© The Institution of Engineering and Technology
The authors describe a method for long-term hydro-thermal scheduling allowing treatment of detailed large-scale hydro systems. Decisions for each week are determined by solving a two-stage stochastic linear programming problem considering uncertainty in weather and exogenous market prices. The overall scheduling problem is solved by embedding such two-stage problems in a rolling horizon simulator. The method is verified on data for the Nordic power system, studying the incremental changes in expected socio-economic surplus for expansions in both the transmission and generation systems. Comparisons are made with a widely used existing long-term hydro-thermal scheduling model. The results indicate that the model is well suited to valuate the flexibility of hydropower in systems with a high share of intermittent renewable generation.
References
-
-
1)
-
34. Warland, G., Haugstad, A., Huse, E.S.: ‘Including thermal unit start-up costs in a long-term hydro-thermal scheduling model’. Proc. 16th Power System Computation Conf., Glasgow, Scotland, 2008.
-
2)
-
7. Pereira, M.V.F., Pinto, L.M.V.G.: ‘Multi-stage stochastic optimization applied to energy planning’, Math. Program., 1991, 52, pp. 359–375.
-
3)
-
20. Rebennack, S.: ‘Combining sampling-based and scenario-based nested benders decomposition methods: application to stochastic dual dynamic programming’, Math. Program., 2016, 156, (1), pp. 343–389.
-
4)
-
14. 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.
-
5)
-
2. Maceira, M.E.P., Duarte, V.S., Penna, D.D.J., et al: ‘Ten years of application of stochastic dual dynamic programming in official and agent studies in Brazil - description of the Newave program’. Proc. 16th Power System Computation Conf., Glasgow, 2008.
-
6)
-
25. Martinez, L., Soares, S.: ‘Comparison between closed-loop and partial open-loop feedback control policies in long term hydrothermal scheduling’, IEEE Trans. Power Syst., 2002, 17, pp. 330–336.
-
7)
-
33. Diniz, A., Maceira, M.E.P.: ‘A four-dimensional model of hydro generation for the short-term hydrothermal dispatch problem considering head and spillage effects’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 1298–1308.
-
8)
-
9. Halliburton, T.S.: ‘An optimal hydrothermal planning model for the New Zealand power system’, Aust. J. Electr. Electron. Eng., 2004, 1, (3), pp. 193–198.
-
9)
-
12. 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. 299–314.
-
10)
-
18. Philpott, A., de Matos, V.: ‘Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion’, Eur. J. Oper. Res., 2012, 218, (2), pp. 470–483.
-
11)
-
30. Warland, G., Mo, B.: ‘Stochastic optimization model for detailed long-term hydro thermal scheduling using scenario-tree simulation’, Energy Procedia, 2016, 87, pp. 165–172.
-
12)
-
13. Cerisola, S., Latorre, J.M., Ramos, A.: ‘Stochastic dual dynamic programming applied to nonconvex hydrothermal models’, Eur. J. Oper. Res., 2012, 218, pp. 687–897.
-
13)
-
32. Van Slyke, R.M., Wets, R.: ‘L-shaped linear programs with applications to optimal control and stochastic programming’, SIAM J. Appl. Math., 1969, 17, (4), pp. 638–663.
-
14)
-
4. Turgeon, A., Charbonneau, R.: ‘An aggregation-disaggregation approach to long-term reservoir management’, Water Resour. Res., 1998, 34, (12), pp. 3585–3594.
-
15)
-
5. Wolfgang, O., Haugstad, A., Mo, B., et al: ‘Hydro reservoir handling in Norway before and after deregulation’, Energy, 2009, 34, (10), pp. 1642–1651.
-
16)
-
35. Helseth, A., Warland, G., Mo, B.: ‘A hydrothermal market model for simulation of area prices including detailed network analyses’, Int. Trans. Electr. Energy Syst., 2013, 23, (8), pp. 1396–1408.
