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Energy Systems

, Volume 10, Issue 3, pp 543–565 | Cite as

Hydropower bidding in a multi-market setting

  • Ellen Krohn AasgårdEmail author
  • Stein-Erik Fleten
  • Michal Kaut
  • Kjetil Midthun
  • Gerardo A. Perez-Valdes
Original Paper
First Online:

Abstract

We present a literature survey and research gap analysis of mathematical and statistical methods used in the context of optimizing bids in electricity markets. Particularly, we are interested in methods for hydropower producers that participate in multiple, sequential markets for short-term delivery of physical power. As most of the literature focus on day-ahead bidding and thermal energy producers, there are important research gaps for hydropower, which require specialized methods due to the fact that electricity may be stored as water in reservoirs. Our opinion is that multi-market participation, although reportedly having a limited profit potential, can provide gains in flexibility and system stability for hydro producers. We argue that managing uncertainty is of key importance for making good decision support tools for the multi-market bidding problem. Considering uncertainty calls for some form of stochastic programming, and we define a modelling process that consists of three interconnected tasks; mathematical modelling, electricity price forecasting and scenario generation. We survey research investigating these tasks and point out areas that are not covered by existing literature.

Keywords

Short-term physical bidding Multi-market Hydropower 
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Notes

Acknowledgements

This work was supported by the Research Council of Norway under Project Number 255100/E20 MultiSharm.

