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A convex mathematical program for pump scheduling in a class of branched water networks

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  • Bonvin, Gratien
  • Demassey, Sophie
  • Le Pape, Claude
  • Maïzi, Nadia
  • Mazauric, Vincent
  • Samperio, Alfredo

Abstract

We address the day-ahead pump scheduling problem for a class of branched water networks with one pumping station raising water to tanks at different places and levels. This common class is representative of rural drinking water distribution networks, though not exclusively so. Many sophisticated heuristic algorithms have been designed to tackle the challenging general problem. By focusing on a class of networks, we show that a pure model-based approach relying on a tractable mathematical program is pertinent for real-size applications. The practical advantages of this approach are that it produces optimal or near-optimal solutions with performance guarantees in near real-time, and that it is replicable without algorithmic development. We apply the approach to a real drinking water supply system and compare it to the current operational strategy based on historical data. An extensive empirical analysis assesses the financial and practical benefits: (1) it achieves significant savings in terms of operation costs and energy consumption, (2) its robustness to dynamic pricing means that demand-response can be efficiently implemented in this type of energy-intensive utility.

Suggested Citation

  • Bonvin, Gratien & Demassey, Sophie & Le Pape, Claude & Maïzi, Nadia & Mazauric, Vincent & Samperio, Alfredo, 2017. "A convex mathematical program for pump scheduling in a class of branched water networks," Applied Energy, Elsevier, vol. 185(P2), pages 1702-1711.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1702-1711
    DOI: 10.1016/j.apenergy.2015.12.090
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    References listed on IDEAS

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    1. Ghaddar, Bissan & Naoum-Sawaya, Joe & Kishimoto, Akihiro & Taheri, Nicole & Eck, Bradley, 2015. "A Lagrangian decomposition approach for the pump scheduling problem in water networks," European Journal of Operational Research, Elsevier, vol. 241(2), pages 490-501.
    2. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
    3. D’Ambrosio, Claudia & Lodi, Andrea & Wiese, Sven & Bragalli, Cristiana, 2015. "Mathematical programming techniques in water network optimization," European Journal of Operational Research, Elsevier, vol. 243(3), pages 774-788.
    4. van Staden, Adam Jacobus & Zhang, Jiangfeng & Xia, Xiaohua, 2011. "A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges," Applied Energy, Elsevier, vol. 88(12), pages 4785-4794.
    5. Naoum-Sawaya, Joe & Ghaddar, Bissan & Arandia, Ernesto & Eck, Bradley, 2015. "Simulation-optimization approaches for water pump scheduling and pipe replacement problems," European Journal of Operational Research, Elsevier, vol. 246(1), pages 293-306.
    6. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    7. Björn Geißler & Oliver Kolb & Jens Lang & Günter Leugering & Alexander Martin & Antonio Morsi, 2011. "Mixed integer linear models for the optimization of dynamical transport networks," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 73(3), pages 339-362, June.
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    Cited by:

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    2. Liu, Mingzhe & Ooka, Ryozo & Choi, Wonjun & Ikeda, Shintaro, 2019. "Experimental and numerical investigation of energy saving potential of centralized and decentralized pumping systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Gao, Wei & Feng, Xiao, 2017. "The power target of a fluid machinery network in a circulating water system," Applied Energy, Elsevier, vol. 205(C), pages 847-854.
    4. Bohong Wang & Yongtu Liang & Wei Zhao & Yun Shen & Meng Yuan & Zhimin Li & Jian Guo, 2021. "A Continuous Pump Location Optimization Method for Water Pipe Network Design," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 447-464, January.
    5. Ma, Jiaze & Wang, Yufei & Feng, Xiao, 2018. "Optimization of multi-plants cooling water system," Energy, Elsevier, vol. 150(C), pages 797-815.
    6. Xuetao Wang & Qianchuan Zhao & Yifan Wang, 2020. "A Distributed Optimization Method for Energy Saving of Parallel-Connected Pumps in HVAC Systems," Energies, MDPI, vol. 13(15), pages 1-24, July.

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