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Stochastic short-term hydropower planning with inflow scenario trees

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  • Séguin, Sara
  • Fleten, Stein-Erik
  • Côté, Pascal
  • Pichler, Alois
  • Audet, Charles

Abstract

This paper presents an optimization approach to solve the short-term hydropower unit commitment and loading problem with uncertain inflows. A scenario tree is built based on a forecasted fan of inflows, which is developed using the weather forecast and the historical weather realizations. The tree-building approach seeks to minimize the nested distance between the stochastic process of historical inflow data and the multistage stochastic process represented in the scenario tree. A two-phase multistage stochastic model is used to solve the problem. The proposed approach is tested on a 31 day rolling-horizon with daily forecasted inflows for three power plants situated in the province of Quebec, Canada, that belong to the company Rio Tinto.

Suggested Citation

  • Séguin, Sara & Fleten, Stein-Erik & Côté, Pascal & Pichler, Alois & Audet, Charles, 2017. "Stochastic short-term hydropower planning with inflow scenario trees," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1156-1168.
    • Handle: RePEc:eee:ejores:v:259:y:2017:i:3:p:1156-1168
    DOI: 10.1016/j.ejor.2016.11.028
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    References listed on IDEAS

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    Cited by:

    1. Yoan Villeneuve & Sara Séguin & Abdellah Chehri, 2023. "AI-Based Scheduling Models, Optimization, and Prediction for Hydropower Generation: Opportunities, Issues, and Future Directions," Energies, MDPI, vol. 16(8), pages 1-27, April.
    2. Ellen Krohn Aasgård & Hans Ivar Skjelbred, 2020. "Progressive hedging for stochastic programs with cross-scenario inequality constraints," Computational Management Science, Springer, vol. 17(1), pages 141-160, January.
    3. Alexia Marchand & Michel Gendreau & Marko Blais & Jonathan Guidi, 2019. "Optimized operating rules for short-term hydropower planning in a stochastic environment," Computational Management Science, Springer, vol. 16(3), pages 501-519, July.
    4. Yang, Zhe & Wang, Yufeng & Yang, Kan, 2022. "The stochastic short-term hydropower generation scheduling considering uncertainty in load output forecasts," Energy, Elsevier, vol. 241(C).
    5. Firehiwot Girma Dires & Mikael Amelin & Getachew Bekele, 2023. "Long-Term Hydropower Planning for Ethiopia: A Rolling Horizon Stochastic Programming Approach with Uncertain Inflow," Energies, MDPI, vol. 16(21), pages 1-15, November.
    6. Changjun Wang & Shutong Chen, 2019. "Planning of Cascade Hydropower Stations with the Consideration of Long-Term Operations under Uncertainties," Complexity, Hindawi, vol. 2019, pages 1-23, November.
    7. Keles, Dogan & Dehler-Holland, Joris, 2022. "Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming," Energy Economics, Elsevier, vol. 106(C).
    8. Wang, Fengjuan & Xie, Yachen & Xu, Jiuping, 2019. "Reliable-economical equilibrium based short-term scheduling towards hybrid hydro-photovoltaic generation systems: Case study from China," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Henao, Felipe & Dyner, Isaac, 2020. "Renewables in the optimal expansion of colombian power considering the Hidroituango crisis," Renewable Energy, Elsevier, vol. 158(C), pages 612-627.
    10. Lei, Kaixuan & Chang, Jianxia & Long, Ruihao & Wang, Yimin & Zhang, Hongxue, 2022. "Cascade hydropower station risk operation under the condition of inflow uncertainty," Energy, Elsevier, vol. 244(PA).
    11. Liu, Benxi & Cheng, Chuntian & Wang, Sen & Liao, Shengli & Chau, Kwok-Wing & Wu, Xinyu & Li, Weidong, 2018. "Parallel chance-constrained dynamic programming for cascade hydropower system operation," Energy, Elsevier, vol. 165(PA), pages 752-767.
    12. Thomas Kuppelwieser & David Wozabal, 2023. "Intraday power trading: toward an arms race in weather forecasting?," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 57-83, March.
    13. Krešimir Fekete & Srete Nikolovski & Zvonimir Klaić & Ana Androjić, 2019. "Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming," Energies, MDPI, vol. 12(9), pages 1-25, April.
    14. Henao, Felipe & Rodriguez, Yeny & Viteri, Juan Pablo & Dyner, Isaac, 2019. "Optimising the insertion of renewables in the Colombian power sector," Renewable Energy, Elsevier, vol. 132(C), pages 81-92.
    15. Löschenbrand, Markus, 2020. "Finding multiple Nash equilibria via machine learning-supported Gröbner bases," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1178-1189.
    16. Moreira, Alexandre & Pozo, David & Street, Alexandre & Sauma, Enzo & Strbac, Goran, 2021. "Climate‐aware generation and transmission expansion planning: A three‐stage robust optimization approach," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1099-1118.
    17. Wim Ackooij & Debora Daniela Escobar & Martin Glanzer & Georg Ch. Pflug, 2020. "Distributionally robust optimization with multiple time scales: valuation of a thermal power plant," Computational Management Science, Springer, vol. 17(3), pages 357-385, October.
    18. Rodríguez, Jesús A. & Anjos, Miguel F. & Côté, Pascal & Desaulniers, Guy, 2021. "Accelerating Benders decomposition for short-term hydropower maintenance scheduling," European Journal of Operational Research, Elsevier, vol. 289(1), pages 240-253.

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