TECHNICAL PAPERS
Aug 7, 2010

Optimal Multipurpose-Multireservoir Operation Model with Variable Productivity of Hydropower Plants

Publication: Journal of Water Resources Planning and Management
Volume 137, Issue 3

Abstract

Stochastic dual dynamic programming (SDDP) is one of the few methods available to solve multipurpose-multireservoir operation problems in a stochastic environment. This algorithm requires that the one-stage optimization problem be a convex program so that the efficient Benders decomposition scheme can be implemented to handle the large state-space that characterizes multireservoir operation problems. When working with hydropower systems, one usually assumes that the production of hydroelectricity is dominated by the release term and not by the head (storage) term to circumvent the nonlinearity of the hydropower production function. Although this approximation is satisfactory for high head power stations for which the difference between the maximum and the minimum head is small compared to the maximum head, it may no longer be acceptable when a significant portion of the energy originates from low and/or medium head power plants. Recent developments improve the representation of the nonlinear hydropower function through a convex hull approximation of the true hydropower function. A network of hydropower plants and irrigated areas in the Nile Basin is used to illustrate the difference between the two SDDP formulations on the energy generation and the allocation decisions.

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References

Allen, R., Pereira, L., Raes, D., and Smith, M. (1998). “Guidelines for computing crop water requirements.” Irrigation and Drainage Paper 56, United Nations Food and Agriculture Organization, Rome.
Archibald, T., Buchanan, C. S., McKinnon, K. I. M., and Thomas, L. C. (1999). “Nested benders decomposition and dynamic programming for reservoir optimization.” J. Oper. Res. Soc., 50(5), 468–479.
Archibald, T. W., McKinnon, K. I. M., and Thomas, L. C. (1997). “An aggregate stochastic dynamic programming model of multireservoir systems.” Water Resour. Res., 33(2), 333–340.
Barber, C. B., Dobkin, D. P., and Huhdanpaa, H. T. (1996). “The quickhull algorithm for convex hulls.” ACM Trans. Math. Software, 22(4), 469–483.
Bortolossi, H., Pereira, M., and Tomei, C. (2002). “Optimal hydrothermal scheduling with variable production coefficient.” Math. Methods Oper. Res., 55(1), 11–36.
Cai, X. M., McKinney, D. C., Lasdon, L. S., and Watkins, D. W. (2001). “Solving large nonconvex water resources management models using generalized benders decomposition.” Oper. Res., 49(2), 235–245.
Castelletti, A., de Rigo, D., Rizzoli, A., Soncini-Sessa, R., and Weber, E. (2007). “Neuro-dynamic programming for designing water reservoir network management policies.” Control Eng. Pract., 15(8), 1031–1038.
Cervellera, C., Chen, V. C. P., and Wen, A. (2006). “Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization.” Eur. J. Oper. Res., 171(3), 1139–1151.
Cunha, S., Prado, S., and da Costa, J. (1997). “Modelagem da produtividade variável de usinas hidrelétricas com base na construção de uma função de produção energética.” Proc., XII Simpósio Brasileiro de Recursos Hídricos, ABRH, anais 2, 391–397, Brazil, 16–20 (in Portuguese).
Haynes, K. E., and Whittington, D. (1981). “International management of the Nile: Stage three?” Geogr. Rev., 71(1), 17–32.
Huang, W.-C., Harboe, R., and Bogardi, J. J. (1991). “Testing stochastic dynamic programming models conditioned on observed or forecasted inflows.” J. Water Resour. Plann. Manage., 117(1), 28–36.
Johnson, S. A., Stedinger, J. R., Shoemaker, C. A., Li, Y., and Tejada-Guibert, J. A. (1993). “Numerical solution of continuous-state dynamic programs using linear and spline interpolation.” Oper. Res., 41(3), 484–500.
Kall, P., and Wallace, S. W. (1994). Stochastic programming, Wiley, New York.
Karamouz, M., Szidarovszky, F., and Zahraie, B. (2003). Water Resources Systems Analysis, Lewis Publishers, Boca Raton, FL.
Karamouz, M., and Vasiliadis, H. V. (1992). “Bayesian stochastic optimization of reservoir operation using uncertain forecasts.” Water Resour. Res., 28(5), 1221–1232.
Kristiansen, T. (2004). “Financial risk management in the electric power industry using stochastic optimization.” Adv. Model. Optim., 6(2), 17–24.
Labadie, J. (2004). “Optimal operation of multireservoir system: State-of-the-art review.” J. Water Resour. Plann. Manage., 130(2), 93–111.
Lee, J.-H., and Labadie, J. W. (2007). “Stochastic optimization of multireservoir systems via reinforcement learning.” Water Resour. Res., 43, W11408.
Loucks, D., and van Beek, E. (2005). Water resources systems planning and management: An introduction to methods, models and applications, J. Stedinger, J. Dijkman, and M. Villars, eds., UNESCO Publishing/WL-Delft Hydraulics, Paris.
