Numerous studies on the wave resource have been published since the 1970s when interest began in ocean waves as a source of energy. Most studies have focused on assessing the wave power climate rather than the yield from a specific device.
A number of atlases have been created showing the geographic distribution of wave energy on various scales. Recent examples include the European Wave Energy Resource Atlas (Pontes et al, 1997), the Atlas of UK Marine Renewable Energy Resources (DTI, 2004), the Sea Power South West Review (Metoc Plc, 2004), and the Accessible Wave Energy Resource Atlas, Ireland (ESBI, 2005). These give annual and seasonal mean values of wave power (equation 1.14) for offshore locations, based on a few years of data from numerical wind-wave models. Validation is limited for these atlases and they do not give confidence bounds on estimates of mean power. They are intended primarily for use in strategic level considerations, i.e. identifying areas suitable for wave energy development, rather than predicting the potential energy yield at a specific site. The ability to produce strategic level maps showing the estimated yield for a specific device, quickly and cost effectively for any location in the world, would aid the development of the wave energy industry.
The first objective of this research addresses this need. The choice for spatial wave data is between wave model data and satellite altimeter data. Model data is normally
purchased on a point-by-point basis and spatial mapping of the wave resource can be costly using model data. Altimeter data is well suited to spatial mapping of
oceanographic properties and its use for mapping wave climate has been demonstrated by numerous authors. Previous studies of wave climate using altimeter data have considered Hs only. To estimate the power produced by a WEC an estimate of wave period is required as well. Existing algorithms for estimating wave period from altimeter data are not suitable for estimating WEC power, since they do not correctly reproduce the joint distribution of wave height and period. This research will develop on previous studies in the following ways:
• In Chapter 3 a new algorithm is developed for estimating wave period from satellite altimeter data, which is capable of reproducing the joint distribution of wave height and period.
• In Chapter 4 it is demonstrated that the power produced by a WEC can be reliably estimated from altimeter measurements of Hs and the new algorithm for altimeter Te. The accuracy of long-term mean values of WEC power from altimeter measurements is calculated. The limitations imposed by the
variability in wave conditions and the sampling patterns of satellite altimeters are investigated.
The result is a tool which can be used to create spatial maps of not just wave conditions, but also of the estimated yield of a WEC with quantified accuracy, at any location in the world. These altimeter maps provide a cost-effective alternative to using model data for strategic level planning.
The second stage of wave energy resource assessment is to provide detailed information on the wave conditions at a chosen site. The method used to estimate the long-term resource at a particular site is similar to that used by the wind energy industry to predict the yield of a wind farm, known as Measure-Correlate-Predict (MCP). It is rare that at a site of a proposed wave energy development there will be an existing long term dataset.
In the MCP procedure short-term measurements recorded at the site of a proposed development (the predictor site) are correlated with concurrent measurements taken at a nearby reference site for which long-term data exists. This calibration is then applied to the historic data at the reference site to estimate the historic climate at the predictor site.
In anticipation that the MCP procedure could be used for site assessment in the wave energy industry, Halliday and Douglas (2008) have presented a survey of the long-term wave data available in UK waters. They note that there is relatively little in-situ data available for the most energetic locations and that it would aid wave energy
development if coverage was increased in these areas.
Due to the lack of long-term measurements as a reference dataset, some authors have proposed the use of data from numerical wind-wave models as a long-term reference (e.g. Mollison, 1994; Barstow et al, 1998; Pitt, 2006a). Mollison (1994) proposed that offshore data from ocean-scale models could be used as the boundary conditions of a smaller scale shallow-water wave model, which is used to estimate the wave conditions at the site of interest. Since wave model data are estimates rather than measurements, Mollison (1994) suggests that the model data should be calibrated against nearby buoy measurements before use. Barstow et al (1998) take a similar approach, but use satellite altimeter measurements to calibrate the offshore wave model data, before using it to drive a nearshore model. Pitt (2006a) compares estimates of wave power from model data to estimates from buoy measurements at the site of the proposed Wave Hub site in south west Britain. He finds that the mean values are generally quite close, but that sometimes the model fails to reproduce long-period swell energy measured by the buoy.
Crucially though, the issue of uncertainty of wave energy yield predictions necessary for the economic assessment of a wave energy project has not been addressed. This is in part because until recently the industry has not required such detailed calculations. With the first full scale devices being deployed and rapid expansion of the wave energy industry foreseen over the next decade, the problem of making accurate yield predictions with quantified uncertainty needs to be considered. Objective B of this research addresses this point. In Chapter 5 the calibration of wave model data is discussed and a method is proposed to calculate confidence bounds for estimates of WEC yield from calibrated model data. This is compared to the accuracy it is possible to achieve using in-situ and satellite altimeter measurements.
An assumption central to the MCP procedure is that weather patterns will not change significantly in the future, and the historic data provides a good estimate of the available resource at a site. Objective C of this research addresses the validity of this assumption.
Chapter 6 reviews studies of the long-term variability in wave climate and potential effects of anthropogenic climate change. A model is proposed to account for both historic variability in the resource and the potential effects of climate change at a site in Northern Scotland. This is used to examine the accuracy of predictions of WEC yield.
Chapter 7 discusses the estimation of extreme wave conditions at a site. A review of the methods proposed to estimate extreme values of Hs is presented, and the motivation for the Peaks Over Threshold (POT) / Generalised Pareto Distribution (GPD) model is described. Numerous methods have been proposed to estimate the parameters of this distribution, but no consensus exists on which is the most appropriate for extreme wave analysis. This research attempts to answer this question. In Chapter 7 the performance of different estimators of the generalised Pareto distribution is compared in order to determine which is most appropriate for use in extreme wave predictions (Objective D).
An assumption made in classical extreme value modelling is that the data being modelled are stationary. This assumption is not strictly true for wave data because of seasonal and climatic variability. It has been suggested by some authors that extreme value models which account for seasonal variability should be used in preference to non-seasonal models. However, it has not been conclusively demonstrated that these model are more accurate in practice. In Chapter 7 a simulation study is conducted to compare the use of seasonal and non-seasonal models in realistic situations (Objective E).