Using Copulas for Modeling Dependence in Wind Power

Ayse Metin Karakaş

Abstract


Wind power is clean and renewable source of energy in all countries and circles. Moreover, wind power is one of the world’s largest and most accessible sources of renewable energy. In this paper, marginal distributions were fitted to each of the variables and to examine the relationship between wind speed of Elazig, Bitlis and Van with COPULA method. The results show that there is a weak dependence between wind speed of Elazig, Bitlis and Van.


Keywords


Wind Speed; COPULA Method; Marginal Modeling.

Full Text:

PDF

References


Grothe, O., & Schnieders, J. 2011. Spatial dependence in wind and optimal wind power allocation: A copula- based analysis. Energy policy, 39(9):4742-4754.

Hagspiel, S., Papaemannouil, A., Schmid, M., & Andersson, G. 2012. Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid. Applied energy, 96: 33-44.

Díaz, G., Gómez-Aleixandre, J., & Coto, J. 2014. Statistical characterization of aggregated wind power from small clusters of generators. International Journal of Electrical Power & Energy Systems, 62: 273-283.

Haghi, H. V., Bina, M. T., Golkar, M. A., & Moghaddas-Tafreshi, S. M. 2010. Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran. Renewable Energy, 35(9): 1991-2000.

Bilgen, S., Keleş, S., Kaygusuz, A., Sarı, A., & Kaygusuz, K. 2008. Global warming and renewable energy sources for sustainable development: a case study in Turkey. Renewable and sustainable energy reviews, 12(2): 372-396.

Kaplan, Y. A. 2015. Overview of wind energy in the world and assessment of current wind energy policies in Turkey. Renewable and Sustainable Energy Reviews, 43: 562-568.

Ilkılıç, C., Aydın, H., & Behçet, R. (2011). The current status of wind energy in Turkey and in the world. Energy policy, 39(2): 961-967.

Sklar. 1959. Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris, 229-231.

Genest, C., MacKay, J. 1986. The joy of copulas: Bivariate distributions with uniform marginals. The American Statistician, 40(4): 280-283.

Genest, C., & Rivest, L. P. 1993. Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88(423): 1034-1043.

Capéraà, P., Fougères, A. L., Genest, C. 1997. A nonparametric estimation procedure for bivariate extreme value copulas. Biometrika, 84(3): 567-577.

Nelsen, R. B. 1997. Dependence and order in families of Archimedean copulas. Journal of Multivariate Analysis, 60(1): 111-122.

R. B. Nelsen, (2006) An Introduction to Copulas, 2nd ed., Springer, New York.

Genest, C., Favre, A. C. 2007. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of hydrologic engineering, 12(4): 347-368.

Genest, C., Nešlehová, J. 2007. A primer on copulas for count data. ASTIN Bulletin: The Journal of the IAA, 37(2): 475-515.

Nelsen, R. B., Quesada-Molina, J. J., Rodríguez-Lallena, J. A., Úbeda-Flores, M. 2008. On the construction of copulas and quasi-copulas with given diagonal sections. Insurance: Mathematics and Economics, 42(2): 473-483.

Genest, C., Rémillard, B., Beaudoin, D. 2009. Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and economics, 44(2): 199-213.

Bessa, R. J., Miranda, V., Botterud, A., Zhou, Z., & Wang, J. 2012. Time-adaptive quantile-copula for wind power probabilistic forecasting. Renewable Energy, 40(1): 29-39.

Bouyé, E., Salmon, M. 2013. Dynamic copula quantile regressions and tail area dynamic dependence in Forex markets. In Copulae and Multivariate Probability Distributions in Finance (pp. 125-154). Routledge.

Lu, Q., Hu, W., Min, Y., Yuan, F., & Gao, Z. 2014. Wind power uncertainty modeling considering spatial dependence based on pair-copula theory. In PES General Meeting| Conference & Exposition, 2014 IEEE (pp. 1-5). IEEE.

Zhang, N., Kang, C., Xia, Q., & Liang, J. 2014. Modeling conditional forecast error for wind power in generation scheduling. IEEE Transactions on Power Systems, 29(3):1316-1324.




DOI: https://doi.org/10.24203/ajet.v7i1.5673

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Asian Journal of Engineering and Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.