How to determine the Portfolio Effect based on wind regime dependency: European examples

1. How to determine the Portfolio Effect based on wind regime dependency: European examples José Manuel Marco Circe Triviño Guillermo Gil EWEC 09 Marseille, 18 March 2009

2. Introduction The wind industry has seen large players owning portfolios of wind farms spread across regions and countries Financing portfolios of wind farms allows uncertainties associated with wind variability to be mitigated  Benefit from reductions in energy prediction uncertainty levels We show a statistical and meteorological perspective with due consideration to the correlation of the wind regimes at the wind farm sites This is a tool to determine the degree of dependency between wind regimes in order to analytically evaluate the “portfolio effect

3. Introduction What do we mean with “Portfolio Effect”? Geographic spread Lower overall wind speed variability Extreme wind conditions are balanced in average Financing a portfolio of wind farms provides reduced risks due to reduced wind variability uncertainty

4. Introduction With statistic techniques we find a statistical relation (dependency) between those wind regimes (Pearson coefficient) Wind sites spread around Production data or wind data as a proxy We are able to add the wind variability uncertainties as partially dependent (portfolio effect) This allows mitigation of the wind variability uncertainties and the overall portfolio uncertainty associated to the energy production estimation

5. Definition of Uncertainty • For the Wind Analysis: That associated with the prediction of the long-term annual average energy production, typically expressed as a standard deviation, σ. • The estimation of the energy production defines the mean, and the uncertainty in the estimate, σ. A Gaussian distribution is the industry standard assumption.

6. 12% 10% 8% Uncertainty [% of net energy] 6% 4% 2% 0% Anemometer Wind shear Correlation to Period Frequency Wind flow Future 1-yr reference representative distribution modeling variability of long-term Uncertainty source Uncertainty Analysis - Sources Wind variability uncertainties Technical uncertainties

7. Uncertainty Analysis – Wind Variability Our analysis focuses in the reduction of the uncertainty associated to the wind variability uncertainty sources of combined projects

8. Statistical basis - Parametric techniques (1) When using Parametric Inferential Statistics , the correlation coefficient “r” is given together with a confidence interval, which contains the value of the population parameter (with a concrete significance level) and, at the same time, this interval expresses how representative the sample is

9. Statistical basis - Parametric techniques (2) • (1)Both the population and the sample must fit to a normal distribution. Kolmogorov-Smirnov normality test • (2)Independency of the samples Ljung-Box test of autocorrelation

10. Portfolio Effect – Pearson matrix and confidence intervals The correlation coefficient “r” is given together with a confidence interval, which contains the value of the population parameter, with a significance level of 95%. The level of dependence between project uncertainty elements is described by the Pearson or correlation coefficient, r Pearson Matrix

11. Portfolio Effect – Uncertainty Matrix A portfolio of m wind farms with its future wind uncertainties Uncertainty Matrix It is necessary to combine (add) the uncertainties with the correlation structure defined by the Pearson Matrix:

12. Portfolio Effect – Variance-Covariance matrix Variance-Covariance Matrix The sum of all the elements in the above matrix is the portfolio variance, and its square root, the final uncertainty associated to the portfolio

13. Our case – European portfolio Our portfolio consists of 75 wind farms distributed across Portugal, Spain, France and Germany Our Pearson Matrix consists of 75x75 = 5625 Pearson coefficients Since it is a symmetric matrix, we have analyzed 2775 coefficients and its associated confidence interval, with a significance level of 95%, in order to evaluate the statistical significance of Pearson and adjust it when necessary

14. Pearson Matrix Pearson Coefficient Interval (0.56 – 0.90) 95 % confidence Number of pairs of the correlation

15. Statistical+Meteorological Analysis Pearson = 0.29 Pearson = 0 Independency Pearson = 0.49

16. Results of the Analysis- 1-year scenario Energy prediction of the portfolio 3793 GWh/year Uncertainty without Portfolio Effect 651 GWh/year Uncertainty considering Portfolio Effect 488 GWh/year Two different Gaussian distributions depending on the Uncertainty = σ

17. Results of the Analysis – 1-year scenario P75 = 3463 Lower uncertainties imply higher energy estimations for the same exceedance levels P75, P90 P75 = 3354 P90 = 3167 P90 = 2958 P50 = 3793

18. Summary and conclusions (1) The analysis is carried out using real production data or monthly wind speed data as proxy, which have been used to state a level of dependency/independency between sites summarised by a Pearson coefficient matrix. The “portfolio effect” of the wind speed variability uncertainty of a portfolio made up of 75 wind farms in different geographical areas has been assessed in this presentation. Combining this matrix with the individual uncertainties, it is possible to determine the “portfolio effect” associated with wind speed variability uncertainties.

19. Summary and conclusions (2)

20. Summary and conclusions (3) The geographic and climatological dispersion intensifies the independency between wind regimes and therefore increases the observed “portfolio effect” The scope of this study is to show the importance of considering and quantifying this effect when analysing portfolios rather than considering the wind farms as isolated entities This feature is very important for investors and owners to mitigate wind risks by acquiring or developing a geographically distributed wind farm portfolio. Thanks for your attention Jose.Marco@garradhassan.com Circe.Trivino@garradhassan.com www.garradhassan.com