Stochastic Co-Design of Storage and Control for Water Distribution Systems

Water distribution systems (WDSs) are typically designed with a conservative estimate of the ability of a control system to utilize the available infrastructure. The controller is designed and tuned after a WDS has been laid out, a methodology that may introduce unnecessary conservativeness in both system design and control, adversely impacting operational efficiency and increasing economic costs. To address these limitations, we introduce a method to simultaneously design infrastructure and develop control parameters, the co-design problem, with the aim of improving the overall efficiency of the system. Nevertheless, the co-design of a WDS is a challenging task given the presence of stochastic variables (e.g., water demands and electricity prices). In this article, we propose a tractable stochastic co-design method to design the best tank size and optimal control parameters for WDS, where the expected operating costs are established based on Markov chain theory. We also give a theoretical result showing that the average long-run operating cost converges to the expected operating cost with probability 1. Furthermore, this method is not only applicable to greenfield projects for the co-design of WDSs but can also be utilized to improve the operations of existing WDSs in brownfield projects. The effectiveness and applicability of the co-design method are validated through three illustrative examples and a real-world case study in South Australia.

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Publication Details

Journal
IEEE Transactions on Control Systems Technology
Published
2024-10-18
DOI
https://doi.org/10.1109/tcst.2024.3477304
Citations
3
Primary Topic
Water Systems and Optimization
Type
article
Field-Weighted Citation Impact
0.78
Controls
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article

Stochastic Co-Design of Storage and Control for Water Distribution Systems

Chris Manzie, Erik Weyer, Angus R. Simpson, Lisa J. Blinco et al.
3 citations
IEEE Transactions on Control Systems Technology
Water Systems and Optimization
0.78
article

Stochastic Co-Design of Storage and Control for Water Distribution Systems

Chris Manzie, Erik Weyer, Angus R. Simpson, Lisa J. Blinco, Ye Wang
article en
3 citations

Abstract

Water distribution systems (WDSs) are typically designed with a conservative estimate of the ability of a control system to utilize the available infrastructure. The controller is designed and tuned after a WDS has been laid out, a methodology that may introduce unnecessary conservativeness in both system design and control, adversely impacting operational efficiency and increasing economic costs. To address these limitations, we introduce a method to simultaneously design infrastructure and develop control parameters, the co-design problem, with the aim of improving the overall efficiency of the system. Nevertheless, the co-design of a WDS is a challenging task given the presence of stochastic variables (e.g., water demands and electricity prices). In this article, we propose a tractable stochastic co-design method to design the best tank size and optimal control parameters for WDS, where the expected operating costs are established based on Markov chain theory. We also give a theoretical result showing that the average long-run operating cost converges to the expected operating cost with probability 1. Furthermore, this method is not only applicable to greenfield projects for the co-design of WDSs but can also be utilized to improve the operations of existing WDSs in brownfield projects. The effectiveness and applicability of the co-design method are validated through three illustrative examples and a real-world case study in South Australia.

IEEE Transactions on Control Systems TechnologyVol. 33(1)
The University of Melbourne (AU), South Australian Water Corporation (AU), The University of Adelaide (AU)
Clean water and sanitation
Openalex Percentile: Top 30%
Water Systems and Optimization
0.78
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Stochastic Co-Design of Storage and Control for Water Distribution Systems — Chris Manzie, Erik Weyer, et al. · IEEE Transactions on Control Systems Technology (2024) | TGRS Research Map | TGRS