Evaluation of bivariate Archimedean and elliptical copulas to model wind power dependency structures

Wind Energy

Volumn 17, Issue 2, 2014, Pages 225-240

  Louie, Henry ^a  

^a SEATTLE UNIVERSITY   (United States)

Author keywords

concordance; copula; correlation; dependence; modeling; Monte Carlo; wind power

Indexed keywords

CORRELATION METHODS; DISTRIBUTION FUNCTIONS; FUNCTION EVALUATION; MODELS; MONTE CARLO METHODS; WIND TURBINES;

APPLICATION GUIDELINES; CONCORDANCE; COPULA; DEPENDENCE; DEPENDENCY STRUCTURES; GAUSSIAN DEPENDENCES; JOINT DISTRIBUTION FUNCTIONS; NATIONAL RENEWABLE ENERGY LABORATORY;

WIND POWER;

DATA SET; DEPENDENCY; METHODOLOGY; MODELING; MONTE CARLO ANALYSIS; SIMULATION; WIND FARM; WIND POWER; WIND TURBINE;

EID: 84893025546     PISSN: 10954244     EISSN: 10991824     Source Type: Journal    
DOI: 10.1002/we.1571     Document Type: Article

References (27)

1. Nelsen R,. An Introduction to Copulas, (2nd edn). Springer: New York, NY, 2006.
2. Hasche B,. General statistics of geographically dispersed wind power. Wind Energy 2010; 13: 773-784. DOI: 10.1002/we.397.
3. Wan Y,. Wind power plant behaviors: Analyses of long-term wind power data. Technical Report NREL/TP-500-36551, NREL, 2004.
4. Ernst B, Wan Y, Kirby B,. Short-term power fluctuation of wind turbines: Analyzing data from the German 250-MW measurement program from the ancillary services viewpoint. Technical Report NREL/CP-500-26722, NREL, 1999.
5. Wan Y, Milligan M, Parsons B,. Output power correlation between adjacent wind power plants. Journal of Solar Energy Engineering 2003; 125: 551-555. DOI: 10.1115/1.1626127.
6. Holttinen H,. Hourly wind power variations in the Nordic countries. Wind Energy 2005; 8: 173-195. DOI: 10.1002/we.144.
7. Osborn D, Hendersen M, Nickell B, Lasher W, Liebold C, Adams J, Caspary J,. Driving forces behind wind. Power & Energy Magazine 2011; 9: 60-74. DOI: 10.1109/MPE.2011.942474.
8. Kroese D, Taimre T, Botev Z,. Handbook of Monte Carlo Methods, (2nd edn). John Wiley & Sons: Hoboken, NJ, 2011.
9. Papaefthymiou G, Kurowicka D,. Using copulas for modeling stochastic dependence in power system uncertainty analysis. IEEE Transactions on Power Systems 2009; 24: 40-49, DOI: 10.1109/PES.2009.5275265.
10. Louie H,. Evaluating Archimedean copula models of wind speed for wind power modeling, PowerAfrica, Johannesburg, South Africa, 2012; 1-8.
11. Hagspiel S, Papaemannouil A, Schmid M, Andersson G,. Copula-based modeling of stochastic wind power in Europe and implications to the Swiss power grid, August 2012. DOI: 10.1016/j.apenergy.2011.10.039.
12. Pinson P, Madsen H, Nielsen H, Papaefthymiou G, Klöckl B,. From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy 2009; 12: 51-62, DOI: 10.1002/we.
13. Pinson P, Girard R,. Evaluating the quality of scenarios of short-term wind power generation. Applied Energy 2012; 96: 12-20, DOI: 10.1016/j.apenergy. 2011.11.004.
14. Gill S, Stephen B, Galloway S,. Wind turbine condition assessment through power curve copula modeling. IEEE Transactions on Sustainable Energy 2012; 3: 94-101, DOI: 10.1109/TSTE.2011.2167164.
15. Bessa R, Mendes J, Miranda V, Botterud A, Wang J, Zhou Z,. Quantile-copula density forecast for wind power uncertainty modeling, PowerTech, Trondheim, Norway, 2011; 1-8.
16. NREL. Wind integration datasets, August 2010. [Online]. Available: http://www.nrel.gov/wind/integrationdatasets [Accessed on August 2011].
17. Brower M,. Development of eastern regional wind resource and wind plant output datasets. Technical Report NREL/SR-550-46764, NREL, Golden, CO, December 2009.
18. Joe H,. Multivariate Models and Dependence Concepts. Chapman and Hall/CRC: London, UK, 1997.
19. Genest C, Rivest L,. Statistical inference procedures for bivariate Archimedean copulas. Journal of American Statistical Association 1993; 88: 1034-1043.
20. Genz A,. Numerical computation of rectangular bivariate and trivariate Normal and t probabilities. Statistics and Computing 2004; 3: 251-260.
21. Dytham C,. Choosing and Using Statistics: A Biologist's Guide, (4th edn). Wiley-Blackwell: Chichester, UK, 2011.
22. Genest C, Rémillard B, Beaudoin D,. Goodness-of-fit tests for copulas: A review and power study. Insurance: Mathematics and Economics 2009; 44: 199-213.
23. Berg D,. Copula goodness-of-fit testing: an overview and power comparison. European Journal of Finance 2009; 7-8: 675-701.
24. National Institute of Standards and Technology. Chi squared goodness of fit test, April 2003. [Online]. Available: http://www.itl.nist.gov/div898/ software/dataplot/refman1/auxillar/chsqgood.htm [Accessed on October 2011].
25. Cormen T, Leiserson C, Rivest R, Stein C,. Introduction to Algorithms, (3rd edn). MIT Press: Cambridge, MA, 2001.
26. Louie H,. Evaluation of probabilistic models of wind plant power output characteristics, Probabilistic Methods Applied to Power Systems, Singapore, 2010; 442-447.
27. Stephen B, Galloway S, Hill DC, McMillan D,. A copula model of wind turbine performance. IEEE Transactions on Power Systems 2011; 26: 965-966. DOI: 10.1109/TPWRS.2010.2073550.