A Practical Optimization Scheme for Real-Time Operation of Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 4
Abstract
Pump scheduling is a key element in water distribution systems operation. Modeling this problem requires a mixed integer nonlinear program (MINLP) formulation. Even linearization schemes of mixed integer linear programs (MILPs) are typically beyond the capability of real-time optimization frameworks. In this study, we explore different levels of MILP approximations by reducing the number of binary decision variables (i.e., different binarization levels). In addition, we present a simple demand forecast model and evaluate the performance and approximation accuracy of the suggested approach in a real-time optimization framework under a receding horizon operation mode. The results show that the balance between approximation accuracy and solution efficiency is biased. That is, a simple low-accuracy approximation may yield an efficient and practical solution algorithm that results in a near-optimal solution.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This study was supported by the Israeli Water Authority (Contract number 4501284516).
References
Alvisi, S., M. Franchini, and A. Marinelli. 2007. “A short-term, pattern-based model for water-demand forecasting.” J. Hydroinf. 9 (1): 39. https://doi.org/10.2166/hydro.2006.016.
Biscos, C., M. Mulholland, M. V. Le Lann, C. A. Buckley, and C. J. Brouckaert. 2003. “Optimal operation of water distribution networks by predictive control using MINLP.” Water SA 29 (4): 393–404. https://doi.org/10.4314/wsa.v29i4.5044.
Copeland, C., and N. T. Carter. 2017. Energy–water nexus: The water sector’s energy use R43200. Washington, DC: Congressional Research Service.
Coulbeck, B., and C. H. Orr. 1989. “Real-time optimized control of a water distribution system.” IFAC Proc. 22 (6): 441–446. https://doi.org/10.1016/B978-0-08-037034-7.50080-8.
Creaco, E., A. Campisano, N. Fontana, G. Marini, P. R. Page, and T. Walski. 2019. “Real time control of water distribution networks: A state-of-the-art review.” Water Res. 161 (Sep): 517–530. https://doi.org/10.1016/j.watres.2019.06.025.
Donkor, E. A., T. A. Mazzuchi, R. Soyer, and J. Alan Roberson. 2014. “Urban water demand forecasting: review of methods and models.” J. Water Resour. Plann. Manage. 140 (2): 146–159. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000314.
Ellis, M., J. Liu, and P. D. Christofides. 2017. “Economic model predictive control.” In Vol. 2 of Encyclopedia of systems and control, 1420–1430. Oxford, UK: Pergamon Press.
Forrest, J., and R. Lougee-Heimer. 2005. “CBC user guide.” In Emerging theory, methods, and applications, 257–277. Aurora, CO: INFORMS.
Galuppini, G., E. Creaco, C. Toffanin, and L. Magni. 2019. “Service pressure regulation in water distribution networks.” Control Eng. Pract. 86 (May): 70–84. https://doi.org/10.1016/j.conengprac.2019.03.007.
Goldstein, R., and W. Smith. 2002. Water and sustainability (volume 4): US electricity consumption for water supply and treatment–The next half century. Concord, CA: Electric Power Research Institute (EPRI).
Grosso, J. M., C. Ocampo-Martínez, V. Puig, and B. Joseph. 2014. “Chance-constrained model predictive control for drinking water networks.” J. Process Control 24 (5): 504–516. https://doi.org/10.1016/j.jprocont.2014.01.010.
Herrera, M., L. Torgo, J. Izquierdo, and R. Pérez-García. 2010. “Predictive models for forecasting hourly urban water demand.” J. Hydrol. 387 (1–2): 141–150. https://doi.org/10.1016/j.jhydrol.2010.04.005.
Housh, M., and E. Salomons. 2019. “Optimal dynamic pump triggers for cost saving and robust water distribution system operations.” J. Water Resour. Plann. Manage. 145 (2): 1–9. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001028.
IBM (International Business Machines). 2009. “V12. 1: User’s manual for CPLEX.” Int. Bus. Mach. Corporation 46 (53): 157.
