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.

Two post processing methods for quantifying the shadow effect of the offshore wind farm Princes Amalia (PA) onto Egmond aan Zee (OWEZ) wind farm are analyzed and benchmarked. The first method is the author’s proposed shadow effect determination method (SEDM), which quantifies an offshore wind farm’s shadow effect based on mesoscale WRF (Weather Research Forecast) idealized modeling and the observed frequency of the analyzed site’s wind conditions. The Fitch turbine parametrization scheme and Mellor-Yamada-Nakanishi-Niino (MYNN) surface layer and planetary boundary layer (PBL) schemes were used to simulate the wind farm’s interactions, based on site conditions. The proposed physical downscaling method (SEDM) uses filtered simulated atmospheric stability and wind speed conditions, in order to calculate the percent wind speed deficit downstream of PA, with regard, first, to observed wind speed frequency and, secondly, to wind speed and corresponding atmospheric stability regimes. Then a statistical downscaling method, based on the established Analog Ensemble (AnEn) technique, developed by Luca Delle Monache et al. (2011) was employed to verify the results from the first method. This method runs a post processing algorithm using the weighted average of the observations that were verified when the 15 best analogs were valid. Observed wind speed data at 10 m and 50 m height was used as Numerical Weather Prediction (NWP) input data and fit to observed time series data. From this, wind speeds at 116 m were extrapolated, in order to estimate the reconstructed atmospheric stability. The two methods were benchmarked and shadow effects were quantified in the range of 7.53% - 22.92% for the SEDM and within an 80% confidence interval of 0.23% -1.83% for the statistical downscaling method. Given the physical method’s exceedance of this confidence interval, WRF idealized modeling proves itself as a consistent means of quantifying an offshore wind farm’s wake, as demonstrated by comparable studies, however inaccurate when benchmarked to statistical modelling methods that use observed wind speed data to recreate atmospheric conditions. / Wake Research Group

<div><br></div><div>Extreme heat is one of the deadliest health hazards that is projected to increase in intensity and persistence in the near future. Temperatures are further exacerbated in the urban areas due to the Urban Heat Island (UHI) effect resulting in increased heat-related mortality and morbidity. However, the spatial distribution of urban temperatures is highly heterogeneous. As a result, metrics such as UHI Intensity that quantify the difference between the average urban and non-urban air temperatures, often fail to characterize this spatial and temporal heterogeneity. My objective in this thesis is to understand and characterize the spatio-temporal dynamics of UHI for cities across the world. This has several applications, such as targeted heat mitigation, energy load estimation, and neighborhood-level vulnerability estimation.</div><div><br></div><div>Towards this end, I have developed a novel multi-scale framework of identifying emerging heat clusters at various percentile-based thermal thresholds T_thr and refer to them collectively as <i>intra-Urban Heat Islets</i>. Using the Land Surface Temperatures from Landsat for 78 cities representative of the global diversity, I have showed that the heat islets have a fractal spatial structure. They display properties analogous to that of a percolating system as T_thr varies. At the percolation threshold, the size distribution of these islets in all cities follows a power-law, with a scaling exponent = 1.88 and an aggregated Area-Perimeter Fractal Dimension =1.33. This commonality indicates that despite the diversity in urban form and function across the world, the urban temperature patterns are different realizations with the same aggregated statistical properties. In addition, analogous to the UHI Intensity, the mean islet intensity, i.e., the difference between mean islet temperature and thermal threshold, is estimated for each islet, and their distribution follows an exponential curve. This allows for a single metric (exponential rate parameter) to serve as a comprehensive measure of thermal heterogeneity and improve upon the traditional UHI Intensity as a bulk metric.</div><div><br></div><div><br></div><div>To study the impact of urban form on the heat islet characteristics, I have introduced a novel lacunarity-based metric, which quantifies the degree of compactness of the heat islets. I have shown that while the UHIs have similar fractal structure at their respective percolation threshold, differences across cities emerge when we shift the focus to the hottest islets (T_thr = 90^th percentile). Analysis of heat islets' size distribution demonstrates the emergence of two classes where the dense cities maintain a power law, whereas the sprawling cities show an exponential deviation at higher thresholds. This indicates a significantly reduced probability of encountering large heat islets for sprawling cities. In contrast, analysis of heat islet intensity distributions indicates that while a sprawling configuration is favorable for reducing the mean Surface UHI Intensity of a city, for the same mean, it also results in higher local thermal extremes. </div><div><br></div><div>Lastly, I have examined the impact of external forcings such as heatwaves (HW) on the heat islet characteristics. As a case study, the European heatwave of 2018 is simulated using the Weather Research Forecast model with a focus on Paris. My results indicate that the UHI Intensity under this HW reduces during night time by 1^oC on average. A surface energy budget analysis reveals that this is due to drier and hotter rural background temperatures during the HW period.</div><div>To analyze the response of heat islets at every spatial scale, power spectral density analysis is done. The results show that large contiguous heat islets (city-scale) persist throughout the day during a HW, whereas the smaller islets (neighborhood-scale) display a diurnal variability that is the same as non-HW conditions. </div><div><br></div><div>In conclusion, I have presented a new viewpoint of the UHI as an archipelago of intra-urban heat islets. Along the way, I have introduced several properties that enable a seamless comparison of thermal heterogeneity across diverse cities as well as under diverse climatic conditions. This thesis is a step towards a comprehensive characterization of heat from the spatial scales of an urban block to a megalopolis.</div><div><br></div>