Abstract

Water loss according to water leakages in water distribution systems (WDSs) is a challenging problem worldwide. An inappropriate operation of the WDS leads to unnecessarily high pressure distribution in the WDS and thus a large amount of water leakage exists. For this reason, optimal pressure management in WDSs through regulating operations of pressure reducing valves (PRVs) is priority for water utilities. The pressure management can be accomplished in a hierarchical control scheme with high level and low level controllers. While the high level controller is responsible for calculating pressure set points for critical nodes, the task of a low level controller is to regulate the pressures at the critical nodes to the set points. The optimal pressure management in the high level controller can be casted into a nonlinear programing problem (NLP) where PRV models are crucial and determine proper operation of the WDS and quality of overall pressure control. PRV models having been used until now either describe two operating modes (active and open modes) or three operating modes (active, open and check valve modes) with parameter dependence. Such models make the formulated NLP unsuitable for the case PRVs work in check valve modes or resulted in inaccurate NLP solution with unexpected operation modes of PRVs, respectively. Therefore, this paper proposes an accurate PRV model based on complementarity constraints. The new PRV model is parameter-less dependence and is capable of describing complete operation modes of PRVs in practice. As a result, the formulated NLP is general and provides accurate NLP solution. The efficiency of our new PRV model is demonstrated on numerous case studies for optimal pressure management of WDSs.

Highlights

  • According to significant population growth, cities are stressed worldwide predominantly

  • We propose an accurate pressure reducing valves (PRVs) model for formulation of the nonlinear programing problem (NLP), which overcomes the shortcomings of existing PRV models

  • Pressure control can be efficiently accomplished in a hierarchical control scheme Pressure control can be efficiently accomplished in a hierarchical control scheme with with high and low level controllers

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Summary

Introduction

According to significant population growth, cities are stressed worldwide predominantly. Many solution approaches proposed modeling simulator, and solving the were used to determine optimal pressure settings of PRVs or on/off working states of NLP. Optimization based gradient methods were applied to solve the NLP for identifying optimal pressure settings Such the methods are suitable for near real time control implementation due to less computation time requirement. To enhance the quality of NLP solution, the method of sequential convex programming (SCP) was applied for determination of optimal pressure settings for PRVs for regulating pressures in DMAs [38] The idea of this method lies in the fact that the nonlinear and non-convex equality constraints are linearized and the resulting convex optimization problem was solved iteratively until convergent condition is reached.

Existing Model of Pressure Reducing Valves
An Accurate PRV Model Based Complementarity Constraints
Problem Formulation for Optimal Pressure Management
Objective Function
The Continuity Equation at Node i
The Reservoir Water Levels
Case Study 1
The demand patterns were were assumed as the
Objective
Optimal
Optimal Pressure PRV
14. Pressure
Findings
Conclusions
Full Text
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