Wave energy resource modelling and energy pattern identification using a spectral wave model
The benefits of the Oceans and Seas have been exploited by societies for many centuries; the marine offshore and naval sectors have been the predominant users of the waters. It has been overlooked until recently, that significant amounts of energy can be harnessed by waves, providing an additional abundant resource for renewable energy generation. The increasing energy needs of current societies have led to the consideration of waves as an exploitable renewable resource. During the past decades, advancements have been made towards commercialising wave energy converters (WECs), though significant knowledge gap exists on the accurate estimation of the potential energy that can be harnessed. In order, to enhance our understanding of opportunities within wave energy highly resolved long-term resource assessment of potential sites are necessary, which will allow for not only a detailed energy estimation methodology but also information on extreme waves that are expected to affect the survivability and reliability of future wave energy converters. This research work aims to contribute the necessary knowledge to the estimation of wave energy resources from both highly energetic and milder sea environment, exhibiting the opportunities that lay within these environments. A numerical model SWAN (Simulating WAves Nearshore), based on spectral wave formulation has been utilised for wave hindcasting which was driven by high resolution temporal and spatially varying wind data. The capabilities of the model, allow a detailed representation of several coastal areas, which are not usually accurately resolved by larger ocean models. The outcome of this research provides long-term data and characterisation of the wave environment and its extremes for the Scottish region. Moreover, investigation on the applicability of wave energy in the Mediterranean Sea, an area which was often overlooked, showed that wave energy is more versatile than expected. The outcomes provide robust estimations of extreme wave values for coastal waters, alongside valuable information about the usage of numerical modelling and WECs to establish energy pattern production. Several key tuning factors and inputs such as boundary wind conditions and computational domain parameters are tested. This was done in a systematic way in order to establish a customized solution and detect parameters that may hinder the process and lead to erroneous results. The uncertainty of power production by WECs is reduced by the introduction of utilization rates based on the long-term data, which include annual and seasonal variability. This will assist to minimize assumptions for energy estimates and financial returns in business plans. Finally, the importance of continuous improvements in resource assessment is stressed in order to enhance our understanding of the wave environment.
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