Jamieson, D. G., U. Shamir, F. Martinez, and M. Franchini. 2007. “Conceptual design of a generic, real-time, near-optimal control system for water-distribution networks.” J. Hydroinf. 9 (1): 3. https://doi.org/10.2166/hydro.2006.013.
Jamshidnejad, A., I. Papamichail, M. Papageorgiou, and B. De Schutter. 2016. “A model-predictive urban traffic control approach with a modified flow model and endpoint penalties.” IFAC-PapersOnLine 49 (3): 147–152. https://doi.org/10.1016/j.ifacol.2016.07.025.
Jowitt, P. W., and G. Germanopoulos. 1992. “Optimal pump scheduling in water-supply networks.” J. Water Resour. Plann. Manage. 118 (4): 406–422. https://doi.org/10.1061/(ASCE)0733-9496(1992)118:4(406).
Lofberg, J. 2004. “YALMIP : A toolbox for modeling and optimization in MATLAB.” In Proc., 2004 IEEE Int. Conf. on Robotics and Automation (IEEE Cat. No.04CH37508), 284–289. Piscataway, NJ: IEEE. https://ieeexplore.ieee.org/document/1393890. https://doi.org/10.1109/CACSD.2004.1393890.
Lougee-Heimer, R. 2003. “The common optimization interface for operations research: Promoting open-source software in the operations research community.” IBM J. Res. Dev. 47 (1): 57–66. https://doi.org/10.1147/rd.471.0057.
Mala-Jetmarova, H., N. Sultanova, and D. Savic. 2017. “Lost in optimisation of water distribution systems? A literature review of system operation.” Environ. Modell. Software 93 (Jul): 209–254. https://doi.org/10.1016/j.envsoft.2017.02.009.
Ocampo-Martinez, C., D. Barcelli, V. Puig, and A. Bemporad. 2012. “Hierarchical and decentralised model predictive control of drinking water networks: Application to Barcelona case study.” IET Control Theory Appl. 6 (1): 62. https://doi.org/10.1049/iet-cta.2010.0737.
Ormsbee, L. E., and K. E. Lansey. 1994. “Optimal control of water supply pumping systems.” J. Water Resour. Plann. Manage. 120 (2): 237–252. https://doi.org/10.1061/(ASCE)0733-9496(1994)120:2(237).
Ostfeld, A., et al. 2012. “Battle of the water calibration networks.” J. Water Resour. Plann. Manage. 138 (5): 523–532. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000191.
Pacchin, E., S. Alvisi, and M. Franchini. 2017. “A short-term water demand forecasting model using a moving window on previously observed data.” Water 9 (3): 172. https://doi.org/10.3390/w9030172.
Rao, Z., and E. Salomons. 2007. “Development of a real-time, near-optimal control process for water-distribution networks.” J. Hydroinf. 9 (1): 25. https://doi.org/10.2166/hydro.2006.015.
Sanders, K. T., and M. E. Webber. 2012. “Evaluating the energy consumed for water use in the United States.” Environ. Res. Lett. 7 (3): 034034.
Selek, I., J. G. Bene, and C. Hs. 2012. “Optimal (short-term) pump schedule detection for water distribution systems by neutral evolutionary search.” Appl. Soft Comput. J. 12 (8): 2336–2351. https://doi.org/10.1016/j.asoc.2012.03.045.
Shamir, U., and E. Salomons. 2008. “Optimal real-time operation of urban water distribution systems using reduced models.” J. Water Resour. Plann. Manage. 134 (2): 181–185. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(181).
Vigerske, S., W. Huang, H. Held, and A. Gleixner. 2012. “Towards globally optimal operation of water supply networks.” Numer. Algebra, Control Optim. 2 (4): 695–711. https://doi.org/10.3934/naco.2012.2.695.
Zhou, S. L., T. A. McMahon, A. Walton, and J. Lewis. 2002. “Forecasting operational demand for an urban water supply zone.” J. Hydrol. 259 (1–4): 189–202. https://doi.org/10.1016/S0022-1694(01)00582-0.
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Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 4 • April 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: May 9, 2019
Accepted: Oct 2, 2019
Published online: Feb 13, 2020
Published in print: Apr 1, 2020
Discussion open until: Jul 13, 2020
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