IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v7y2025i1p7-d1580616.html
   My bibliography  Save this article

Evaluating the Potential of Copulas for Modeling Correlated Scenarios for Hydro, Wind, and Solar Energy

Author

Listed:
  • Anderson M. Iung

    (Industrial Engineering Department, PUC-Rio-Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225 Gávea, Rio de Janeiro 22451-900, RJ, Brazil)

  • Fernando L. Cyrino Oliveira

    (Industrial Engineering Department, PUC-Rio-Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225 Gávea, Rio de Janeiro 22451-900, RJ, Brazil)

  • Andre L. M. Marcato

    (Department of Electrical Energy, UFJF-Federal University of Juiz de Fora, Rua José Lourenço Kelmer, s/n-São Pedro, Juiz de Fora 36036-900, MG, Brazil)

  • Guilherme A. A. Pereira

    (Department of Economics, UFES-Federal University of Espírito Santo, Av. Fernando Ferrari 514, Vitória 29075-910, ES, Brazil)

Abstract

The increasing global adoption of variable renewable energy (VRE) sources has transformed the use of forecasting, scenario planning, and other techniques for managing their inherent generation uncertainty and interdependencies. What were once desirable enhancements are now fundamental requirements. This is more prominent in Brazil, given the large hydro capacity that has been installed. Given the need to understand the interdependencies within variable renewable energy systems, copula-based techniques are receiving increasing consideration. The objective is to explore and model the correlation and complementarity, based on the copula approach, evaluating the potential of this methodology considering a case test composed of hydro, wind, and solar assets. The proposed framework simulated joint scenarios for monthly natural energy (streamflows transformed into energy), wind speed and solar radiation, applied to a small case test, considering historical data from the Brazilian energy system. The results demonstrate that simulated scenarios are validated by their ability to replicate key statistical attributes of the historical record, as well as the interplay and complementarity among hydrology, wind speed, and solar radiation.

Suggested Citation

  • Anderson M. Iung & Fernando L. Cyrino Oliveira & Andre L. M. Marcato & Guilherme A. A. Pereira, 2025. "Evaluating the Potential of Copulas for Modeling Correlated Scenarios for Hydro, Wind, and Solar Energy," Forecasting, MDPI, vol. 7(1), pages 1-21, January.
  • Handle: RePEc:gam:jforec:v:7:y:2025:i:1:p:7-:d:1580616
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/7/1/7/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-9394/7/1/7/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Denault, Michel & Dupuis, Debbie & Couture-Cardinal, Sébastien, 2009. "Complementarity of hydro and wind power: Improving the risk profile of energy inflows," Energy Policy, Elsevier, vol. 37(12), pages 5376-5384, December.
    3. Guilherme Armando Almeida Pereira & Álvaro Veiga, 2019. "Periodic Copula Autoregressive Model Designed to Multivariate Streamflow Time Series Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3417-3431, August.
    4. Ávila R., Leandro & Mine, Miriam R.M. & Kaviski, Eloy & Detzel, Daniel H.M. & Fill, Heinz D. & Bessa, Marcelo R. & Pereira, Guilherme A.A., 2020. "Complementarity modeling of monthly streamflow and wind speed regimes based on a copula-entropy approach: A Brazilian case study," Applied Energy, Elsevier, vol. 259(C).
    5. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    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. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    2. Hofert, Marius & Prasad, Avinash & Zhu, Mu, 2022. "Multivariate time-series modeling with generative neural networks," Econometrics and Statistics, Elsevier, vol. 23(C), pages 147-164.
    3. Shogo Kato & Toshinao Yoshiba & Shinto Eguchi, 2022. "Copula-based measures of asymmetry between the lower and upper tail probabilities," Statistical Papers, Springer, vol. 63(6), pages 1907-1929, December.
    4. Alexander J. McNeil, 2021. "Modelling Volatile Time Series with V-Transforms and Copulas," Risks, MDPI, vol. 9(1), pages 1-26, January.
    5. Giuseppe Orlando & Michele Bufalo, 2021. "Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions," Risks, MDPI, vol. 9(5), pages 1-35, May.
    6. Hua, Lei & Joe, Harry, 2017. "Multivariate dependence modeling based on comonotonic factors," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 317-333.
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.
    9. Kuntadi, Cris, 2022. "Effective energy commodity risk management on Indonesia," Resources Policy, Elsevier, vol. 78(C).
    10. Martin Bladt & Alexander J. McNeil, 2020. "Time series copula models using d-vines and v-transforms," Papers 2006.11088, arXiv.org, revised Jul 2021.
    11. Ackerer Damien & Vatter Thibault, 2017. "Dependent defaults and losses with factor copula models," Dependence Modeling, De Gruyter, vol. 5(1), pages 375-399, December.
    12. Li, Xiaoting & Joe, Harry, 2024. "Multivariate directional tail-weighted dependence measures," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
    13. Xiangqian Sun & Xing Yan & Qi Wu, 2020. "Generative Learning of Heterogeneous Tail Dependence," Papers 2011.13132, arXiv.org, revised Nov 2023.
    14. Huawei Li & Guohe Huang & Yongping Li & Jie Sun & Pangpang Gao, 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    15. Ávila R., Leandro & Mine, Miriam R.M. & Kaviski, Eloy & Detzel, Daniel H.M. & Fill, Heinz D. & Bessa, Marcelo R. & Pereira, Guilherme A.A., 2020. "Complementarity modeling of monthly streamflow and wind speed regimes based on a copula-entropy approach: A Brazilian case study," Applied Energy, Elsevier, vol. 259(C).
    16. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    17. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Time reversal and last passage time of diffusions with applications to credit risk management," Finance and Stochastics, Springer, vol. 24(3), pages 795-825, July.
    18. Pfeifer Dietmar & Mändle Andreas & Ragulina Olena, 2017. "New copulas based on general partitions-of-unity and their applications to risk management (part II)," Dependence Modeling, De Gruyter, vol. 5(1), pages 246-255, October.
    19. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    20. Sleire, Anders D. & Støve, Bård & Otneim, Håkon & Berentsen, Geir Drage & Tjøstheim, Dag & Haugen, Sverre Hauso, 2022. "Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations," Finance Research Letters, Elsevier, vol. 46(PB).

    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:gam:jforec:v:7:y:2025:i:1:p:7-:d:1580616. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.