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Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models

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  • Souza, Reinaldo Castro
  • Marcato, André Luı´s Marques
  • Dias, Bruno Henriques
  • Oliveira, Fernando Luiz Cyrino

Abstract

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal systems. This model is based on the Periodic Autoregressive Model, PAR (p), where the identification of the p orders is based on the significance of the Partial Autocorrelation Function (PACF) estimated via Bootstrap, an intensive computational technique. The scenarios generated through this new technique were applied to the operation planning of the Brazilian Electrical System (BES), using the previously developed methodology of Stochastic Dynamic Programming based on Convex Hull algorithm (SDP-CHull). The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders models in most cases which retains the statistical characteristics of the original series. Additionally it presents a closer total mean operation cost when compared to the cost obtained via historic series.

Suggested Citation

  • Souza, Reinaldo Castro & Marcato, André Luı´s Marques & Dias, Bruno Henriques & Oliveira, Fernando Luiz Cyrino, 2012. "Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models," European Journal of Operational Research, Elsevier, vol. 222(3), pages 606-615.
    • Handle: RePEc:eee:ejores:v:222:y:2012:i:3:p:606-615
    DOI: 10.1016/j.ejor.2012.05.020
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    Citations

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    Cited by:

    1. Anderson Mitterhofer Iung & Fernando Luiz Cyrino Oliveira & André Luís Marques Marcato, 2023. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence," Energies, MDPI, vol. 16(3), pages 1-24, January.
    2. de Queiroz, Anderson Rodrigo, 2016. "Stochastic hydro-thermal scheduling optimization: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 382-395.
    3. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    4. 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.
    5. Marcos Tadeu Barros de Oliveira & Patrícia de Sousa Oliveira Silva & Elisa Oliveira & André Luís Marques Marcato & Giovani Santiago Junqueira, 2021. "Availability Projections of Hydroelectric Power Plants through Monte Carlo Simulation," Energies, MDPI, vol. 14(24), pages 1-18, December.
    6. Lohmann, Timo & Hering, Amanda S. & Rebennack, Steffen, 2016. "Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling," European Journal of Operational Research, Elsevier, vol. 255(1), pages 243-258.
    7. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando Luiz, 2023. "An overview of non-Gaussian state-space models for wind speed data," Energy, Elsevier, vol. 266(C).
    8. Baldini, Mattia & Klinge Jacobsen, Henrik, 2016. "Optimal trade-offs between energy efficiency improvements and additional renewable energy supply: A review of international experiences," MPRA Paper 102031, University Library of Munich, Germany.
    9. Dias, Bruno Henriques & Tomim, Marcelo Aroca & Marcato, André Luís Marques & Ramos, Tales Pulinho & Brandi, Rafael Bruno S. & Junior, Ivo Chaves da Silva & Filho, João Alberto Passos, 2013. "Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems," European Journal of Operational Research, Elsevier, vol. 229(1), pages 212-222.
    10. Calili, Rodrigo F. & Souza, Reinaldo C. & Galli, Alain & Armstrong, Margaret & Marcato, André Luis M., 2014. "Estimating the cost savings and avoided CO2 emissions in Brazil by implementing energy efficient policies," Energy Policy, Elsevier, vol. 67(C), pages 4-15.
    11. Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.

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