Abstract
Increasing investment activity in offshore wind energy projects has induced the need for an improved appraisal framework of the assets. As opposed to the deterministic appraisal models currently available, a probabilistic analysis can provide decision support with assigned confidence levels, taking into account uncertainties inherent in the analysis. To this end, departing from an integrated lifecycle techno-economic model developed by the authors, the present study develops a probabilistic approach considering time-dependent and independent stochastic variables. To this end, advanced numerical methods, namely Artificial Neural Network (ANN) approximation model and an Auto-Regressive Integrated Moving Average (ARIMA) time series model are combined with Monte Carlo simulations in order to assess the impact of the system uncertainties on the performance of the asset. Joint probability distributions of the output variables, namely the NPV, capital cost, annual operating cost and LCOE are presented, providing insights regarding the profitability of the asset within defined confidence intervals.
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