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Synthetic scenario generation of monthly streamflows conditioned to the El Niño–Southern Oscillation: application to operation planning of hydrothermal systems

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Abstract

The Brazilian Interconnected Power System is hydro dominated and characterized by large reservoirs presenting multi-year regulation capability, arranged in complex cascades over several river basins. In this way, the expansion and operation planning should take into account the uncertainties about the future inflows to hydroplants reservoirs. Currently, a stochastic model for synthetic scenarios generation of monthly streamflow, based on Periodic Auto-Regressive formulation, is used to address the uncertainty. This is the official model used in the Brazilian energy operation planning by the Ministry of Mines and Energy, the National Operator of Electrical System, the Chamber of Electric Energy Commercialization and the Energy Planning Company. Recently, a great scientific effort has been made to include relevant climatic information in stochastic streamflow models. Among several important climatic phenomena in the Brazilian hydrological cycles, El Niño–Southern Oscillation has been pointed as one of the most important. Although the stochastic models that include exogenous variables or that use wavelets present good results, they have limitations for long-term horizon projections or are not suitable for applications that use stochastic dual dynamic programming, which is the case of the Brazilian electrical system. This work proposes an improvement to the current scenario generation model, in order to consider the climate information, but still being suitable to be applied in SDDP algorithms. To achieve this goal, a Markov-Switching Periodic Auto-Regressive model is presented. It is demonstrated that the methodology is able to generate synthetic scenarios which better resembles the observed streamflow, mainly during periods when the streamflow are below-average.

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Acknowledgements

The authors would like to express their gratitude towards the Brazilian Electrical Energy Research Center (CEPEL) and the Federal University of Rio de Janeiro (UFRJ) for the financial and technical support, and all the institutions that kindly provided the data used in this study. The authors are thankful and recognize that this study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES)- Finance Code 001. The authors are also grateful to the Brazilian Ministry of Mines and Energy (MME) and the Ministry of Science, Technology, Innovation and Communication (MCTIC), through the National Council for Scientific and Technological Development (CNPq) and the Financier of Studies and Projects (FINEP), and to the Fundação Carlos Chagas de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) for the means and support of the development of this research work.

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  1. Electrical Energy Research Center (Cepel), Horácio Macedo Avenue, 354 - Cidade Universitária, Rio de Janeiro, RJ, CEP: 21941-911, Brazil

    Felipe Treistman, Maria Elvira Piñeiro Maceira, Débora Dias Jardim Penna & Jorge Machado Damázio

  2. Civil Engineering Program, Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Horácio Macedo Avenue, 2020 – Cidade Universitária, Rio de Janeiro, 21941-914, RJ, Brazil

    Felipe Treistman & Otto Corrêa Rotunno Filho

  3. State University of Rio de Janeiro (UERJ), Rio de Janeiro, Brazil

    Maria Elvira Piñeiro Maceira & Jorge Machado Damázio

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  1. Felipe Treistman
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Treistman, F., Maceira, M.E.P., Penna, D.D.J. et al. Synthetic scenario generation of monthly streamflows conditioned to the El Niño–Southern Oscillation: application to operation planning of hydrothermal systems. Stoch Environ Res Risk Assess 34, 331–353 (2020). https://doi.org/10.1007/s00477-019-01763-2

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