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Assessing hydropower operational profitability considering energy and reserve markets

Assessing hydropower operational profitability considering energy and reserve markets

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The authors analyse the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting. A mathematical model based on stochastic dynamic programming is used to compute the water values for the system considering different power plant configurations. The uncertainties in inflow and both energy and reserve capacity prices are considered through a discrete Markov chain. Subsequently, the system operation is simulated based on the obtained water values to assess system performance and expected revenues from the two markets. The model is applied in a case study for a Norwegian hydropower producer, showing how the power plant operation changes and profitability increases when considering sale of reserve capacity. The authors emphasise on how the water values are influenced by the opportunity to sell reserve capacity, and assess how the representation of non-convex relationships in the water value computations as well as simulation influence the profitability.

References

    1. 1)
      • 14. Steeger, G., Rebennack, S.: ‘Dynamic convexification within nested benders decomposition using Lagrangian relaxation: an application to the strategic bidding problem’, Eur. J. Oper. Res., 2017, 257, (2), pp. 669686.
    2. 2)
      • 32. Rebennack, S.: ‘Computing tight bounds via piecewise linear functions through the example of circle cutting problems’, Math. Methods Oper. Res., 2016, 84, (1), pp. 357.
    3. 3)
      • 7. Labadie, J.W.: ‘Optimal operation of multireservoir systems: state-of-the-art review’, J. Water Resour. Plan. Manage., 2004, 130, (2), pp. 93111.
    4. 4)
      • 27. Nilsson, O., Sjelvgren, D.: ‘Hydro unit start-up costs and their impact on the short term scheduling strategies of Swedish power producers’, IEEE Trans. Power Syst., 1997, 12, (1), pp. 3844.
    5. 5)
      • 28. Martino, S., Haff, I.H., Løland, A., et al: ‘Statistical properties of historical inflow series for long-term models’. Tech. Rep. SAMBA/50/13, Norwegian Computing Center, 2014.
    6. 6)
      • 26. Chazarra, M., Garciá-González, J., Pérez-Díaz, J.I., et al: ‘Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets’, Electr. Power Syst. Res., 2016, 130, pp. 6777.
    7. 7)
      • 20. Hjelmeland, M.N., Helseth, A., Korpås, M.: ‘A case study on medium-term hydropower scheduling with sales of capacity’, Energy Proc., 2016, 87, pp. 124131.
    8. 8)
      • 1. Little, J.D.C.: ‘The use of storage water in a hydroelectric system’, J. Oper. Res. Soc. Am., 1955, 3, (2), pp. 187197.
    9. 9)
      • 5. Wolfgang, O., Haugstad, A., Mo, B., et al: ‘Hydro reservoir handling in Norway before and after deregulation’, Energy, 2009, 34, (10), pp. 16421651.
    10. 10)
      • 17. Helseth, A., Fodstad, M., Mo, B.: ‘Optimal medium-term hydropower scheduling considering energy and reserve capacity markets’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 934942.
    11. 11)
      • 29. Fourer, R., Gay, D. M., Kerninghan, B. W.: ‘AMPL: a modeling language for mathematical programming’ (Duxburry, 2003, 2nd edn.).
    12. 12)
      • 21. Helseth, A., Gjelsvik, A., Mo, B., et al: ‘A model for optimal scheduling of hydro thermal systems including pumped-storage and wind power’, IET. Gener. Transm. Distrib., 2013, 7, (12), pp. 14261434.
    13. 13)
      • 16. Abgottspon, H., Andersson, G.: ‘Medium-term optimization of pumped hydro storage with stochastic intrastage subproblems’. Proc. 18th Power System Computation Conf., Wroclaw, Poland, 2014.
