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Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling

Author

Listed:
  • Arild Helseth

    (SINTEF Energy, Sem Sælands vei 11, Trondheim 7465, Norway)

  • Hallvard Braaten

    (Department of Mathematical Sciences, The Norwegian University of Science and Technology, Trondheim 7491, Norway)

Abstract

Stochastic dual dynamic programming (SDDP) has become a popular algorithm used in practical long-term scheduling of hydropower systems. The SDDP algorithm is computationally demanding, but can be designed to take advantage of parallel processing. This paper presents a novel parallel scheme for the SDDP algorithm, where the stage-wise synchronization point traditionally used in the backward iteration of the SDDP algorithm is partially relaxed. The proposed scheme was tested on a realistic model of a Norwegian water course, proving that the synchronization point relaxation significantly improves parallel efficiency.

Suggested Citation

  • Arild Helseth & Hallvard Braaten, 2015. "Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling," Energies, MDPI, vol. 8(12), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12431-14297:d:60828
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    References listed on IDEAS

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    1. Wolfgang, Ove & Haugstad, Arne & Mo, Birger & Gjelsvik, Anders & Wangensteen, Ivar & Doorman, Gerard, 2009. "Hydro reservoir handling in Norway before and after deregulation," Energy, Elsevier, vol. 34(10), pages 1642-1651.
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    Cited by:

    1. Oscar Dowson & Lea Kapelevich, 2021. "SDDP.jl : A Julia Package for Stochastic Dual Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 27-33, January.
    2. Martin N. Hjelmeland & Arild Helseth & Magnus Korpås, 2019. "Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty," Energies, MDPI, vol. 12(1), pages 1-15, January.
    3. Jianjian Shen & Xiufei Zhang & Jian Wang & Rui Cao & Sen Wang & Jun Zhang, 2019. "Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions," Energies, MDPI, vol. 12(1), pages 1-19, January.
    4. D. Ávila & A. Papavasiliou & N. Löhndorf, 2022. "Parallel and distributed computing for stochastic dual dynamic programming," Computational Management Science, Springer, vol. 19(2), pages 199-226, June.
    5. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    6. Xinyu Wu & Rui Guo & Xilong Cheng & Chuntian Cheng, 2021. "Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System," Energies, MDPI, vol. 14(3), pages 1-15, January.

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