profiles affect PAM's accuracy substantially. Binning wind measurements in 72 wind directions solves the problem for typical engineering wake models. Then, a greedy algorithm uses the power losses of the fictitious turbines to build layouts constructively by adding an extra turbine per iteration. The effect of multiple wake sources on a wake target is modelled by linear superimposition of the pre-computed power losses. PAM is tested in combination with three greedy algorithms, namely, Basic Greedy (BG), Add-Remove-Move Greedy (ADREMOG), and ADREMOG II. This research demonstrates that the PAM and the superposition of the power losses can be reliably used for WFLO. Also, the joint use of PAM and greedy algorithms achieve an interesting trade-off between speed and quality of the layouts. Indeed, PAM is beneficial as it speeds up greedy algorithms. Furthermore, greedy algorithms allow generating better layouts at the cost of slowing down the algorithm. The balance between speed and quality can be regulated by using a finer discretization, testing different locations for the first turbine placement, or acting on the nature of the algorithm. In particular, the use of a re-location stage at each constructive iteration increases the quality of the layouts substantially but reduces the speed of execution. As a result, the proposed algorithms present different characteristics: the BG is the fastest but its median layout is the worst; ADREMOG produces the best layouts in the longest time; ADREMOG II is a compromise between the two former algorithms.

Our present-day society is utterly dependant on electricity. This dependence will only grow as electrification of sectors such as manufacturing, transportation and building heating takes off. Most of the electricity these days comes from conventional plants running on coal and natural gas. Despite that these are reliable and cheap, the disadvantage of emitting greenhouse gases is no longer acceptable. Sustainable alternatives such as solar PV and wind have the highest potential. However, the replacement of conventional power plants by sustainable alternatives is subject to understanding the intermittent and unpredictable behaviour of both wind and solar PV and thereby ensuring that generation equals demand at all times. Energy storage technologies allow for separation between generation and supply to the grid. The aforementioned makes the large-scale integration of wind and solar PV more difficult. This study lays the foundation for an interconnected system model where solar PV, wind, and battery storage is combined. This study is therefore deliberately different than existing studies focussing on small, already severely constrained systems, such as island systems. The problems experienced and possible solutions are first identified by conducting a literature review and simultaneity analysis of wind and solar PV power. It turns out that it is possible while being self-reliant, using all the potential renewable energy and shifting the generation by arbitrage on the APX market to design a system with an increasing rate of income. The simulations showed that when the combined generation (of both wind and solar PV) during peak availability is higher than the grid connection capacity, the computed battery size [MWh] increases rapidly, causing a swift decrease in rate of return. This research also shows that with current imbalance settlement prices and battery installation cost minimizing imbalance is less viable than arbitrage on the APX market. The presented results are consistent with how the electricity system currently operates. At last, the results indicate that curtailing ’cheap’ solar PV energy with significant overplanting on the existing limiting grid connection is beneficial. In this research some important steps have been taken towards the design for a grid-connected optimal system. The method proposed should be tested with more wind and solar PV generation data. Further research should consider longer periods with real generation data making the results presented more accurate.

The design of an offshore wind farm (OWF) is multidisciplinary in nature as it involves the design of many disciplines such as the wake effects, support structure, electrical cables etc. For the optimal design of an OWF, an optimization procedure is required where all the disciplines are optimized simultaneously. The objective function plays a significant role in optimization as it expresses the main aim of the model which is to be either minimized or maximized. So far, cost of energy (COE) and annual energy production (AEP) are one of the commonly used objective functions for OWF optimization as far as the author is aware. However, there might be other objective functions that may influence the optimal design of an OWF as well. This may include maximizing the profit, minimizing the environmental impact, reducing their carbon emissions etc. Hence, this thesis investigates the overview of different objective functions and understand its impact on the optimal design of an OWF.

An inventory of different objective functions is prepared, and relevant ones are selected for further study. It is observed that even though some objectives are dissimilar, they still depend on the same wind farm parameters and are therefore expected to give similar design results. From the list of objective functions, net present value (NPV) and risk management objectives are chosen for further research.

The selected objective functions are then formulated in a metric for optimization. The price of electricity plays a significant role in determining the NPV. It is learnt that electricity price varies with the power supply depending on the site conditions. The electricity price is low if the supply of power is high in a region where there are many OWF’s and vice versa. Moreover, OWF investors value constant power output without any fluctuations. Hence, taking all these aspects into consideration, the electricity price in the NPV function is modelled for a constant value, wind variability and wind power predictability.
The risk management function, on the other hand, aims at minimizing the uncertainty associated with an OWF project. The risk here refers to the uncertainty associated with the profit obtained from the OWF. A set of annual average wind speeds is computed using monte carlo simulations and the AEP and NPV are estimated. The mean( NPV_mean) and standard deviation ( NPV_std ) of NPV are then calculated. NPV_std represents the uncertainty in this scenario and is minimized to reduce the risk.

