Konferensartikel

Wind energy has been the fastest growing and most promising renewable energy source in terms of profitability in recent years. However; one major drawback of wind energy is the variability in production due to the stochastic nature of wind. The article presents statistical simulation methods to incorporate risks from stochastic wind speeds into profitability calculations. We apply the Measure-Correlate-Predict Method (MCP) within the variance ratio method to generate long-term wind velocity estimates for a potential wind energy site in Austria. The bootstrapping method is applied to generate wind velocities for the economic life-time of a wind turbine. The internal rate of return is used as profitability indicator. We use the Conditional Value at Risk approach (CVaR) to derive probability levels for a certain internal rate of returns; as the CVaR is a reliable risk measure even if return distributions are not normal. In contrast to other scientific publications; our methodology can be generally applied; because we do not rely on estimated distributions for wind speed predictions; but on measured wind speed distributions; which are usually readily available. In addition; the CVaR has not been applied as a measure of risk for wind site evaluation before and it does not rely on any specific function regarding the profitability distribution.

Wind power; Bootstrapping; Measure-Correlate-Predict Method; Conditional Value at Risk; Internal Rate of Return