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Approximate stochastic dynamic programming for hydroelectric production planning

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  • Zéphyr, Luckny
  • Lang, Pascal
  • Lamond, Bernard F.
  • Côté, Pascal

Abstract

This paper presents a novel approach for approximate stochastic dynamic programming (ASDP) over a continuous state space when the optimization phase has a near-convex structure. The approach entails a simplicial partitioning of the state space. Bounds on the true value function are used to refine the partition. We also provide analytic formulae for the computation of the expectation of the value function in the “uni-basin” case where natural inflows are strongly correlated. The approach is experimented on several configurations of hydro-energy systems. It is also tested against actual industrial data.

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  • Zéphyr, Luckny & Lang, Pascal & Lamond, Bernard F. & Côté, Pascal, 2017. "Approximate stochastic dynamic programming for hydroelectric production planning," European Journal of Operational Research, Elsevier, vol. 262(2), pages 586-601.
    • Handle: RePEc:eee:ejores:v:262:y:2017:i:2:p:586-601
    DOI: 10.1016/j.ejor.2017.03.050
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    Cited by:

    1. Cruise, James R. & Flatley, Lisa & Zachary, Stan, 2018. "Impact of storage competition on energy markets," European Journal of Operational Research, Elsevier, vol. 269(3), pages 998-1012.
    2. Jie Ning & Matthew J. Sobel, 2019. "Easy Affine Markov Decision Processes," Operations Research, INFORMS, vol. 67(6), pages 1719-1737, November.
    3. Luckny Zéphyr & C. Lindsay Anderson, 2018. "Stochastic dynamic programming approach to managing power system uncertainty with distributed storage," Computational Management Science, Springer, vol. 15(1), pages 87-110, January.
    4. Luckny Zephyr & Bernard F. Lamond & Pascal Lang, 2024. "Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization," Computational Management Science, Springer, vol. 21(1), pages 1-44, June.

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