Data-driven analysis of reliability, accessibility and survivability in marine renewable energy projects

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Date
2019
Authors
Barker, Aaron
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University College Cork
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Abstract
Increased activity in the Marine Renewable Energy industry has driven the need for an improved understanding of the wave climate and wave energy resource, which are fundamental to the development of any marine energy project. This thesis assesses the characterisation of the wave energy resource available at the Killard Point site in Co. Clare, as part of a joint industry project on the Electricity Supply Board (ESB)’s’s WestWave project, Ireland’s first proposed commercial wave energy installation. This assessment is done with an eye on the newly formed International Electrotechnical Commission standards for metocean resource assessment, with a focus on producing a standardised analysis method which informs the extractable wave energy resource. Many existing practices are questioned, and their merits assessed. This thesis adds novel tools and advanced data analysis methods, which are implemented to develop new methodologies for enhancing our understanding of our wave resource, and which subsequently enable improved assessment of the impacts of reliability, accessibility and survivability of Marine Renewable Energy projects. The impact of spectral shape on device energy production is examined using both a theoretical and practical application, to show the disconnect between currently accepted practices and the level of certainty which will be required to drive commercial success. A new methodology for the assessment of extreme wave conditions is developed, while a large contribution of this thesis is in developing and applying machine learning techniques to enhance the accuracy and dependability of wave parameter relationships and the prediction of device energy production by improving the estimation of absent wave data. This approach has been shown to result in a reduction in power production error at Killard Point from 30% to just 3.5%. This novel Machine Learning method is integral in enabling the level of characterisation that will be necessary for the commercial success of Marine Renewable Energy projects. The major contribution of this thesis is the development of an enhanced understanding of the available wave resource at the Killard Point site; producing a numerical hindcast nearshore wave model which attempts to bring the project to the level required by IEC standards, while addressing technical issues which affect the standardisation, accuracy, usability and predictability of the data gathered. This work does not focus on the Marine Renewable Energy technology in use, nor will it explore in great detail the economic vagaries of MRE projects. Instead, it focusses on developing methods which will provide a large missing piece of the puzzle in MRE development, accurate and dependable metocean analysis. The results presented here have wider applicability, and indeed much of this research has taken place, or has been verified at, other sites along the west-coast of Ireland.  
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Technoeconomics , Wave energy , Renewable energy , Data analysis
Citation
Barker, A. 2019. Data-driven analysis of reliability, accessibility and survivability in marine renewable energy projects. PhD Thesis, University College Cork.