References

  1. 1.
    Aasgård, E.K., Andersen, G.S., Fleten, S.-E., Haugstvedt, D.: Evaluating a stochastic-programming-based bidding model for a multireservoir system. IEEE Trans. Power Syst. 29(4), 1748–1757 (2014)CrossRefGoogle Scholar
  2. 2.
    Aasgård, E.K., Naversen, C.Ø., Fodstad, M., Skjelbred, H.I.: Optimizing day-ahead bid curves in hydropower production. Energy Systems, pp. 1–19 (2017)Google Scholar
  3. 3.
    Anbazhagan, S., Kumarappan, N.: Day-ahead deregulated electricity market price forecasting using recurrent neural network. IEEE Syst. J. 7(4), 866–872 (2013)CrossRefGoogle Scholar
  4. 4.
    Anderson, E.J., Philpott, A.B.: Using supply functions for offering generation into an electricity market. Oper Res 50(3), 477–489 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Andersen, J., Kaut, M., Tomasgard, A.: Stochastic model for short-term balancing of supply and consumption of electricity. In: Modelling and Optimisation of Renewable Energy Systems, pp. 37–66. School of Business and Social Sciences, Aarhus University, Aarhus (2015). ISBN 9788793195189, http://pure.au.dk/portal/en/publications-research/modelling-and-optimisation-of-renewable-energy-systems%2880b927a9-a945-4d22-9e8e-3210714bea9c%29.html
  6. 6.
    Bayraksan, G., Morton, D.P.: Assessing solution quality in stochastic programs. Math Program 108(2–3), 495–514 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Bayraksan, G., Morton, D.P., Partani, A.: Simulation-based optimality tests for stochastic programs. In: Infanger, G. (ed.) Stochastic Programming: The State of the Art In Honor of George B. Dantzig, International Series in Operations Research & Management Science, pp. 37–55. Springer, Berlin (2011).  https://doi.org/10.1007/978-1-4419-1642-6 zbMATHCrossRefGoogle Scholar
  8. 8.
    Berrada, A., Loudiyi, K., Zorkani, I.: Valuation of energy storage in energy and regulation markets. Energy 115(Part 1), 1109–1118 (2016).  https://doi.org/10.1016/j.energy.2016.09.093 CrossRefGoogle Scholar
  9. 9.
    Boomsma, T.K., Juul, N., Fleten, S.-E.: Bidding in sequential electricity markets: the Nordic case. Eur. J. Oper. Res. 238(3), 797–809 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Brolin, M.O., Söder, L.: Modeling Swedish real-time balancing power prices using nonlinear time series models. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 358–363. IEEE (2010)Google Scholar
  11. 11.
    Chan, K.F., Gray, P., Van Campen, B.: A new approach to characterizing and forecasting electricity price volatility. Int. J. Forecast. 24(4), 728–743 (2008)CrossRefGoogle Scholar
  12. 12.
    Chopra, V., Ziemba, W.: The effects of errors in means, variances, and covariances on optimal portfolio choice. J. Portfolio Manag. 19(2), 6–11 (1993)CrossRefGoogle Scholar
  13. 13.
    Conejo, A.J., Contreras, J., Espínola, R., Plazas, M.A.: Forecasting electricity prices for a day-ahead pool-based electric energy market. Int. J. Forecast. 21(3), 435–462 (2005)CrossRefGoogle Scholar
  14. 14.
    De Ladurantaye, D., Gendreau, M., Potvin, J.-Y.: Strategic bidding for price-taker hydroelectricity producers. IEEE Trans. Power Syst. 22(4), 2187–2203 (2007).  https://doi.org/10.1109/TPWRS.2007.907457 CrossRefGoogle Scholar
  15. 15.
    De Ladurantaye, D., Gendreau, M., Potvin, J.-Y.: Optimizing profits from hydroelectricity production. Comput. Oper. Res. 36(2), 499–529 (2009).  https://doi.org/10.1016/j.cor.2007.10.012 zbMATHCrossRefGoogle Scholar
  16. 16.
    Dupačová, J., Consigli, G., Wallace, S.W.: Scenarios for multistage stochastic programs. Ann. Oper. Res. 100, 25–53 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Faria, E., Fleten, S.-E.: Day-ahead market bidding for a nordic hydropower producer: taking the elbas market into account. Comput. Manag. Sci. 8(1), 75–101 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    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).  https://doi.org/10.1016/j.ejor.2006.08.023 zbMATHCrossRefGoogle Scholar
  19. 19.
    Fleten, S.-E., Haugstvedt, D., Steinsbø, J.A., Belsnes, M.M., Fleischmann, F.: Bidding hydropower generation: integrating short- and long-term scheduling. In: Proceedings of 17th Power Systems Computations Conference PSCC 2011, pp. 352–358 (2011)Google Scholar
  20. 20.
    Garcia, R.C., Contreras, J., Van Akkeren, M., Garcia, J.B.C.: A garch forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. 20(2), 867–874 (2005)CrossRefGoogle Scholar
  21. 21.
