| Summary: | High investment risk is a key barrier to the commercialisation of the nascent tidal energy sector. An increase in investor confidence can unlock funding for early arrays, the lessons from which can provide further de-risking, leading to further investment. This thesis focussed on increasing investor confidence by improving the uncertainty analysis methods used to quantify the overall uncertainty in key investment decision metrics; energy yield, levelised cost of energy (LCOE) and internal rate of return (IRR). A Monte Carlo Analysis (MCA) framework for tidal energy annual yield uncertainty analysis was developed and compared to the currently recommended ISO-GUM method. It was shown that key assumptions implicit in ISO-GUM are inaccurate for most realistic projects. Crucially, the resultant error provides an overly optimistic view of a project's P90 energy yield. By modelling a range of realistic projects, it was shown that the ISO-GUM P90 yield overestimate exceeds 2% for a maximum resource uncertainty between 4% and 11%, depending on the project, with increasing uncertainty leading to larger errors. It is difficult to judge accurately where within that range a given case crosses the 2% error threshold, as it is a complex function of numerous project specific variables. This undermines confidence in ISO-GUM results, even in cases where the method may be acceptable, because it is not possible to deduce the validity for a particular project a priori. MCA does not make the same assumptions and provides consistently accurate results. A modification to the standard ISO-GUM process was also proposed as a simpler alternative to MCA, with an improvement in results compared to the standard method, but the residual error would still remain unquantified. A generic cost modelling tool for probabilistic discounted cashflow analysis using MCA was also developed. The tool accepts user specified uncertainty distributions in a multitude of flexibly defined input variables defining a project's CapEx, OpEx, yield and finances to ...
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