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Digital Twin-Based Pump Station Dynamic Scheduling for Energy-Saving Optimization in Water Supply System

Author

Listed:
  • Sheng-Wen Zhou

    (Wuhan University of Technology
    Wuhan University of Technology)

  • Shun-Sheng Guo

    (Wuhan University of Technology
    Wuhan University of Technology)

  • Wen-Xiang Xu

    (Hubei University of Arts and Science
    Hubei University of Arts and Science)

  • Bai-Gang Du

    (Wuhan University of Technology
    Wuhan University of Technology)

  • Jun-Yong Liang

    (Sichuan University of Science and Engineering)

  • Lei Wang

    (Wuhan University of Technology
    Wuhan University of Technology)

  • Yi-Bing Li

    (Wuhan University of Technology
    Wuhan University of Technology)

Abstract

In urban water supply systems, pump stations are the hubs for making the complete systems operate regularly as well as the main energy-consuming units. In order to address the current problems of water supply systems, such as high energy consumption and low efficiency of the pump station operation, and poor response and adaptability to disturbance events, a digital twin (DT)-based full-process dynamic pump station scheduling method for energy-saving optimization in water treatment plants was proposed in this study. To be specific, the DT technology was introduced to predict the availability status of the pump unit in advance, trigger the rescheduling process in time, and achieve energy conservation and consumption reduction, so as to provide technical and methodological support for unattended pump stations. The results of experiments revealed that an average energy-saving rate of 9.78% could be achieved by using the proposed method on the premise of ensuring the full-process dynamic water balance. In addition, the method could maintain high efficiency during the operation of the pumps, and guarantee the safety and stability of the pump stations.

Suggested Citation

  • Sheng-Wen Zhou & Shun-Sheng Guo & Wen-Xiang Xu & Bai-Gang Du & Jun-Yong Liang & Lei Wang & Yi-Bing Li, 2024. "Digital Twin-Based Pump Station Dynamic Scheduling for Energy-Saving Optimization in Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(8), pages 2773-2789, June.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:8:d:10.1007_s11269-024-03791-2
    DOI: 10.1007/s11269-024-03791-2
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    References listed on IDEAS

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    1. Reis, Ana L. & Lopes, Marta A.R. & Andrade-Campos, A. & Henggeler Antunes, Carlos, 2023. "A review of operational control strategies in water supply systems for energy and cost efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    2. Yasaman Makaremi & Ali Haghighi & Hamid Reza Ghafouri, 2017. "Optimization of Pump Scheduling Program in Water Supply Systems Using a Self-Adaptive NSGA-II; a Review of Theory to Real Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1283-1304, March.
    3. Shengwen Zhou & Shunsheng Guo & Baigang Du & Shuo Huang & Jun Guo, 2022. "A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network," Sustainability, MDPI, vol. 14(17), pages 1-22, September.
    4. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
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    6. Luca O. Turci & Jingcheng Wang & Ibrahim Brahmia, 2020. "Adaptive and Improved Multi-population Based Nature-inspired Optimization Algorithms for Water Pump Station Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2869-2885, July.
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