Modeling the dependency relationship between wind speed and wind power generation: An application of copula theory

Authors

  • Tuany Esthefany Barcellos de Carvalho Silva Author
  • Reinaldo Castro Souza Author
  • Fernando Luiz Cyrino Oliveira Author
  • Marco Aurélio Sanfins Author

DOI:

https://doi.org/10.24084/repqj20.395

Keywords:

Copula, Simulation, Wind power generation

Abstract

The concern with global warming and pollution has increased interest in the development of renewable energy sources, which are less aggressive to the environment. Wind energy can provide adequate solutions to the above-mentioned problems. The use of this energy eliminates unwanted waste that is harmful to health and the environment from other energy sources such as coal and nuclear power plants. This work aims to analyse the dependence relationship between wind speed and wind energy production, a rather complex relationship, so this study seeks to understand the stochastic nature of both phenomena. As a methodological tool the copula theory was used. A copula function is used as a general method, which consists of formulating multivariate distributions so that different dependency structures can be represented. That is, the study is based on the analysis and modelling of the dependence between wind speed data and electrical energy generation, for an hourly database of a wind farm in the state of Bahia, collected in the entire year of 2017. Thus, this paper proposes a study aiming the search for the copula function referring to the data in the period mentioned.

Author Biographies

  • Tuany Esthefany Barcellos de Carvalho Silva

     Industrial Engineering Department
     PUC-RJ, Pontifical Catholic University of Rio de Janeiro
     Gávea- Rio de Janeiro (Brazil)

  • Reinaldo Castro Souza

    Industrial Engineering Department
     PUC-RJ, Pontifical Catholic University of Rio de Janeiro
     Gávea- Rio de Janeiro (Brazil)

  • Fernando Luiz Cyrino Oliveira

    Industrial Engineering Department
     PUC-RJ, Pontifical Catholic University of Rio de Janeiro
     Gávea- Rio de Janeiro (Brazil)

  • Marco Aurélio Sanfins

    Department of Statistics
     UFF, Federal Fluminense University
     São Domingos – Niterói – Rio de Janeiro (Brazil)

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Published

2024-01-03

Issue

Section

Articles