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Control of water distribution networks using convex approximations: WIP abstract

Published:16 April 2019Publication History

ABSTRACT

The status quo of control in water distribution networks (WDN) relies on simple, rule-based control strategies to operate pumps and valves. For example, pumps are switched on/off if a tank's water level---a local measurement---is below/above a certain threshold. As an alternative to localized, rule-based control, network-driven control that utilize WDN topology and measurements can be beneficial.

Unfortunately, network-driven control of WDNs is very difficult as hydraulic models are nonconvex, valve and pump models form non-trivial, combinatorial logic, and water demand patterns are uncertain. Prior research on control of WDNs addressed major research challenges, yet mostly adopted simplified hydraulic models, WDN topologies, and rudimentary valve/pump modeling. This Work-in-Progress showcases the potential of network-driven control of WDNs. The proposed approach amounts to solving a series of convex optimization problems that graciously scale to large networks. Simple case studies are included showcasing early results, and applications of the proposed optimization methods to other WDN applications are discussed.

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    • Published in

      cover image ACM Conferences
      ICCPS '19: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems
      April 2019
      367 pages
      ISBN:9781450362856
      DOI:10.1145/3302509

      Copyright © 2019 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 April 2019

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      Overall Acceptance Rate25of91submissions,27%
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