A suitable method is then identified to deal with multiple objectives. The NPV function is maximized for maximum profit and this objective is evaluated using a single objective optimization technique. The risk management objective involves the calculation of NPV_mean and NPV_std. Both objectives are contrasting in nature as a significant reduction in NPV_std corresponds to an undesirable reduction in NPV_mean. A tradeoff between both these objectives is the best possible solution. Therefore, a multi- objective optimization technique is used, and a list of solutions is obtained by generating a pareto front.

The new approach is then evaluated by implementing different case studies. It is observed that optimum rotor diameter and number of turbines for the single objective optimization technique are influenced by economic indicators such as the real interest rate and lifetime. However, they are not influenced by variation in the electricity price. Nevertheless, the NPV function is sensitive to the economic indicators and variation in the electricity price.

For the multi - objective optimization technique, multi criteria analysis was used to determine the weight to the objective functions while moving along the pareto curve. It was observed that the improvement of one objective led to the deterioration of the other objective. Hence, the pareto front provides opportunities to investors to negotiate and decide on the weight they want to specify for their objectives.

Multi-use of offshore platforms is a long-term research challenge which aims to combine food and energy production at sea. Research in this field is in its infancy, literature is scarce and without much cross-referencing. The aim of this thesis has been to develop a coherent set of recommendations for the further development of multi-use platforms, specifically for offshore floating wind energy and salmon aquaculture. A design-oriented approach has been adopted to generate insights regarding multi-use plat- forms. New multi-use concepts were generated and presented, illustrating what a future multi-use farm could look like. Simultaneously, a rigorous decision-making framework was set-up aimed at identifying superior concepts, while systematically producing insights by making explicit the trade-offs which govern the decision. A most preferred concept has been selected and evaluated in more detail, focussing on the identification of engineering challenges and cost-reduction opportunities through comparison of the multi-use concept with stand-alone references. Except for one item, no major technical barriers for the further development of multi-use platforms were identified, given that the challenges associated to the stand-alone activities can be met. This item is the expectation that mooring loads will largely increase by integrating fixed-netting structures in wind energy platforms. A small set of cost-reduction opportunities was discovered, as well as threats to the economic viability. Evaluation of the cost-reduction potential nuanced the view that large opportunities exist in mooring design and shared O&M vessels and demonstrated that the multi-use concept can also lead to additional costs. Most importantly, further study into the availability of optimal sites for multi-use is recommended, because it can be expected that sites optimal for wind energy may be sub-optimal for the production of Atlantic Salmon.

Traditionally a wind turbine’s power curve is used to model the long-term energy yield of the wind turbine and afterwards assess the performance of the turbine (power curve verification). But the current power curve is typically univariate: only dependent on the wind speed and slightly adjusted for site density, turbulence intensity and wind shear. This method limits the amount of atmospheric conditions taken into account, which (could) affect turbine performance. Therefore the Power Curve Working Group introduced
the concept of an inner power curve (for common conditions) and an outer power curve (for all other conditions). While this is an improvement, it would be desirable to have not 1, not 2, but a multitude of power curves for different conditions.

This multitude of power curves, for a range of atmospheric conditions, is the goal of this thesis. Using a non-parametric machine learning method, a neural network, the feasibility of modeling a wind turbine’s power curve from historical data is studied. This resulted in a model which provides a refinement on the warranted power curve (provided by the manufacturer) for certain conditions of 7 atmospheric parameters. These parameters are wind speed, turbulence intensity, wind shear, wind veer, temperature, pressure and humidity, but could in theory be extended to more parameters.

This model resulted in three sub-models, which: 1) assess individual parameter impact/sensitivity on turbine performance, 2) provide for a company tool to continue power curve verification after a few years and 3) provide a general model to refine long-term energy yield estimations for future wind farms. Despite
the somewhat limited model and currently available datasets to train the models from, both predictive models show improved accuracy compared to the traditional power curve. Besides validation with theory, the impact assessment model allowed for investigation of the impact of certain atmospheric conditions on
the performance of certain wind turbines.

