Stochastic financial appraisal of offshore wind farms. (January 2020)

• Record Type: Journal Article
• Authors: Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal
• Is Part Of: Renewable energy. Volume 145(2020)
• Page Start: 1176
• Page End: 1191
• Publication Date: 2020-01
• Subjects: Offshore wind -- Stochastic financial appraisal -- ARIMA -- Artificial neural networks -- Monte Carlo simulation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.06.111
• Languages: English
• ISSNs: 0960-1481

Abstract

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. Highlights: A probabilistic appraisal framework of offshore wind assets is presented. The model considers time-dependent and independent stochastic variables. An artificial neural network approximation model is used to model O&M costs. The autoregressive integrated moving average model predicts future electricity prices. Methods are combined with Monte Carlo simulations to assess system uncertainties.