    Gjelsvik, A., Mo, B., Haugstad, A.: Long- and medium-term operations planning and stochastic modelling in hydro-dominated power systems based on stochastic dual dynamic programming. In: Pardalos, P.M., Rebennack, S., Pereira, M.V.F., Iliadis, N.A. (eds.) Handbook of Power Systems I, pp. 33–55. Springer, Berlin (2010)zbMATHCrossRefGoogle Scholar
  22. 22.
    Guo, J.-J., Luh, P.B.: Improving market clearing price prediction by using a committee machine of neural networks. IEEE Trans. Power Syst. 19(4), 1867–1876 (2004)CrossRefGoogle Scholar
  23. 23.
    Haldrup, N., Nielsen, M.Ø.: A regime switching long memory model for electricity prices. J. Econom. 135(1), 349–376 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Heitsch, H., Römisch, W., Strugarek, C.: Stability of multistage stochastic programs. SIAM J. Optim. 17(2), 511–525 (2006).  https://doi.org/10.1137/050632865 MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Heitsch, H., Römisch, W.: Scenario tree modelling for multistage stochastic programs. Math. Program. 118(2), 371–406 (2009).  https://doi.org/10.1007/s10107-007-0197-2 MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Heitsch, H., Römisch, W.: Scenario tree generation for multi-stage stochastic programs. In: Bertocchi, M., Consigli, G., Dempster, M.A.H. (eds.) Stochastic Optimization Methods in Finance and Energy, International Series in Operations Research and Management Science, vol. 163, chapter 14, pp. 313–341. Springer, Berlin (2011).  https://doi.org/10.1007/978-1-4419-9586-5_14 CrossRefGoogle Scholar
  27. 27.
    Høyland, K., Kaut, M., Wallace, S.W.: A heuristic for moment-matching scenario generation. Comput. Optim. Appl. 24(2–3), 169–185 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Jónsson, T., Pinson, P., Nielsen, H.A., Madsen, H., Nielsen, T.S.: Forecasting electricity spot prices accounting for wind power predictions. IEEE Trans. Sustain. Energy 4(1), 210–218 (2013)CrossRefGoogle Scholar
  29. 29.
    Kaut, M.: A copula-based heuristic for scenario generation. Comput. Manag. Sci. 11(4), 503–516 (2014).  https://doi.org/10.1007/s10287-013-0184-4 MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Kaut, M.: Forecast-based scenario-tree generation method. Optimization Online, e-print ID 2017-03-5898 (2017). http://www.optimization-online.org/DB_HTML/2017/03/5898.html
  31. 31.
    Kaut, M., Wallace, S.W.: Evaluation of scenario generation methods for stochastic programming. Pac. J. Optim. 3, 257–271 (2007)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Kaut, M., Midthun, K.T., Werner, A.S., Tomasgard, A., Hellemo, L., Fodstad, M.: Multi-horizon stochastic programming. Comput. Manag. Sci. 11(1–2), 179–193 (2014).  https://doi.org/10.1007/s10287-013-0182-6. Special Issue: Computational Techniques in Management ScienceMathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Kiesel, R., Paraschiv, F.: Econometric analysis of 15-minute intraday electricity prices. Energy Econ. 64, 77–90 (2017)CrossRefGoogle Scholar
  34. 34.
    King, A.J., Wallace, S.W.: Modeling with Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer, Berlin (2012).  https://doi.org/10.1007/978-0-387-87817-1
  35. 35.
    Klæboe, G., Fosso, O.B.: Optimal bidding in sequential physical markets—a literature review and framework discussion. PowerTech (POWERTECH), 2013 IEEE Grenoble, pp. 1–6 (2013)Google Scholar
  36. 36.
    Klæboe, G., Eriksrud, A.L., Fleten, S.-E.: Benchmarking time series based forecasting models for electricity balancing market prices. Energy Syst. 6(1), 43–61 (2015)CrossRefGoogle Scholar
  37. 37.
    Kosater, P., Mosler, K.: Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices. Appl. Energy 83(9), 943–958 (2006)CrossRefGoogle Scholar
  38. 38.
    Kristiansen, T.: Forecasting Nord Pool day-ahead prices with an autoregressive model. Energy Policy 49, 328–332 (2012)CrossRefGoogle Scholar
  39. 39.
    Li, G., Shi, J., Qu, X.: Modeling methods for genco bidding strategy optimization in the liberalized electricity spot market—a state-of-the-art review. Energy 36(8), 4686–4700 (2011).  https://doi.org/10.1016/j.energy.2011.06.015 CrossRefGoogle Scholar
  40. 40.
    Lindqvist, J.: Operation of a hydrothermal electric system: a multistage decision processr. AIEE Trans. Power Appar. Syst. 81, 1–7 (1962)CrossRefGoogle Scholar
  41. 41.
