Journées de l'optimisation 2017

HEC Montréal, 8-10 mai 2017

1er Atelier Canadien sur l'optimisation des soins de santé (CHOW)

HEC Montréal, 10-11 mai 2017

JOPT2017

HEC Montréal, 8 — 11 mai 2017

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Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402

M7 Planification et gestion des opérations hydroéléctriques 2 / Hydropower operations planning and management 2

8 mai 2017 15h30 – 17h10

Salle: St-Hubert

Présidée par Sara Séguin

3 présentations

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    15h30 - 15h55

    A hybrid Stochastic dynamic programming - Tabu Search approach for long-term energy planning

    • Yves Alain Mbeutcha, Présentateur, École Polytechnique de Montréal
    • Michel Gendreau, Polytechnique Montréal
    • Grégory Émiel, Hydro-Québec

    The long-term Energy planning can be modeled and solved using classical Stochastic Dynamic Programming (SDP). However, SDP fails to represent adequately the risk brought by some inflows hypothesis on energy reliability of the Hydro-Quebec’s power system. We propose a Tabu-Search approach to improve SDP policies performance.

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    15h55 - 16h20

    A Least Square Monte Carlo method applied to the Kemano system

    • Nicolas Léveillé, Présentateur, HEC
    • Michel Denault, GERAD - HEC Montréal
    • Pascal Côté, Rio Tinto

    A hydropower management policy is built using a Least Square Monte Carlo method. The inflows are simulated by the corporate partner Rio Tinto, using a hydrological model. Numerical experiments are conducted on the Kemano system in British Colombia.

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    16h20 - 16h45

    A Q-learning approach for short-term hydropower generation

    • Mahdi Zarghami, Présentateur, Ecole de Technologie supérieure
    • Fausto Errico, École de technologie supérieure

    Stochastic dynamic programming (SDP) has been widely applied to hydropower optimization. However, space-state discretization and the course of modeling might significantly deteriorate the SDP performances. In this study we explore the Q-learning algorithm for the short-term management of a multi-reservoir system. Computational results prove the efficiency of the proposed algorithm.

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