Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this tool can aid in the development of future regulations by considering various constraints on the placement of wind turbines within the site and exploring how these impact the levelized cost of energy.

This paper sets out to analyze the observed wake effect at Block Island Wind Farm. A comparison is made between several wake simulation methods and the observed data at Block Island using analytic and CFD (Computational Fluid Dynamics) modelling methods.  The observed wake results at Block Island show a similar trend evident in earlier papers- a large power deficit found between the first two Wind Turbine Generators (WTGs) in the row followed by a slight variation in power along the row for the remainder of the WTGs. A noticeable difference is seen between the last two WTGs in the row where an increase in power is found. This increase in power is thought to be due to the alignment of the wind farm. Nevertheless, when the observed data is compared with the modeled results, the observed data seem to underestimate the wake effect due to misalignment issue with the nacelle wind direction measurement. A sensitivity analysis is conducted on the Wake Decay Constant (WDC) and Turbulence Intensity (TI) values. The results show a maximum power variation of ≈30% between a WDC value of 0.07 and 0.04 and ≈18% for TI values between 8% and 14%. The findings show that a value in the higher range of the examined WDC (0.06 and 0.07) and TI (12% and 14%) values represent a better comparison to the observed data. Nevertheless, it is not recommended to alter these parameters to fit the observed data. Furthermore, due to high uncertainty in the data measurements, and hence observed results, a clear conclusion indicating which wake model best represents the wake effect at Block Island cannot be stated.

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

Co-location of offshore wind turbines at sites being developed for tidal stream arrays has been proposed as a method to increase capacity and potentially reduce the cost of electricity compared to operating either technology independently. This research evaluates the cost of energy based on capital expenditure and energy yield. It is found that, within the space required around a single 3 MW wind turbine, co-location provides a 10-16% cost saving compared to operating the same size tidal-only array without a wind turbine. Furthermore, for the same cost of electricity, a co-located farm could generate 20% more yield than a tidal-only array. These results are based on analysis of a case-study site in the Pentland Firth. Wind energy is assessed using an eddy viscosity wake model in OpenWind, with a 3 MW rated power curve and thrust coefficient from a Vestas V90 turbine. Three years of wind resource data is from the UK Met Office UK Variable (UKV) 1.5 km numerical model and corrected against a 400 m Weather Research and Forecasting (WRF) model run over the site. Tidal stream energy is modelled using a semi-empirical superposition of self-similar plane wakes, with a generic 1 MW rated power curve and thrust based on a full-scale, fixed-pitch turbine. Coincident tidal resource data is from the Forecasting Ocean Assimilation Model (FOAM) at 7.5 km resolution and correlated with a 150 m ADvanced CIRCulation model (ADCIRC). Wave parameters are corrected from ERA-Interim data with six months of wave buoy data. Multiple tidal turbine array layouts are considered, with maximum tidal energy generated for a staggered array with spacing of 20 tidal turbine diameters, Dt , streamwise and 1.5Dt cross-stream. However, cheapest cost of electricity from the tidal-only array, was found for a single row of turbines, due to minimal wake effects. Laboratory experiments were undertaken to validate the superposition wake model for use with large, shared support structures. Two rotors mounted either side of a central tower generate a peak wake velocity deficit 70% greater than predicted by superposition. This was due to high local blockage and a complex near-wake structure, with a corresponding increase in tower drag of 9%. Further experiments evaluated the impact of oblique inflow on turbines yawed at +/-15 degrees. These results validated a theoretical cosine correction for thrust coefficient and characterised the centreline wake drift with downstream distance. Extreme environmental loads for a shared support structure, compared to structures for wind-only and tidal-only, have also been modelled. A non-linear wave model was used to represent a single wave form with 1% occurrence for each hour of time-series data. Overturning moment about the base of a shared support, with one wind and two tidal turbines, was found to be 4.5% larger than for a wind-only turbine in strong current and with turbines in different operational states. Peak loads across the tidal array were found to vary by 2.5% and so little load reduction benefit could be gained by locating a shared support in a more sheltered area of the array.