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The ambition to achieve an environmentally sustainable, cost-effective and secure future energy supply has motivated greater use of renewable resources for electricity generation. The majority of renewable capacity to date has been served by wind turbine technology. However, current Irish energy policy has expressed a desire to diversify renewable energy portfolios by increasing the share of electricity generated by alternate renewable technologies. Ireland has a considerable wave energy resource and, as such, the deployment of Wave Energy Conversion (WEC) devices has been cited as contributing to such diversification. An appropriate economic evaluation of this decision must consider each of the costs and benefits that comprise the economic trade-off associated with deployment. Given the pre-commercial nature of these devices, many of these parameters are unknown or uncertain. This thesis develops three modelling frameworks to improve the understanding of uncertain or unknown parameters determining cost-effective WEC deployment, thus improving the fidelity of WEC policy appraisal in Ireland. The need for this contribution is motivated through discussions of the policy, market and economic context for WEC deployment. The first modelling framework presented comprises a probabilistic method of device cost estimation. This allows a degree of likelihood to be placed on a cost or profitability estimate, improving the interpretation of values for the appraisal of investment and policy support. The second modelling framework presented builds on this by developing a real options modelling approach to analyse the deployment trade-off relative to dynamic fuel and carbon prices. This model identifies the time period and economic conditions for cost-effective deployment. The final section of this thesis analyses the spatial and micro-level distribution of socio-economic impact as a result of WEC deployment using Spatial Microsimulation. In providing this contribution, a Spatial Microsimulation framework is created, known as SMILE. A novel statistical matching algorithm known as Quota Sampling is developed thus providing a methodological contribution to the literature of Spatial Microsimulation. This thesis concludes by outlining future applications and extensions of these modelling frameworks.