This paper presents an implementation of an extended simplification algorithm of water distribution network models for the purpose of inclusion in the online optimisation strategy for energy and leakage management in water distribution systems. Whereas the previously proposed reduced model represented accurately the original hydraulic water network characteristics, the energy distribution in the simplified model was not preserved. This could cause a situation where the pump speed required to satisfy specified minimum pressure constraints is different for the reduced model and the original model. This problem has been identified, and an appropriate modification to the simplification algorithm has been introduced. The idea comprises introduction of the energy audit of the water network and the calculation of new minimum service pressure constraints for the simplified model. The approach allows the preservation of both hydraulic and energetic characteristics of the original water network and therefore meets the requirements of the online optimisation strategy. Suitability of the proposed approach is evaluated via a case study. The modern parallel programming implementation allowed water network models consisting of several thousand elements to be reduced within 2 min with an average relative accuracy of less than 2% in terms of tanks flows.
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Research Article|
January 03 2013
Online simplification of water distribution network models for optimal scheduling
Daniel Paluszczyszyn;
1Water Software Systems, De Montfort University, Leicester, LE1 9BH, UK
E-mail: [email protected]
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Piotr Skworcow;
Piotr Skworcow
1Water Software Systems, De Montfort University, Leicester, LE1 9BH, UK
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Bogumil Ulanicki
Bogumil Ulanicki
1Water Software Systems, De Montfort University, Leicester, LE1 9BH, UK
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Journal of Hydroinformatics (2013) 15 (3): 652–665.
Article history
Received:
February 08 2012
Accepted:
August 09 2012
Citation
Daniel Paluszczyszyn, Piotr Skworcow, Bogumil Ulanicki; Online simplification of water distribution network models for optimal scheduling. Journal of Hydroinformatics 1 July 2013; 15 (3): 652–665. doi: https://doi.org/10.2166/hydro.2013.029
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