Wave energy is expected to play an important role in the forthcoming years for the de-carbonisation of Scottish and British electricity production. This study underlines the importance of resource assessment and attempts to improve the quantifiable wave power resource, with use of a validated numerical model. While levels of wave flux are high for an area that may not always constitute the best option for wave energy applications. In this study, a long-term hindcast for the Scottish coastlines run from 2004 to 2014 (11 years) improving the existing wave maps and resource estimations. Spatial and physical considerations of a third generation spectral model allow examination at locations of immediate interest for the ocean energy community. Utilising numerical wave models of finer resolution allows for the detailed coupling of potential wave energy converters (WECs) and site characterization. Such detail energy results allow for improved financial analysis that take into account the severity of local resource and its energy potential.

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Wind is the dominant process for wave generation. Detailed evaluation of metocean conditions strengthens our understanding of issues concerning potential offshore applications. However, the scarcity of buoys and high cost of monitoring systems pose a barrier to properly defining offshore conditions. Through use of numerical wave models, metocean conditions can be hindcasted and forecasted providing reliable characterisations. This study reports the sensitivity of wind inputs on a numerical wave model for the Scottish region. Two re-analysis wind datasets with different spatio-temporal characteristics are used, the ERA-Interim Re-Analysis and the CFSR-NCEP Re-Analysis dataset. Different wind products alter results, affecting the accuracy obtained. The scope of this study is to assess different available wind databases and provide information concerning the most appropriate wind dataset for the specific region, based on temporal, spatial and geographic terms for wave modelling and offshore applications. Both wind input datasets delivered results from the numerical wave model with good correlation. Wave results by the 1-h dataset have higher peaks and lower biases, in expense of a high scatter index. On the other hand, the 6-h dataset has lower scatter but higher biases. The study shows how wind dataset affects the numerical wave modelling performance, and that depending on location and study needs, different wind inputs should be considered.@en

Many islands have high electricity prices due to the reliance on imported diesel. However, hybrid energy systems (HES) which combine renewable generation with backup generators and energy storage are becoming cost competitive. Diesel usually provides about 10% of the demand because most renewables are non-dispatchable and thus the complete decarbonisation requires massively oversized renewable generation and storage. By including renewables with different resource profiles and demand side management (DSM), the diesel consumption could be decreased without increasing storage and renewable generation capacities. Here a framework for the design and optimisation of HES using wind, wave and solar generation and DSM is introduced. For the Mediterranean it is shown that wave energy is not competitive but that DSM reduces the emissions and costs by 21 and 8%. In the North Sea, DSM has lower benefits because waves act as an energy store for the wind. Thus, the combination of wave energy converters (WECs) and wind turbines significantly reduces the need for backup generation and energy storage which leads to large reductions in costs (up to 40%) and emissions (up to 60%). DSM and WECs can both simultaneously reduce the cost and emissions of HES but need to be designed for the particular circumstances.

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Wave energy sites around Scotland, are considered one of the most energetic waters, as they are exposed to the Atlantic Ocean. The amount of energy reaching the shoreline provides an opportunity for wave energy deployments. Currently, considerations on wave devices expect them to be installed at nearshore locations. That means that the potential wave resource has to be investigated, since deep to shallow water interactions alter the shape of propagated waves. Resource assessment for these regions is essential in order to estimate the available and extractable energy resource. Although several numerical models exist for wave modelling, not all are suitable for nearshore applications. For the present work, the nearshore wave model SWAN has been used to simulate waves for the Hebridean region. The set-up, calibration and validation of the model are discussed. The resulting wave conditions are compared with buoy measurements. Results indicate that the modelling technique performed well.@en