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A successive linear programming algorithm with non-linear time series for the reservoir management problem

Author

Listed:
  • Charles Gauvin

    (Polytechnique Montréal)

  • Erick Delage

    (HEC Montréal)

  • Michel Gendreau

    (Polytechnique Montréal)

Abstract

This paper proposes a multi-stage stochastic programming formulation based on affine decision rules for the reservoir management problem. Our approach seeks to find a release schedule that balances flood control and power generation objectives while considering realistic operating conditions as well as variable water head. To deal with the non-convexity introduced by the variable water head, we implement a simple, yet effective, successive linear programming algorithm. We also introduce a novel non-linear inflow representation that captures serial correlation of arbitrary order. We test our method on a small real river system and discuss policy implications. Our results namely show that our method can decrease flood risk and increase production compared to the historical decisions, albeit at the cost of reduced final storages.

Suggested Citation

  • Charles Gauvin & Erick Delage & Michel Gendreau, 2018. "A successive linear programming algorithm with non-linear time series for the reservoir management problem," Computational Management Science, Springer, vol. 15(1), pages 55-86, January.
    • Handle: RePEc:spr:comgts:v:15:y:2018:i:1:d:10.1007_s10287-017-0295-4
    DOI: 10.1007/s10287-017-0295-4
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    References listed on IDEAS

    as
    1. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
    2. Shapiro, Alexander, 2011. "Analysis of stochastic dual dynamic programming method," European Journal of Operational Research, Elsevier, vol. 209(1), pages 63-72, February.
    3. Gauvin, Charles & Delage, Erick & Gendreau, Michel, 2017. "Decision rule approximations for the risk averse reservoir management problem," European Journal of Operational Research, Elsevier, vol. 261(1), pages 317-336.
    4. Álvaro Lorca & X. Andy Sun & Eugene Litvinov & Tongxin Zheng, 2016. "Multistage Adaptive Robust Optimization for the Unit Commitment Problem," Operations Research, INFORMS, vol. 64(1), pages 32-51, February.
    5. Steeger, Gregory & Rebennack, Steffen, 2017. "Dynamic convexification within nested Benders decomposition using Lagrangian relaxation: An application to the strategic bidding problem," European Journal of Operational Research, Elsevier, vol. 257(2), pages 669-686.
    6. Lohmann, Timo & Hering, Amanda S. & Rebennack, Steffen, 2016. "Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling," European Journal of Operational Research, Elsevier, vol. 255(1), pages 243-258.
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    Cited by:

    1. Kristina Rognlien Dahl, 2019. "Management of a hydropower system via convex duality," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(1), pages 43-71, February.

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