-
17)
-
24. Séguin, S., Fleten, S.E., Côté, P., et al: ‘Stochastic short-term hydropower planning with inflow scenario trees’, Eur. J. Oper. Res., 2016, 259, pp. 1156–1168.
-
18)
-
6. Maceira, M.E.P., Duarte, V.S., Penna, D.D.J., et al: ‘An approach to consider hydraulic coupled systems in the construction of equivalent reservoir model in hydrothermal operation planning’. Power System Computation Conf. (PSCC), Stockholm, Sweden, 2011.
-
19)
-
15. 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. 669–686.
-
20)
-
17. Poorsepahy-Samian, H., Espanmanesh, V., Zahraie, B.: ‘Improved inflow modeling in stochastic dual dynamic programming’, J. Water Resour. Plan. Manage., 2016, 142, (12).
-
21)
-
8. 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 Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A. (Eds.): Handbook of power systems I (Springer-Verlag, Berlin and Heidelberg, 2010), pp. 33–55.
-
22)
-
23)
-
21. Kelman, J., Stedinger, J.R., Cooper, L.A., et al: ‘Sampling stochastic dynamic programming applied to reservoir operation’, Water Resour. Res., 1990, 26, (3), pp. 447–454.
-
24)
-
27. Nolde, K., Uhr, M., Morari, M.: ‘Medium term scheduling of a hydro-thermal system using stochastic model predictive control’, Automatica, 2008, 44, pp. 1585–1594.
-
25)
-
3. Valdés, J. B., Filippo, J.M., Strzepek, K.M., et al: ‘Aggregation–disaggregation approach to multireservoir operation’, J. Water Resour. Plan. Manage., 1992, 118, (4), pp. 423–444.
-
26)
-
31. Gröwe-Kuska, N., Heitsch, H., Römisch, W.: ‘Scenario reduction and scenario tree construction for power management problems’. IEEE PowerTech Conf., Bologna, Italy, 2003.
-
27)
-
11. Gjerden, K.S., Helseth, A., Mo, B., et al: ‘Hydrothermal scheduling in Norway using stochastic dual dynamic programming: a large-scale case study’. Proc. of IEEE PowerTech, Eindhoven, The Netherlands, 2015.
-
28)
-
23. Aasgård, E.K., Andersen, G.S., Fleten, S.E., et al: ‘Evaluating a stochastic-programming-based bidding model for a multireservoir system’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 1748–1757.
-
29)
-
16. Penna, D.D.J., Dámazio, M.E.P.M.J.M.: ‘Selective sampling applied to long-term hydrothermal generation planning’. Power System Computation Conf. (PSCC), Stockholm, Sweden, 2011.
-
30)
-
22. Scharff, R., Egerer, J., Söder, L.: ‘A description of the operative decision-making process of a power generating company on the Nordic electricity market’, Energy Syst., 2014, 5, pp. 349–369.
-
31)
-
26. Zambelli, M.S., Soares, S.: ‘A predictive control approach for long term hydrothermal scheduling’. IEEE/PES Power Systems Conf. and Exposition, 2009.
-
32)
-
19. 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. 1426–1434.
-
33)
-
1. Fosso, O.B., Gjelsvik, A., Haugstad, A., et al: ‘Generation scheduling in a deregulated system. The Norwegian case’, IEEE Trans. Power Syst., 1999, 14, pp. 75–81.
-
34)
-
28. Powell, W.B., George, A., Simão, H., et al: ‘SMART: a stochastic multiscale model for the analysis of energy resources, technology, and policy’, INFORMS J. Comput., 2011, 24, (4), pp. 665–682.
-
35)
-
29. Helseth, A., Mo, B., Warland, G.: ‘Long-term scheduling of hydro-thermal power systems using scenario fans’, Energy Syst., 2010, 1, (4), pp. 377–391.
-
36)
-
10. Granville, S., Oliveira, G.C., Thomé, L.M., et al: ‘Stochastic optimization of transmission constrained and large scale hydrothermal systems in a competitive framework’. Proc. IEEE General Meeting, Toronto, Canada, 2003.
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