Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches
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DOI: 10.1016/j.renene.2018.07.026
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References listed on IDEAS
- 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.
- 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.
- 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.
- Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
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Cited by:
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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).
- 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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Keywords
Hydropower production function; Long-term generation scheduling problem; Piecewise linear model; Constant productivity model; Stochastic dual dynamic programming;All these keywords.
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