IDEAS home Printed from https://ideas.repec.org/p/ris/ewikln/2011_005.html
   My bibliography  Save this paper

Spatial Dependence in Wind and Optimal Wind Power Allocation: A Copula Based Analysis

Author

Listed:
  • Grothe, Oliver

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Schnieders, Julius

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

Abstract

The investment decision on the placement of wind turbines is, neglecting legal formalities, mainly driven by the aim to maximize the expected annual energy production of single turbines. The result is a concentration of wind farms at locations with high average wind speed. While this strategy may be optimal for single investors maximizing their own return on investment, the resulting overall allocation of wind turbines may be unfavorable for energy suppliers and the economy because of large fluctuations in the overall wind power output. This paper investigates to what extent optimal allocation of wind farms in Germany can reduce these fluctuations. We analyze stochastic dependencies of wind speed for a large data set of German on- and offshore weather stations and find that these dependencies turn out to be highly nonlinear but constant over time. Using copula theory we determine the value at risk of energy production for given allocation sets of wind farms and derive optimal allocation plans. We find that the optimized allocation of wind farms may substantially stabilize the overall wind energy supply on daily as well as hourly frequency.

Suggested Citation

  • Grothe, Oliver & Schnieders, Julius, 2011. "Spatial Dependence in Wind and Optimal Wind Power Allocation: A Copula Based Analysis," EWI Working Papers 2011-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    • Handle: RePEc:ris:ewikln:2011_005
    as

    Download full text from publisher

    File URL: https://www.ewi.uni-koeln.de/cms/wp-content/uploads/2015/12/EWI-WP-11-05-Spatial-Dependence-in-Wind-Power-Allocation.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthias Fischer & Christian Kock & Stephan Schluter & Florian Weigert, 2009. "An empirical analysis of multivariate copula models," Quantitative Finance, Taylor & Francis Journals, vol. 9(7), pages 839-854.
    2. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    3. Christian Genest & Jean‐François Quessy & Bruno Rémillard, 2006. "Goodness‐of‐fit Procedures for Copula Models Based on the Probability Integral Transformation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 337-366, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    2. Nguyen-Huy, Thong & Deo, Ravinesh C. & An-Vo, Duc-Anh & Mushtaq, Shahbaz & Khan, Shahjahan, 2017. "Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones," Agricultural Water Management, Elsevier, vol. 191(C), pages 153-172.
    3. Nagler Thomas & Schellhase Christian & Czado Claudia, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    4. Jean-David Fermanian, 2012. "An overview of the goodness-of-fit test problem for copulas," Papers 1211.4416, arXiv.org.
    5. Weiß, Gregor N.F. & Scheffer, Marcus, 2015. "Mixture pair-copula-constructions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 175-191.
    6. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    7. Wei Huang & Meng-Shiuh Chang, 2021. "Gold and Government Bonds as Safe-Haven Assets Against Stock Market Turbulence in China," SAGE Open, , vol. 11(1), pages 21582440219, January.
    8. Grothe, Oliver & Schnieders, Julius, 2011. "Spatial dependence in wind and optimal wind power allocation: A copula-based analysis," Energy Policy, Elsevier, vol. 39(9), pages 4742-4754, September.
    9. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    10. Siburg, Karl Friedrich & Stoimenov, Pavel & Weiß, Gregor N.F., 2015. "Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 129-140.
    11. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.
    12. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    13. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2013. "Risk prediction management and weak form market efficiency in Eurozone financial crisis," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 384-393.
    14. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
    15. Zhou, Rui & Ji, Min, 2021. "Modelling mortality dependence: An application of dynamic vine copula," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 241-255.
    16. Jeongwook Lee & Joon Jin Song & Yongku Kim & Jung In Seo, 2020. "Estimation and Prediction of Record Values Using Pivotal Quantities and Copulas," Mathematics, MDPI, vol. 8(10), pages 1-16, October.
    17. Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
    18. Kim, Daeyoung & Kim, Jong-Min & Liao, Shu-Min & Jung, Yoon-Sung, 2013. "Mixture of D-vine copulas for modeling dependence," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 1-19.
    19. Ji, Hao & Wang, Hao & Zhong, Rui & Li, Min, 2020. "China's liberalizing stock market, crude oil, and safe-haven assets: A linkage study based on a novel multivariate wavelet-vine copula approach," Economic Modelling, Elsevier, vol. 93(C), pages 187-204.
    20. Brechmann Eike Christain & Czado Claudia, 2013. "Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50," Statistics & Risk Modeling, De Gruyter, vol. 30(4), pages 307-342, December.

    More about this item

    Keywords

    Wind power; Vine copula; Optimal turbine allocation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:ewikln:2011_005. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sabine Williams (email available below). General contact details of provider: https://edirc.repec.org/data/ewikode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.