IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/44450.html
   My bibliography  Save this paper

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

Author

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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/44450/1/MPRA_paper_44450.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boomsma, Trine Krogh & Juul, Nina & Fleten, Stein-Erik, 2014. "Bidding in sequential electricity markets: The Nordic case," European Journal of Operational Research, Elsevier, vol. 238(3), pages 797-809.
    2. Ellen Krohn Aasgård, 2017. "Hydropower Bidding Using Linearized Start-Ups," Energies, MDPI, vol. 10(12), pages 1-13, November.
    3. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2020. "Strategic offering of a flexible producer in day-ahead and intraday power markets," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1136-1153.
    4. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
    5. van Ackere, Ann & Ochoa, Patricia, 2010. "Managing a hydro-energy reservoir: A policy approach," Energy Policy, Elsevier, vol. 38(11), pages 7299-7311, November.
    6. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    7. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    8. Laureen Deman & Quentin Boucher & Sonia Djebali & Guillaume Guerard & C?dric Clastres, 2023. "Bidding strategy of storage hydropower plants in reserve markets," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2023(2), pages 77-101.
    9. Finn R. Førsund, 2015. "Hydropower Economics," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4899-7519-5, July-Dece.
    10. Andreas Dietrich & Christian Furtwängler & Christoph Weber, "undated". "Thesenpapier: Managing combined power and heat portfolios in sequential spot power markets under uncertainty," EWL Working Papers 2003, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
    11. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).
    12. Bungener, Stephane & Hackl, Roman & Van Eetvelde, Greet & Harvey, Simon & Marechal, Francois, 2015. "Multi-period analysis of heat integration measures in industrial clusters," Energy, Elsevier, vol. 93(P1), pages 220-234.
    13. Klein Haneveld, Willem K. & Vlerk, Maarten H. van der, 2002. "Integrated chance constraints: reduced forms and an algorithm," Research Report 02A33, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    14. Hewings, Geoffrey J.D. & Dridi, Chokri & Guilhoto, Joaquim J.M., 2005. "Impacts of reallocation of resource constraints on the northeast economy of Brazil," MPRA Paper 38210, University Library of Munich, Germany.
    15. Michael Chen & Sanjay Mehrotra & Dávid Papp, 2015. "Scenario generation for stochastic optimization problems via the sparse grid method," Computational Optimization and Applications, Springer, vol. 62(3), pages 669-692, December.
    16. 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.
    17. Daniel F. Salas & Warren B. Powell, 2018. "Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 106-123, February.
    18. Conejo, Antonio J., 2001. "A decomposition procedure based on approximate newton directions," DES - Working Papers. Statistics and Econometrics. WS ws010906, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    20. Gro Klaeboe & Anders Lund Eriksrud & Stein-Erik Fleten, 2013. "Benchmarking time series based forecasting models for electricity balancing market prices," Working Papers 2013-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:44450. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.