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Distributed zone MPC of pressure management for water distribution network systems

Distributed zone MPC of pressure management for water distribution network systems

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In the pressure management of large scale water distribution network (WDN), a distributed zone model predictive control (MPC) is proposed to keep the terminal water head in thedesired pressure range for satisfying the customer's demand, avoiding frequently operating of the actuator and reducing the correlation between subsystems. To ensure the existence of feasible solutions of constrained distributed zone MPC, a new reference trajectory of the tank level is introduced as an optimised variable. With the consideration of the desired pressure range constraints on the new reference trajectory and some physical constraints on the corresponding physical variables, a distributed zone MPC is presented to minimise the weighted sum of the three terms in the proposed performance index. To achieve the convergence of distributed zone MPC optimisation problem, an augmented Lagrangian formulation is applied to the distributed coordinated strategy. The proposed distributed zone MPC is applied to the WDN in the Shinan district of Shanghai, and the effectiveness of the method is illustrated.

References

    1. 1)
      • 1. Ocampomartinez, C., Puig, V., Cembrano, G., et al: ‘Improving water management efficiency by using optimization-based control strategies: the Barcelona case study’, Water Sci. Technol. Water Supply, 2009, 9, (9), pp. 565575.
    2. 2)
      • 2. Ulanicki, B., Bounds, P.L.M., Rance, J.P., et al: ‘Open and closed loop pressure control for leakage reduction’, Urban Water, 2000, 2, (2), pp. 105114.
    3. 3)
      • 3. Nicolini, M.: ‘Optimal pressure management in water networks: ‘increased efficiency and reduced energy costs’. 2011 Defense Science Research Conf. and Expo (DSR), Singapore, August 2011, pp. 14.
    4. 4)
      • 4. Negenborn, R.R., De Schutter, B., Hellendoorn, J.: ‘Multi-agent model predictive control for transportation networks: serial versus parallel schemes’, Eng. Appl. Artif. Intell., 2008, 21, (3), pp. 353366.
    5. 5)
      • 5. Ocampo-Martinez, C., Puig, V., Cembrano, G., et al: ‘Application of predictive control strategies to the management of complex networks in the urban water cycle [applications of control]’, IEEE Control Syst. Mag., 2013, 33, (1), pp. 1541.
    6. 6)
      • 6. García, L., Barreiro-Gomez, J., Escobar, E., et al: ‘Modeling and real-time control of urban drainage systems: a review’, Adv. Water Resour., 2015, 85, pp. 120132.
    7. 7)
      • 7. Zhou, L., Li, S.: ‘Distributed model predictive control for consensus of sampled-data multi-agent systems with double-integrator dynamics’, IET Control Theory Appl., 2015, 9, (12), pp. 17741780.
    8. 8)
      • 8. Negenborn, R.R., Maestre, J.M.: ‘Distributed model predictive control: an overview and roadmap of future research opportunities’, IEEE Control Syst. Mag., 2014, 34, (4), pp. 8797.
    9. 9)
      • 9. Li, S., Zhang, Y., Zhu, Q.: ‘Nash-optimization enhanced distributed model predictive control applied to the shell benchmark problem’, Inf. Sci., 2005, 170, (2), pp. 329349.
    10. 10)
      • 10. Zheng, Y., Li, S., Li, N.: ‘Distributed model predictive control over network information exchange for large-scale systems’, Control Eng. Pract., 2011, 19, (7), pp. 757769.
    11. 11)
      • 11. Maestre, J.M., Munoz De La Pena, D., Camacho, E.F.: ‘Distributed model predictive control based on a cooperative game’, Optim. Control Appl. Methods, 2011, 32, (2), pp. 153176.
    12. 12)
      • 12. Li, S., Zheng, Y., Lin, Z.: ‘Impacted-region optimization for distributed model predictive control systems with constraints’, IEEE Trans. Autom. Sci. Eng., 2015, 12, (4), pp. 14471460.
    13. 13)
      • 13. Leirens, S., Zamora, C., Negenborn, R.R., et al: ‘Coordination in urban water supply networks using distributed model predictive control’. Proc. 2010 American Control Conf., Baltimore, Maryland, USA, July 2010, pp. 39573962.
    14. 14)
      • 14. Maciejowski, J.M.: ‘Predictive control: with constraints’ (Pearson Education, Harlow, 2002).
    15. 15)
      • 15. González, A.H., Marchetti, J.L., Odloak, D.: ‘Robust model predictive control with zone control’, IET Control Theory Appl., 2009, 3, (1), pp. 121135.
    16. 16)
      • 16. Qin, S.J., Badgwell, T.A.: ‘A survey of industrial model predictive control technology’, Control Eng. Pract., 2003, 11, (7), pp. 733764.
    17. 17)
      • 17. Zanin, A.C., De Gouvea, M.T., Odloak, D.: ‘Integrating real-time optimization into the model predictive controller of the FCC system’, Control Eng. Pract., 2002, 10, (8), pp. 819831.
    18. 18)
      • 18. Gonzalez, A.H., Odloak, D.: ‘A stable MPC with zone control’, J. Process Control, 2009, 19, (1), pp. 110122.
    19. 19)
      • 19. González, A.H., Marchetti, J.L., Odloak, D.: ‘Robust model predictive control with zone control’, IET Control Theory Appl., 2009, 3, (1), pp. 121135.
    20. 20)
      • 20. Liu, D.M., Li, S.Y.: ‘Predictive zone control of pressure management for water supply network systems’, Int. J. Autom. Comput., 2016, 13, (6), pp. 607614.
    21. 21)
      • 21. Liu, D., Wu, J., Li, S.: ‘Wiener model of pressure management for water distribution network’, Int. J. Modell. Identif. Control, 2018, 30, (2), pp. 7382.
    22. 22)
      • 22. Śliwiński, P., Marconato, A., Wachel, P., et al: ‘Non-linear system modelling based on constrained Volterra series estimates’, IET Control Theory Applic., 2017, 11, (15), pp. 26232629.
    23. 23)
      • 23. Brdys, M.A.: ‘Operational control of water systems: structures, algorithms, and applications’ (Prentice-Hall, Upper Saddle River, 1994).
    24. 24)
      • 24. Kim, B.H., Baldick, R.: ‘Coarse-grained distributed optimal power flow’, IEEE Trans. Power Syst., 1997, 12, (2), pp. 932939.
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