Wind speed prediction for wind farm applications by Extreme Value Theory and Copulas

In this paper we use risk management techniques to evaluate the effects of some risk factors that affect the energy production of a wind farm. We focus our attention on three major risks: wind speed variability, wind turbine failures and correlations between produced energy. As a first contribution, we show that the Weibull distribution, commonly used to fit recorded wind speed data, underestimates rare events. Therefore, in order to achieve a better estimation of the tail of the wind speed distribution, we advance a Generalized Pareto distribution. We considered one aspect of the wind turbines reliability by modeling their failure events as a compound Poisson process. Finally, the use of Copula enables us to consider the correlation between wind turbines that compose the wind farm. Once this procedure is set up, we show a sensitivity analysis and we also compare the results from the proposed procedure with a simplistic energy prediction using the Weibull distribution.