Advertisement

Real-Time Optimal Control of River Basin Networks

  • R. Evans
  • L. Li
  • I. Mareels
  • N. Okello
  • M. Pham
  • W. Qiu
  • S. K. Saleem

Abstract

River basins are key components of water supply grids. River basin operators must handle a complex set of objectives including runoff storage, flood control, supply for consumptive use, hydroelectric power generation, silting management, and maintenance of river basin ecology. At present, operators rely on a combination of simulation and optimization tools to help make operational decisions. The complexity associated with this approach, however, makes it unsuitable for real-time (daily or hourly) operation. The consequence is that between longer-term optimized operating points, river basins are largely operated in open loop. This leads to operational inefficiencies most notably wasted water and poor ecological outcomes. This chapter proposes a systematic approach for the real-time operation of entire river basin networks, using optimal control and employing simple low order models.

Keywords

River Basin Model Predictive Control Linear Quadratic Regulator Storage Element Maximum Water Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Baume, J.P., Malaterre, P.O., Sau, J.: Tuning of PI controllers for an irrigation canal using optimization tools. In: Proceedings of USCID Workshop on Modernization of Irrigation Water Delivery Systems, Phoenix Arizona, USA (1999) Google Scholar
  2. 2.
    Blanco, T.B., Willems, P., De Moor, B., Berlamont, J.: Flood prevention of the demer using model predictive control. In: Proceedings of the 17th IFAC World Congress, pp. 3629–3934 (2008) Google Scholar
  3. 3.
    Bridgart, R.J., Bethune, M.: Development of RiverOperator: a tool to support operational management of river systems. In: 18th World IMACS/MODSIM Congress, pp. 3782–3788 (2009) Google Scholar
  4. 4.
    Chow, V.T.: Open-Channel Hydraulics. McGraw-Hill Book Company, New York (1988) Google Scholar
  5. 5.
    Georgakakos, A.P.: Decision support systems for integrated water resources management with an application to the Nile Basin. In: Proceedings IFAC Workshop on Modeling and Control for Participatory Planning and Managing Water Systems, Venice, Italy (2004) Google Scholar
  6. 6.
    Gilmore, R.L., Kuczera, G., Penton, D., Podger, G.: Improving the efficiency of delivering water in Australian river systems: modelling multiple paths. In: 18th World IMACS/MODSIM Congress, pp. 225–231 (2009) Google Scholar
  7. 7.
    Jakeman, A.J., Horngerger, G.M.: How much complexity is warranted in a Rainfall-Runoff model. Water Resour. Res. 29, 2637–2649 (1993) CrossRefGoogle Scholar
  8. 8.
    Labadie, J.W.: Optimal operation of multireservoir systems: state-of-the-art review. J. Water Resour. Plan. Manag. 130, 93–111 (2004) CrossRefGoogle Scholar
  9. 9.
    Litrico, X, Georges, D.: Robust continuous-time and discrete-time flow control of a dam-river system (I) modelling. Appl. Math. Model. 23, 809–827 (1999) MATHCrossRefGoogle Scholar
  10. 10.
    Litrico, X., Georges, D.: Robust continuous-time and discrete-time flow control of a dam-river system (II) controller design. Appl. Math. Model. 23, 829–846 (1999) MATHCrossRefGoogle Scholar
  11. 11.
    Litrico, X., Fromion, V.: H infinity control of an irrigation canal pool with a mixed control politics. IEEE Trans. Control Syst. Technol. 1(14), 99–111 (2006) CrossRefGoogle Scholar
  12. 12.
    Maciejowski, J.M.: Predictive Control with Constraints. Prentice Hall, London (2002) Google Scholar
  13. 13.
    Mareels, I., Weyer, E., Ooi, S.K., Cantoni, M., Yuping, Li, Nair, G.: Systems engineering for irrigation systems: Successes (2005) Google Scholar
  14. 14.