    14. 14)
      • 31. Padberg, M.: ‘Approximating separable nonlinear functions via mixed zero-one programs’, Oper. Res. Lett., 2000, 27, (1), pp. 15.
    15. 15)
      • 24. Guisandez, I., Perez-Diaz, J.I., Wilhelmi, J.R.: ‘The influence of environmental constraints on the water value’, Energies, 2016, 9, (6), pp. 121.
    16. 16)
      • 4. Turgeon, A., Charbonneau, R.: ‘An aggregation–disaggregation approach to long-term reservoir management’, Water Resour. Res., 1998, 34, (12), pp. 35853594.
    17. 17)
      • 19. 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., Rebennack, S., Pereira, M., et al(Eds): ‘Handbook of power systems I’ (Springer, 2010), pp. 3355.
    18. 18)
      • 18. Gjelsvik, A., Belsnes, M.M., Haugstad, A.: ‘An algorithm for stochastic medium-term hydrothermal scheduling under spot price uncertainty’. Proc. 13th Power System Computation Conf., Trondheim, Norway, 1999.
    19. 19)
      • 22. Nandalal, K.D.W., Bogardi, J.J.: ‘Dynamic programming based operation of reservoirs, applicability and limits’ (Cambridge University Press, Cambridge, 2007).
    20. 20)
      • 13. Abgottspon, H., Njálsson, K., Bucher, M.A., et al: ‘Risk-averse medium-term hydro optimization considering provision of spinning reserves’. Int. Conf. on Probabilistic Methods Applied to Power Systems (PMAPS), Durham, England, 2014.
    21. 21)
      • 9. Pereira, M.V.F., Pinto, L.M.V.G.: ‘Multi-stage stochastic optimization applied to energy planning’, Math. Program., 1991, 52, pp. 359375.
    22. 22)
      • 12. Cerisola, S., Latorre, J.M., Ramos, A.: ‘Stochastic dual dynamic programming applied to nonconvex hydrothermal models’, Eur. J. Oper. Res., 2012, 218, pp. 687897.
    23. 23)
      • 2. Lindqvist, J.: ‘Operation of a hydrothermal electric system: a multistage decision process’, AIEE Trans.– pt. III (Power Apparatus and Systems), 1962, 81, (3), pp. 17.
    24. 24)
      • 25. Tejada-Guibert, J.A., Johnson, S., Stedinger, J.R.: ‘Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming’, Water Resour. Res., 1993, 29, (12), pp. 3693980.
    25. 25)
      • 3. Stage, S., Larsson, Y.: ‘Incremental cost of water power’, Trans. Am. Inst. Electr. Eng., 1961, 80, (3), pp. 361364.
    26. 26)
      • 8. Steeger, G., Barosso, L.A., Rebennack, S.: ‘Optimal bidding strategies for hydro-electric producers: a literature survey’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 17581766.
    27. 27)
      • 30. ‘IBM ILOG CPLEX optimizer’. Available at http://www-01.ibm.com/software/.
    28. 28)
      • 15. Löhndorf, N., Wozabal, D., Minner, S.: ‘Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming’, Oper. Res., 2013, 61, (4), pp. 810823.
    29. 29)
      • 6. Neto, T.A.A., Pereira, M.V.F., Kelman, J.: ‘A risk-constrained stochastic dynamic programming approach to the operation planning of hydrothermal systems’, IEEE Trans. Power Apar. Syst., 1985, PAS-104, (2), pp. 273279.
    30. 30)
      • 10. Flach, B., Barroso, L., Pereira, M.: ‘Long-term optimal allocation of hydro generation for a price-maker company in a competitive market: latest developments and a stochastic dual dynamic programming approach’, IET. Gener. Transm. Distrib., 2010, 4, (2), pp. 299314.
    31. 31)
      • 23. Rajaraman, R., Kirsch, L., Alvarado, F.L., et al: ‘The next generation of electric power unit commitment models’, vol. 36, ch. 6 (Kluwer Academic Publishers, Boston, 2001).
    32. 32)
      • 11. Goor, Q., Kelman, R., Tilmant, A.: ‘Optimal multipurpose-multireservoir operation model with variable productivity of hydropower plants’, J. Water Resour. Plan. Manage., 2011, 137, (3), pp. 258267.
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