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Optimized operating rules for short-term hydropower planning in a stochastic environment

Author

Listed:
  • Alexia Marchand

    (Polytechnique Montréal)

  • Michel Gendreau

    (Polytechnique Montréal)

  • Marko Blais

    (Hydro-Québec)

  • Jonathan Guidi

    (Hydro-Québec)

Abstract

To operate a large-scale hydropower production system in an ever-changing environment, operating rules are a convenient way of communication between short-term planners and real-time dispatchers. This articles presents a new form of operating rules, and a solution approach to solve the short-term planning problem directly in the space of rules. Our operating rules are designed to handle complex hydro-valleys and highly constrained reservoirs. Our solution approach is based on tabu search and easily implemented. Uncertainty on inflows and electrical load is represented in the mathematical model via a 2-stage scenario tree. Numerical experiments on real instances from Hydro-Québec show that our approach is able to find good stochastic solutions while respecting the operational timing, and it improves the objective value by up to 54% in instances with moderate to high inflows.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:3:d:10.1007_s10287-019-00348-2
    DOI: 10.1007/s10287-019-00348-2
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    References listed on IDEAS

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    1. 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.
    2. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    3. W. Ackooij & I. Danti Lopez & A. Frangioni & F. Lacalandra & M. Tahanan, 2018. "Large-scale unit commitment under uncertainty: an updated literature survey," Annals of Operations Research, Springer, vol. 271(1), pages 11-85, December.
    4. Moradi, Saeed & Khanmohammadi, Sohrab & Hagh, Mehrdad Tarafdar & Mohammadi-ivatloo, Behnam, 2015. "A semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem," Energy, Elsevier, vol. 88(C), pages 244-259.
    5. Julien Keutchayan & Michel Gendreau & Antoine Saucier, 2017. "Quality evaluation of scenario-tree generation methods for solving stochastic programming problems," Computational Management Science, Springer, vol. 14(3), pages 333-365, July.
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    Cited by:

    1. Suwapat Kosasaeng & Nirat Yamoat & Seyed Mohammad Ashrafi & Anongrit Kangrang, 2022. "Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization," Sustainability, MDPI, vol. 14(23), pages 1-14, December.
    2. Songphol Songsaengrit & Anongrit Kangrang, 2022. "Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization," Sustainability, MDPI, vol. 14(14), pages 1-17, July.

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