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Critical Elements Analysis of Water Supply Systems to Improve Energy Efficiency in Failure Scenarios

Author

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  • Gal Perelman

    (Technion – Israel Institute of Technology)

  • Barak Fishbain

    (Technion – Israel Institute of Technology)

Abstract

Water supply systems (WSS) are significant energy consumers, which makes it crucial to find methods to increase energy efficiency. A novel approach is presented here to locate and quantify the most vulnerable elements in terms of energetic efficiency. The method is based on a linear programming (LP) optimization model and the elimination of different components in the system to analyze their impact on energy costs. Pump reallocation is then suggested as a novel dynamic design paradigm for temporary changes in the system to cope with extreme scenarios. Exposing the critical components of WSS improves maintenance prioritization and inventory and contributes to the planning of future investments in the system. Pump reallocation provides an innovative approach for response to critical conditions, it is suggesting rethinking the design concepts to incorporate not just long-term solutions but also rapid, temporary steps in response to failures.

Suggested Citation

  • Gal Perelman & Barak Fishbain, 2022. "Critical Elements Analysis of Water Supply Systems to Improve Energy Efficiency in Failure Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3797-3811, August.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03232-y
    DOI: 10.1007/s11269-022-03232-y
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    References listed on IDEAS

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    1. Mehdi Dini & Mozhdeh Hemmati & Saeed Hashemi, 2022. "Optimal Operational Scheduling of Pumps to Improve the Performance of Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 417-432, January.
    2. Turner, Jonathan P. & Qiao, Jianhong & Lawley, Mark & Richard, Jean-Philippe & Abraham, Dulcy M., 2012. "Mitigating shortage and distribution costs in damaged water networks," Socio-Economic Planning Sciences, Elsevier, vol. 46(4), pages 315-326.
    3. Xiang He & Yongbo Yuan, 2019. "A Framework of Identifying Critical Water Distribution Pipelines from Recovery Resilience," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3691-3706, September.
    4. Amin Minaei & Adell Moradi Sabzkouhi & Ali Haghighi & Enrico Creaco, 2020. "Developments in Multi-Objective Dynamic Optimization Algorithm for Design of Water Distribution Mains," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2699-2716, July.
    5. Wei Liu & Binhao Wang & Zhaoyang Song, 2022. "Failure Prediction of Municipal Water Pipes Using Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1271-1285, March.
    6. Wang, Shuliang & Hong, Liu & Chen, Xueguang, 2012. "Vulnerability analysis of interdependent infrastructure systems: A methodological framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3323-3335.
    7. 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.
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    Cited by:

    1. Sooyeon Yi & G. Mathias Kondolf & Samuel Sandoval-Solis & Larry Dale, 2022. "Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5675-5694, November.

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