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Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches

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  • Fredo, Guilherme Luiz Minetto
  • Finardi, Erlon Cristian
  • de Matos, Vitor Luiz

Abstract

The long-term generation scheduling (LTGS) problem aims at finding a generation policy that minimizes an objective function over a multi-year planning horizon. A crucial aspect of this problem is the Hydropower Production Function (HPF), which relates power with head, turbined outflow, and efficiency of the generating units. Given that the LTGS is a large-scale stochastic optimization problem, the HPF is modeled in a simplified manner. However, considering the high-performance computers currently available and the recent advances in stochastic optimization algorithms, it is possible to enhance the HPF modeling to use the energy resources more efficiently. This paper proposes a piecewise linear model of HPF that considers the plant generation as a function of the volume and the total outflow. Unlike previous works, the HPF also considers the (nonlinear) efficiency function of each generating unit. The paper also presents a comparison between the proposed HPF and a one-dimensional HPF known as constant productivity. The generation policy and the computational burden are analyzed using an optimization-simulation process based on Stochastic Dual Dynamic Programming algorithm. The computational tests use data of a large-scale electrical power system, which corresponds to about 90% of the Brazilian system.

Suggested Citation

  • Fredo, Guilherme Luiz Minetto & Finardi, Erlon Cristian & de Matos, Vitor Luiz, 2019. "Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches," Renewable Energy, Elsevier, vol. 131(C), pages 45-54.
    • Handle: RePEc:eee:renene:v:131:y:2019:i:c:p:45-54
    DOI: 10.1016/j.renene.2018.07.026
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. Cerisola, Santiago & Latorre, Jesus M. & Ramos, Andres, 2012. "Stochastic dual dynamic programming applied to nonconvex hydrothermal models," European Journal of Operational Research, Elsevier, vol. 218(3), pages 687-697.
    3. Philpott, A.B. & de Matos, V.L., 2012. "Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion," European Journal of Operational Research, Elsevier, vol. 218(2), pages 470-483.
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    Citations

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    Cited by:

    1. Zheng, Hao & Feng, Suzhen & Chen, Cheng & Wang, Jinwen, 2022. "A new three-triangle based method to linearly concave hydropower output in long-term reservoir operation," Energy, Elsevier, vol. 250(C).
    2. David Lucas dos Santos Abreu & Erlon Cristian Finardi, 2022. "Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems," Energies, MDPI, vol. 15(5), pages 1-23, February.
    3. Ramon Abritta & Frederico Panoeiro & Leonardo Honório & Ivo Silva Junior & André Marcato & Anapaula Guimarães, 2020. "Hydroelectric Operation Optimization and Unexpected Spillage Indications," Energies, MDPI, vol. 13(20), pages 1-20, October.
    4. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    5. Shuo Huang & Xinyu Wu & Yiyang Wu & Zheng Zhang, 2023. "Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness," Energies, MDPI, vol. 16(17), pages 1-13, September.
    6. Haugen, Mari & Blaisdell-Pijuan, Paris L. & Botterud, Audun & Levin, Todd & Zhou, Zhi & Belsnes, Michael & Korpås, Magnus & Somani, Abhishek, 2024. "Power market models for the clean energy transition: State of the art and future research needs," Applied Energy, Elsevier, vol. 357(C).
    7. Pedro H. M. Nascimento & Vinícius A. Cabral & Ivo C. Silva Junior & Frederico F. Panoeiro & Leonardo M. Honório & André L. M. Marcato, 2021. "Spillage Forecast Models in Hydroelectric Power Plants Using Information from Telemetry Stations and Hydraulic Control," Energies, MDPI, vol. 14(1), pages 1-16, January.
    8. Periçaro, Gislaine A. & Karas, Elizabeth W. & Gonzaga, Clóvis C. & Marcílio, Débora C. & Oening, Ana Paula & Matioli, Luiz Carlos & Detzel, Daniel H.M. & de Geus, Klaus & Bessa, Marcelo R., 2020. "Optimal non-anticipative scenarios for nonlinear hydro-thermal power systems," Applied Mathematics and Computation, Elsevier, vol. 387(C).

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