    Marinaki, M., Papageorgiou, M.: Central flow control in sewer networks. J. Water Resour. Plan. Manag. 123(5), 274–283 (1997) CrossRefGoogle Scholar
  15. 15.
    Marinaki, M., Papageorgiou, M.: Optimal Real-Time Control of Sewer Networks. Springer, London (2005) MATHGoogle Scholar
  16. 16.
    Marwali, M.N., Keyhani, A.: Control of distributed generation systems—Part I voltages and current control. IEEE Trans. Ind. Electron. 19(6), 1541–1550 (2004) Google Scholar
  17. 17.
    Maxwell, M., Warnick, S.: Modeling and identification of the Sevier River System. In: Americal Control Conference, pp. 5342–5347 (2006) Google Scholar
  18. 18.
    Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: stability and optimality. Automatica 36(6), 789–814 (2000) MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Murray-Darling Basin Water Resources Fact Sheet: Murray-Darling Basin Commission—July 2006. http://www2.mdbc.gov.au/. Cited July 27, 2010
  20. 20.
    Running the river: Murray-Darling Basin Commission. http://www2.mdbc.gov.au/. Cited July 27, 2010
  21. 21.
    Negenborn, R.R., Van Overloop, P.-J., Keviczky, T., De Schutter, B.: Distributed model predictive control of irrigation canals. Netw. Heterog. Media 4, 359–380 (2009) MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Overloop, P.J.: Model predictive control of open water systems. PhD thesis, Delft University of Technology, Delft, The Netherlands (2006) Google Scholar
  23. 23.
    Papageorgiou, M.: Optimal control of generalized flow networks. In: System Modelling and Optimization. Lecture Notes in Control and Information Sciences, vol. 59, pp. 373–382 (1984) CrossRefGoogle Scholar
  24. 24.
    Sage, A.P., White, C.C.: Optimum Systems Control. Prentice-Hall, Upper Saddle River (1977) MATHGoogle Scholar
  25. 25.
    Sawadogo, S., Malaterre, P.O., Kosuth, P.: Multivariable optimal control for on-demand operation of irrigation canals. Int. J. Syst. Sci. 1(26), 161–178 (1995) CrossRefGoogle Scholar
  26. 26.
    Setz, C., Heinrich, A., Rostalski, P., Papafotiou, G., Morari, M.: Application of model predictive control to a cascade of river power plants. In: Proceedings of the 17th World Congress IFAC, pp. 11978–11983 (2008) Google Scholar
  27. 27.
    Shamma, J.S., Athans, M.: Analysis of gain scheduled control for nonlinear plants. IEEE Trans. Autom. Control 35(8), 898–907 (1990) MathSciNetMATHCrossRefGoogle Scholar
  28. 28.
    Venkat, A.N., Hiskens, I.A., Rawlings, J.B., Wright, S.J.: Distributed MPC strategies with application to power system automatic generation control. IEEE Trans. Control Syst. Technol. 16(6), 1192–1206 (2008) CrossRefGoogle Scholar
  29. 29.
    Weyer, E.: System identification of an open water channel. Control Eng. Pract. 9(12), 1289–1299 (2001) CrossRefGoogle Scholar
  30. 30.
    Weyer, E.: Decentralized PI control of an open water channel. In: IFAC, 5th Triennial World Congress, Barcelona, Spain (2002) Google Scholar
  31. 31.
    Weyer, E.: LQ control of an irrigation channel. In: Proceedings of 42nd IEEE Conference on Decision and Control, vol. 1, pp. 750–755 (2003) Google Scholar
  32. 32.
    Winn, C.B., Moore, J.B.: The application of optimal linear regulator theory to a problem in water pollution. IEEE Trans. Syst. Man Cybern. 3, 450–455 (1973) CrossRefGoogle Scholar
  33. 33.
    Young, P.C.: Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environ. Model. Softw. 13, 105–122 (1998) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • R. Evans
    • 1
  • L. Li
    • 1
  • I. Mareels
    • 2
  • N. Okello
    • 1
  • M. Pham
    • 1
  • W. Qiu
    • 1
  • S. K. Saleem
    • 1
  1. 1.National ICT Australia LtdEveleighAustralia
  2. 2.The University of MelbourneMelbourneAustralia

Personalised recommendations