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Energy Systems

, Volume 9, Issue 2, pp 257–275 | Cite as

Optimizing day-ahead bid curves in hydropower production

  • Ellen Krohn Aasgård
  • Christian Øyn Naversen
  • Marte Fodstad
  • Hans Ivar Skjelbred
Original Paper

Abstract

In deregulated electricity markets, hydropower producers must bid their production into the day-ahead market. For price-taking producers, it is optimal to offer energy according to marginal costs, which for hydropower are determined by the opportunity cost of using water that could have been stored for future production. At the time of bidding, uncertainty of future prices and inflows may affect the opportunity costs and thus also the optimal bids. This paper presents a model for hydropower bidding where the bids are based on optimal production schedules from a stochastic model. We also present a heuristic algorithm for reducing the bid matrix into the size required by the market operator. Results for the optimized bids and the reduction algorithm are analyzed in a case study showing how uncertain inflows may affect the bids.

Keywords

Unit-commitment Stochastic programming 

References

  1. 1.
    Fosso, O.B., Gjelsvik, A., Haugstad, A., Mo, B., Wangensteen, I.: Generation scheduling in a deregulated system. The Norwegian case. IEEE Trans. Power Syst. 14(1), 75–81 (1999)CrossRefGoogle Scholar
  2. 2.
    Pereira, M.V.F., Pinto, L.M.V.G.: Stochastic optimization of a multireservoir hydroelectric system: a decomposition approach. Water Resour. Res. 21(6), 779–892 (1985)CrossRefGoogle Scholar
  3. 3.
    Fleten, S.-E., Kristoffersen, T.K.: Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer. Eur. J. Oper. Res. 181(2), 916–928 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Pritchard, G., Zakeri, G.: Market offering strategies for hydro-electric generators. Oper. Res. 51, 602612 (2003)CrossRefGoogle Scholar
  5. 5.
    Pritchard, G., Philpott, A.B., Neame, P.J.: Hydroelectric reservoir optimization in a pool market. Math. Program. 103(3), 445461 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Fleten, S.-E., Kristoffersen, T.K.: Short-term hydropower production planning by stochastic programming. Comput. Oper. Res. 35, 2656–2671 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Séguin, S., Fleten, S.-E., Côté, P., Pichler, A., Audet, C.: Stochastic short-term hydropower planning with inflow scenario trees. Technical Report G-2015-97. GERAD, Montreal, Canada (2015)Google Scholar
  8. 8.
    Belsnes, M.M., Wolfgang, O., Follestad, T., Aasgård, E.K.: Applying successive linear programming for stochastic short-term hydropower optimization. Electr. Power Syst. Res. 130, 167–180 (2016)CrossRefGoogle Scholar
  9. 9.
    Padhy, N.P.: Unit commitment—a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Steeger, G., Barroso, L.A., Rebennack, S.: Optimal bidding strategies for hydro-electric producers: a literature survey. IEEE Trans. Power Syst. 29(4), 17581766 (2014)CrossRefGoogle Scholar
  11. 11.
    Fosso, O.B., Belsnes, M.M.: Short-term hydro scheduling in a liberalized power system. Int. Conf. Power Syst. Technol. 2, 1321–1326 (2004)Google Scholar
  12. 12.
  13. 13.
    Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Springer, New York (1997)zbMATHGoogle Scholar
  14. 14.
    Gjelsvik, A., Belsnes, M.M., Haugstad, A.: An algorithm for stochastic medium-term hydrothermal scheduling under spot price uncertainty. In: Proceedings 13th Power System Computation Conference, Trondheim, 28 June–2 July 1999Google Scholar
  15. 15.
    Fodstad, M., Henden, A.L., Helseth, A.: Hydropower scheduling in day-ahead and balancing markets. In: 12th International Conference on the European Energy Market (2015)Google Scholar
  16. 16.
    Plazas, M.A., Conejo, A.J., Prieto, F.J.: Multimarket optimal bidding for a power producer. IEEE Trans. Power Syst. 20(4), 2041–2049 (2005)CrossRefGoogle Scholar
  17. 17.
    Ottesen, S.Ø., Tomasgard, A., Fleten, S.-E.: Prosumer bidding and scheduling in electricity markets. Energy 94, 828843 (2016)CrossRefGoogle Scholar
  18. 18.
    SKM Market Predictor AS P.O Box 2637, 7414 Trondheim, Norway. Tel: (+47) 73 80 58 00, Fax: (+47) 73 80 58 01. Contact: info@skmenergy.comGoogle Scholar
  19. 19.
    Bergström, S.: The HBV model—its structure and applications. SMHI Reports RH, No. 4, Norrköping (1992)Google Scholar
  20. 20.
    Heitsch, H., Römisch, W.: Scenario reduction algorithms in stochastic programming. Comput. Optim. Appl. 24(2–3), 187–206 (2003)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Ellen Krohn Aasgård
    • 1
  • Christian Øyn Naversen
    • 2
  • Marte Fodstad
    • 2
  • Hans Ivar Skjelbred
    • 2
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.SINTEF Energy ResearchTrondheimNorway

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