Surrogate models are used to approximate the expensive ‘true’ simulation codes and thus have the potential to speed up the wind farm layout optimisation problem (WFLOP). One technique to make surrogate models is Polynomial Chaos Expansion (PCE). PCE can approximate a (wind farm) model by using orthogonal polynomials which are constructed based on input variables. In case of a wind farm model, these are wind speed and wind direction. The technique sounds promising, but up till now, PCE has mainly been used as an uncertainty quantification method and not as much in order to help with optimisation problems. This thesis research project aims to implement the PCE method in WFLOP by implementing a multivariate polynomial basis based on the wind speed and wind direction. The usability of the method will be determined based on a comparison between the WFLOP results of the PCE surrogate model and the conventional approach.

Flows over complex terrains present uncertainties in wind farm power production and lifespan. Core to these uncertainties are wake dynamics, which lack adequate models and need further validation. In lieu of conducting field experiments, this study integrates scanning lidar measurements and large eddy simulation (LES) to characterize wakes in complex terrain. LES data of flow over complex terrain near Perdigao, Portugal has been generated prior to this study. In the current study a `synthetic’ lidar is placed inside the LES to sample the wind field in space and time as a commercial scanning lidar would. From the synthetic scan fields, the wake is located and characterized using metrics of wake center location and maximum velocity deficit. These metrics are evaluated against the same metrics as determined by LES fields. By treating the LES metrics as the ``target”, the relative accuracy of the metrics can be determined. This accuracy is a function of the scan geometry, allowing for the geometry to be optimized for wake characterization. Since a lidar scan represents neither an instantaneous field nor a mean field, it is not clear whether the target should be the LES instantaneous or mean fields, therefore the metric accuracy is assessed using different LES fields in the form of instantaneous snapshots of the wake, fields averaged over the time of a scan cycle, and long-term averaged fields.

A set of scan geometries was tested which vary in measurement point density, scan direction, and scan area to find that accuracy depends on a trade-off in the temporal and spatial resolution of the geometry. The results highlight not only the dependency of accuracy on the geometry parameters, but also on which type of LES field is used as the target. The long-term averaged errors were found to be unsuitable for lidars in this terrain, where the longest time scales exceed the simulation time, therefore accuracy was assessed using two types of fields, instantaneous fields taken at the start of the scan cycle, and the cycle-averaged fields. From these results two geometries were identified as performing the best.

Improvements are still needed in the wake detection algorithm, both in distinguishing points in the wake from terrain-generated turbulence and the inclusion of more wake characteristics such as size, shape, and orientation in order to develop more accurate scan geometries, hence improving predictions of wind farm production and longevity. Other scan geometry parameters also need to be explored, including scan paths and non-uniform measurement point densities.

Nowadays an increasing number of offshore wind farms (OWF) is getting built. Several disciplines are considered into this subject. The manual and sequential design approach used so far is not sufficient to guarantee optimized systems because interactions between the different components are disregarded. Therefore, engineering companies are looking for a multidisciplinary, modular, user-friendly and easy tool to be applied during the preliminary design assessment. This should take into consideration almost every crucial aspect of OWF design. Moreover, it should be able to perform some overall layout optimization, i.e. providing the best turbine positioning w.r.t. the energy yield, the costs, the cables, the installation and the O&M, ensuring some good constraint-handling techniques and guaranteeing a sufficient degree of robustness, precision, flexibility and speed. Therefore, the concept of MDAO (Multisciplinary Design Analysis and Optimization) workflow has been introduced. With respect to the common sequential design procedure, which optimizes the wind farm components separately, the MDAO analysis helps the user deal with the interactions between different design areas whilst automating the design process. The result is a design where all the parts are jointly optimized.Within the MDAO domain, two components are identified. The former is called analysis block and it comprehends all the modular tools which refer to a specific physical/economic discipline; the latter is the driver, i.e. an optimizing procedure which calls the analysis block in each iteration.This thesis focuses mostly on the analysis of the driver. In particular, the first goal is to identify the best optimizing strategy to be adopted, thanks to several quantitative assessment criteria. The second research goal is studying to what extent the different design areas - aerodynamics, support structure, cable topology etc. - affect the result of the optimization. The third research objective is to see whether the chosen optimizing procedures are able to deal with more complex design situations, such as more constraints or longer design vectors. In the end, an additional analysis on the effect of neighbouring wind farms on the layout optimization has been done, in order to see whether the optimization should consider the effect of those wind farms (disturbed wind conditions) or whether the results from the undisturbed case can be still valuable if re-computed in disturbed conditions.