Mo, B., Gjelsvik, A., and Grundt, A. (2001). “Integrated risk management of hydro power scheduling and contract management.” IEEE Trans. Power Syst., 16(2), 216–221.
Oven-Thompson, K., Alercon, L., and Marks, D. H. (1982). “Agricultural vs. hydropower tradeoffs in the operation of the High Aswan Dam.” Water Resour. Res., 18(6), 1605–1613.
Pereira, M. (1989.) “Optimal stochastic operations scheduling of large hydroelectric systems.” Int. J. Electr. Power Energy Syst., 11(3), 161–169.
Pereira, M., and Pinto, L. (1983). “Application of decomposition techniques to the mid- and short-term scheduling of hydrothermal systems.” IEEE Trans. Power Appar. Syst., PAS-102(11), 3611–3618.
Pereira, M. V. F., and Pinto, L. M. V. G. (1985). “Stochastic optimization of a multireservoir hydroelectric system: A decomposition approach.” Water Resour. Res., 21(6), 779–792.
Pereira, M., and Pinto, L. (1991). “Multi-stage stochastic optimization applied to energy planning.” Math. Program., 52(1–3), 359–375.
Pereira, M., Campodonico, N., and Kelman, R. (1998). “Long-term hydro scheduling based on stochastic models.” Proc., EPSOM Conf., Centro Empresarial Rio, Rio de Janeiro, Brazil.
Philbrick, R., and Kitanidis, P. K. (1999). “Limitations of deterministic optimization applied to reservoir operations.” J. Water Resour. Plann. Manage., 125(3), 135–142.
Read, E. (1989). “A dual approach to stochastic dynamic programming for reservoir release scheduling.” Dynamic programming for optimal water resources system analysis, A. Esoqbue, ed., Prentice Hall, New York, 361–372.
Rotting, T., and Gjelsvik, A. (1992). “Stochastic dual dynamic-programming for seasonal scheduling in the Norwegian power-system.” IEEE Trans. Power Syst., 7(1), 273–279.
Saad, M., Turgeon, A., Bigras, P., and Duquette, R. (1994). “Learning disaggregation technique for the operation of long-term hydroelectric power systems.” Water Resour. Res., 30(11), 3195–3202.
Scott, T. J., and Read, E. G. (1996). “Modelling hydro reservoir operation in a deregulated electricity sector.” Int. Trans. Oper. Res., 3(3–4), 243–253.
Stedinger, J., Sule, B., and Loucks, D. (1984). “Stochastic dynamic programming models for reservoir operation optimization.” Water Resour. Res., 20(11), 1499–1505.
Tejada-Guibert, J. A., Johnson, S. A., and Stedinger, J. R. (1993). “Comparaison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming.” Water Resour. Res., 29(12), 3969–3980.
Tejada-Guibert, J. A., Johnson, S. A., and Stedinger, J. R. (1995). “The value of hydrologic information in stochastic dynamic programming models of a multireservoir system.” Water Resour. Res., 31(10), 2571–2579.
Tilmant, A., Goor, Q., and Pinte, D. (2008). “Assessing marginal water values in multipurpose multireservoir systems via stochastic programming.” Water Resour. Res., 44, W12431.
Tilmant, A., and Kelman, R. (2007). “A stochastic approach for analyzing trade-offs and risks associated with large-scale water resources systems.” Water Resour. Res., 43, W06425.
Wallace, S., and Fleten, S. (2003). “Stochastic programming models in energy.” Chapter 10, Handbooks in operations research and management science, Vol. 10, A. Ruszczynski and A. Shapiro, eds. Elsevier Science, Amsterdam, The Netherlands.
Watkins, D. W., and McKinney, D. C. (1998). “Decomposition methods for water resources optimization models with fixed costs.” Adv. Water Resour., 21(4), 283–295.
Whittington, D., Wu, X., and Sadoff, C. (2005). “Water resources management in the Nile Basin: The economic value of cooperation.” Water Policy, 7(3), 227–252.
Wood, A. J., and Wollenberg, B. F. (1996). Power generation, operation, and control, 2nd Ed., Wiley-Interscience, Hoboken, NJ.
World Bank. (2006). “Ethiopia: Managing water resources to maximize sustainable growth.” Rep. No. 36000-ET, World Bank Agriculture and Rural Development Dept., Washington, DC.
Wu, X., and Whittington, D. (2006). “Incentive compatibility and conflict resolution in international river basins: A case study of the Nile Basin.” Water Resour. Res., 42, W02417.
Yeh, W.-G. (1985). “Reservoir management and operation models : A state-of-the-art review.” Water Resour. Res., 21(12), 1797–1818.

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Published In

Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 137Issue 3May 2011
Pages: 258 - 267

History

Received: Oct 19, 2009
Accepted: Aug 5, 2010
Published online: Aug 7, 2010
Published in print: May 1, 2011

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Authors

Affiliations

Ph.D. Researcher, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium (corresponding author). E-mail: [email protected]
R. Kelman
Director, Power Systems Research, Rio de Janeiro, Brazil.
A. Tilmant
Senior Lecturer, Dept. of Management and Institutions, UNESCO-IHE, Delft, The Netherlands, and Institute of Environmental Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland.

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