Operational risk of a wind farm energy production by Extreme Value Theory and Copulas

TL;DR

This paper assesses operational risks in wind farm energy production using Extreme Value Theory and Copulas, highlighting the importance of accurate tail estimation, turbine reliability modeling, and turbine interaction effects.

Contribution

It introduces a novel approach combining Generalized Pareto distribution, compound Poisson process, and Copulas to better estimate wind farm operational risks.

Findings

We show Weibull underestimates rare wind speed events.

The proposed model improves risk estimation accuracy.

Correlation among turbines significantly affects risk assessment.

Abstract

In this paper we use risk management techniques to evaluate the potential effects of those operational risks that affect the energy production of a wind farm. We concentrate our attention on three major risk factors: wind speed uncertainty, wind turbine reliability and interactions of wind turbines due mainly to their placement. 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. The wind turbines reliability is considered by modeling the failures events as a compound Poisson process. Finally, the use of Copula able 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 those obtained by ignoring the aforementioned risk factors.