    Löhndorf, N., Wozabal, D., Minner, S.: Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Oper. Res. 61(4), 810–823 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Lu, N., Chow, J.H., Desrochers, A.A.: Pumped-storage hydro-turbine bidding strategies in a competitive electricity market. IEEE Trans. Power Syst. 19(2), 834–841 (2004)CrossRefGoogle Scholar
  43. 43.
    Olsson, M.: On optimal hydropower bidding in systems with wind power: modeling the impact of wind power on power markets. PhD thesis, KTH, Stockholm (2009)Google Scholar
  44. 44.
    Olsson, M., Söder, L.: Modeling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Trans. Power Syst. 23(2), 443–450 (2008)CrossRefGoogle Scholar
  45. 45.
    Paraschiv, F., Fleten, S.-E., Schürle, M.: A spot-forward model for electricity prices with regime shifts. Energy Econ. 47, 142–153 (2015).  https://doi.org/10.1016/j.eneco.2014.11.003. ISSN 0140-9883CrossRefGoogle Scholar
  46. 46.
    Pereira, M.V., Pinto, L.M.: Multi-stage stochastic optimization applied to energy planning. Math. Program. 52(1–3), 359–375 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Pflug, G.C.: Version-independence and nested distributions in multistage stochastic optimization. SIAM J. Optim. 20(3), 1406–1420 (2010).  https://doi.org/10.1137/080718401. ISSN 1095-7189MathSciNetzbMATHCrossRefGoogle Scholar
  48. 48.
    Pflug, G.C., Pichler, A.: Dynamic generation of scenario trees. Comput. Optim. Appl. 62(3), 641–668 (2015).  https://doi.org/10.1007/s10589-015-9758-0. ISSN 1573-2894MathSciNetzbMATHCrossRefGoogle Scholar
  49. 49.
    Pflug, G.C., Pichler, A.: From empirical observations to tree models for stochastic optimization: convergence properties. SIAM J. Optim. 26(3), 1715–1740 (2016).  https://doi.org/10.1137/15M1043376 MathSciNetzbMATHCrossRefGoogle Scholar
  50. 50.
    Philpott, A., Guan, Z., Khazaei, J., Zakeri, G.: Production inefficiency of electricity markets with hydro generation. Utilities Policy 18(4), 174–185 (2010). ISSN 0957-1787.  https://doi.org/10.1016/j.jup.2010.09.001. URL http://www.sciencedirect.com/science/article/pii/S0957178710000585. Designing Electricity Auctions
  51. 51.
    Pritchard, G., Philpott, A.B., Neame, P.J.: Hydroelectric reservoir optimization in a pool market. Math. Program. 103(3), 445–461 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  52. 52.
    Séguin, S., Fleten, S.-E., Côté, P., Pichler, A., Audet, C.: Stochastic short-term hydropower planning with inflow scenario trees. Eur. J. Oper. Res. 259(3), 1156–1168 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  53. 53.
    Stage, S., Larsson, Y.: Incremental cost of water power. Power apparatus and systems, part III. Trans. Am. Inst. Electr. Eng. 80(3), 361–364 (1961)Google Scholar
  54. 54.
    Steeger, G., Barroso, L.A., Rebennack, S.: Optimal bidding strategies for hydro-electric producers: a literature survey. IEEE Trans. Power Syst. 29(4), 1758–1766 (2014)CrossRefGoogle Scholar
  55. 55.
    Triki, C., Beraldi, P., Gross, G.: Optimal capacity allocation in multi-auction electricity markets under uncertainty. Comput. Oper. Res. 32(2), 201–217 (2005).  https://doi.org/10.1016/S0305-0548(03)00211-9 MathSciNetzbMATHCrossRefGoogle Scholar
  56. 56.
    Vespucci, M.T., Bertocchi, M., Tomasgard, A., Innorta, M.: Integration of Wind Power Production in a Conventional Power Production System: Stochastic Models and Performance Measures, pp. 129–152. Springer, Berlin (2013). ISBN 978-3-642-41080-2.  https://doi.org/10.1007/978-3-642-41080-2_5
  57. 57.
    Wang, P., Zareipour, H., Rosehart, W.D.: Descriptive models for reserve and regulation prices in competitive electricity markets. IEEE Trans. Smart Grid 5(1), 471–479 (2014)CrossRefGoogle Scholar
  58. 58.
    Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30, 1030–1081 (2014)CrossRefGoogle Scholar
  59. 59.
    Wolfgang, O., Haugstad, A., Mo, B., Gjelsvik, A., Wangensteen, I., Doorman, G.: Hydro reservoir handling in Norway before and after deregulation. Energy 34(10), 1642–1651 (2009)CrossRefGoogle Scholar
  60. 60.
    Yakowitz, S.: Dynamic programming applications in water resources. Water Resour. Res. 18(4), 673–696 (1982)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ellen Krohn Aasgård
    • 1
    Email author
  • Stein-Erik Fleten
    • 1
  • Michal Kaut
    • 2
  • Kjetil Midthun
    • 2
  • Gerardo A. Perez-Valdes
    • 2
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.Applied EconomicsSINTEF Technology and SocietyTrondheimNorway

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