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Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

Author

Listed:
  • Kristine Klock Fleten

    (Aneo AS)

  • Ellen Krohn Aasgård

    (Aneo AS)

  • Liyuan Xing

    (Aneo AS)

  • Hanne Høie Grøttum

    (Aneo AS)

  • Stein-Erik Fleten

    (Norwegian University of Science and Technology)

  • Odd Erik Gundersen

    (Norwegian University of Science and Technology
    Aneo AS)

Abstract

Aneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

Suggested Citation

  • Kristine Klock Fleten & Ellen Krohn Aasgård & Liyuan Xing & Hanne Høie Grøttum & Stein-Erik Fleten & Odd Erik Gundersen, 2024. "Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling," Computational Management Science, Springer, vol. 21(2), pages 1-24, December.
    • Handle: RePEc:spr:comgts:v:21:y:2024:i:2:d:10.1007_s10287-024-00525-y
    DOI: 10.1007/s10287-024-00525-y
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