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Bidding hydropower generation: Integrating short- and long-term scheduling

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Listed:
  • Fleten, Stein-Erik
  • Haugstvedt, Daniel
  • Steinsbø, Jens Arne
  • Belsnes, Michael
  • Fleischmann, Franziska

Abstract

Bidding of flexible reservoir hydropower in day-ahead (spot) auctions needs to be done under uncertainty of electricity prices and inflow to reservoirs. The presence of reservoirs also means that the short-term problem of determining bids for the next 12–36 hours is a part of a long term problem in which the question is whether to release water now or store it for the future. This multi-scale challenge is usually addressed by using several models for hydropower planning, at least one long-term model and one short-term model. We present a multistage stochasticmixed integer programming model that has a fine time resolution on near term, and a coarser resolution going forward. It handles price as a stochastic parameter and assumes deterministic inflow as it is intended for use in the winter season.

Suggested Citation

  • Fleten, Stein-Erik & Haugstvedt, Daniel & Steinsbø, Jens Arne & Belsnes, Michael & Fleischmann, Franziska, 2011. "Bidding hydropower generation: Integrating short- and long-term scheduling," MPRA Paper 44450, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:44450
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    References listed on IDEAS

    as
    1. G. Pritchard & G. Zakeri, 2003. "Market Offering Strategies for Hydroelectric Generators," Operations Research, INFORMS, vol. 51(4), pages 602-612, August.
    2. Philip J. Neame & Andrew B. Philpott & Geoffrey Pritchard, 2003. "Offer Stack Optimization in Electricity Pool Markets," Operations Research, INFORMS, vol. 51(3), pages 397-408, June.
    3. Fleten, Stein-Erik & Kristoffersen, Trine Krogh, 2007. "Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer," European Journal of Operational Research, Elsevier, vol. 181(2), pages 916-928, September.
    4. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, University Library of Munich, Germany, revised 13 Nov 2003.
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    Cited by:

    1. Ali Thaeer Hammid & Omar I. Awad & Mohd Herwan Sulaiman & Saraswathy Shamini Gunasekaran & Salama A. Mostafa & Nallapaneni Manoj Kumar & Bashar Ahmad Khalaf & Yasir Amer Al-Jawhar & Raed Abdulkareem A, 2020. "A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems," Energies, MDPI, vol. 13(11), pages 1-21, June.
    2. Ak, Mümtaz & Kentel, Elcin & Savasaneril, Secil, 2019. "Quantifying the revenue gain of operating a cascade hydropower plant system as a pumped-storage hydropower system," Renewable Energy, Elsevier, vol. 139(C), pages 739-752.
    3. Wim Ackooij & Debora Daniela Escobar & Martin Glanzer & Georg Ch. Pflug, 2020. "Distributionally robust optimization with multiple time scales: valuation of a thermal power plant," Computational Management Science, Springer, vol. 17(3), pages 357-385, October.

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    More about this item

    Keywords

    Hydropower scheduling; bidding strategies; stochastic programming;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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