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A real-time nonlinear method for a single hydropower plant unit commitment based on analytical results of dual decomposition optimization

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  • Vieira, Douglas A.G.
  • Costa, Emerson E.
  • Campos, Pedro H.F.
  • Mendonça, Matheus O.
  • Silva, Gustavo R.L.

Abstract

This paper provides a novel computationally efficient nonlinear method for solving the unit commitment of turbines for a single hydropower plant. A closed-form solution was derived for the number of generating units and the power allocated to each one based on a dual decomposition optimization approach. The objective is to maximize the generated energy for a given water discharge, water net head, and discharge limits, considering the generating units' nonlinear efficiency curves and the prohibited and discontinuous operating zones. Numerical experiments in real-world plants are presented, and they show the effectiveness of the proposed approach, generating considerably higher energy for the same amount of water discharge. It is also compared with a Mixed Integer Linear Programming (MILP) approach, providing a speed-up of 1000 times. The formulation presented in this paper is part of a commercial software module that is currently applied to the operational planning of 18 hydropower plants, including Belo Monte's power plant, which has 24 generating units.

Suggested Citation

  • Vieira, Douglas A.G. & Costa, Emerson E. & Campos, Pedro H.F. & Mendonça, Matheus O. & Silva, Gustavo R.L., 2022. "A real-time nonlinear method for a single hydropower plant unit commitment based on analytical results of dual decomposition optimization," Renewable Energy, Elsevier, vol. 192(C), pages 513-525.
    • Handle: RePEc:eee:renene:v:192:y:2022:i:c:p:513-525
    DOI: 10.1016/j.renene.2022.04.080
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    References listed on IDEAS

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    1. Cristian Camilo Marín-Cano & Juan Esteban Sierra-Aguilar & Jesús M. López-Lezama & Álvaro Jaramillo-Duque & Juan G. Villegas, 2020. "A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem," Energies, MDPI, vol. 13(15), pages 1-19, July.
    2. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    3. Cristian Camilo Marín-Cano & Juan Esteban Sierra-Aguilar & Jesús M. López-Lezama & Álvaro Jaramillo-Duque & Walter M. Villa-Acevedo, 2019. "Implementation of User Cuts and Linear Sensitivity Factors to Improve the Computational Performance of the Security-Constrained Unit Commitment Problem," Energies, MDPI, vol. 12(7), pages 1-20, April.
    4. Niknam, Taher & Khodaei, Amin & Fallahi, Farhad, 2009. "A new decomposition approach for the thermal unit commitment problem," Applied Energy, Elsevier, vol. 86(9), pages 1667-1674, September.
    5. Liping Li & Pan Liu & David Rheinheimer & Chao Deng & Yanlai Zhou, 2014. "Identifying Explicit Formulation of Operating Rules for Multi-Reservoir Systems Using Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1545-1565, April.
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    Cited by:

    1. He, Mengjiao & Han, Shuo & Chen, Diyi & Zhao, Ziwen & Jurasz, Jakub & Mahmud, Md Apel & Liu, Pan & Deng, Mingjiang, 2024. "Optimizing cascade Hydropower-VRE hybrid systems: A novel approach addressing whole-process vibration to enhance operational safety," Energy, Elsevier, vol. 304(C).

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