The influence of wind gustiness on estimating the wave power resource
Introduction
Increased awareness of the need to combat climate change through reducing carbon emissions has driven government investment in the development of low carbon technologies [1]. The energetic environment of our shelf seas provides us with an opportunity to exploit the tidal and wave energy resource, and it has been estimated that the global wave power resource is around [2]. However, progress is slower for wave technology than it is for tidal stream technology, due to the stochastic nature of the wave resource. Tidal currents are primarily driven by astronomical forces which allow them to be predicted with accuracy over long time periods [3], [4]. In contrast, the wave energy resource is difficult to quantify beyond seasonal trends. Waves are largely driven by the prevailing wind field. The fluctuations within this wind field impact the wave resource – this change is particularly evident in the seasonality of the wave resource, which is much more energetic during winter months in contrast to the summer months [5]. Upfront investment is required to support developments in marine renewable technologies. The ability to accurately quantify the resource, and to understand the environment of proposed wave energy extraction sites, is important for developers and investors. Increased awareness of developments in the marine renewable energy industry is reflected in the scientific literature [6]. In particular, oceanographic modelling has an important role in advancing the development and progress of the marine renewable industry, since such models can be used to quantify the resource over various timescales, and so aid site selection. Models are validated using in situ and experimental data, and model outputs help inform our understanding of the natural environment. However, when comparing these model outputs to observed data, it is often found that wave models have difficulty accurately predicting extreme wave height values, and that wave models often underestimate larger wave events [7], [8], [9]. Being able to reduce wave model uncertainty is important so that we can have more confidence in wave model results – and so have more confidence in simulated wave energy resource assessments. A resource popular with developers is the Atlas of UK Marine Renewable Energy Resources [10]. However, this product only provides developers with mean wave power, and does not include detailed spatial and temporal variability beyond seasonal timescales; Neill and Hashemi [5] show that it is important to consider inter-annual variability when assessing the wave energy resource over long time periods, for example when considering long term trends in climatic indices such as the North Atlantic Oscillation. Wave growth occurs when the wind speed exceeds the phase speed of the waves [11], [12]. Traditionally, wave models are forced with 3-hourly synoptic wind fields; subsequently, high frequency wind gusts and their impact on the wave climate, are not being captured. The aim of our idealised study is to determine the importance of high frequency wind forcing on the simulated wave energy resource. The results of this study are also relevant to model simulations of wave-induced sediment transport, and for quantifying mean and extreme wave impacts on coastal structures.
Section snippets
The study region
The model region selected for this study is the North Sea, a semi-enclosed basin with an area of 575,000 km2 located between the United Kingdom, the European continent and the Scandinavian peninsula. Neill and Hashemi [5] demonstrated that, although the wave energy resource of the North Sea is relatively modest, there is low inter-annual and intra-seasonal variability in the resource compared to other regions of the northwest European shelf seas; the North Sea could therefor be a reliable wave
Methods
There were several stages in this study. A high frequency wind time series was obtained from the FINO-1 research platform in the North Sea and was analysed to determine the distribution of wind variability with respect to the mean. As a result of this analysis, an idealised wind field was generated for a 14 h storm event. This wind field was used to force the wave model SWAN in order to understand the importance of increasing wind variability and the impact that this variability has on simulated
Real wind data
From our analysis of the FINO-1 wind data, it is possible to see that by only inputting 3-hourly wind data into wave models, much of the high frequency variability is not being resolved (Fig. 1). In particular, there is more wind variability not being captured during winter months, in contrast to summer months (Fig. 3). This is important to consider, since it is during winter months that the UK experiences a peak in the wave power resource [24]. Our analysis of wind data showed it to be
Discussion
The aim of this study was to investigate how altering the wind input influences modelled wave parameters. As you would expect, changing the wind time step narrowed the spread of potential values for significant wave height. Subsequently, by using higher frequency wind data, more confidence can be placed in wave power estimations. Using sub-hourly wind data, we found a maximum difference in the mean wave power of 23%. Similarly, using wind fields with a wind time step of less than 3 h shows a
Conclusion
By forcing wave models with high frequency wind, more accurate representations of the wave environment should be created, which will aid predictions of the wave power resource. In future, this can be applied to resolving the stress of extreme wave scenarios on marine current turbines and correctly determining the impact of marine current turbines, on for instance, features such as offshore sandbanks [26]. Much more research needs to go in to making both tide and wave models as accurate as
Acknowledgments
Thanks to the British Oceanographic Data Centre (BODC) for supplying the GEBCO bathymetry data, and to the BMU (Bundesministerium fuer Umwelt, Federal Ministry for the Environment, Nature Conservation and Nuclear Safety) and the PTJ (Projekttraeger Juelich, project executing organisation) for the FINO-1 wind data. Additionally the authors are grateful to Olaf Outzen and Friederike Kinder for their help with gaining access to appropriate wind data. The model simulations were made possible by
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