Developing a Framework for the Reliability Analysis of Water Distribution Systems

Abstract

Being able to quantify the reliability of a water distribution system (WDS) is particularly important for decision makers as it allows to take objective decisions for the benefit of the population served by the system. The reliability of WDSs is an abstract concept that usually refers to the ability of the network to supply the water demanded by the consumers under different circumstances or conditions. However, setting explicit criteria to measure a system’s reliability has proven to be a challenging problem for researchers given the complexity and non-linearity of WDSs. To this point, no widely accepted measure methodology for reliability has been introduced in WDSs. Two main classes of reliability quantification methodologies can be identified in recent literature: 1) Stochastic Reliability Measures which quantify reliability based on probabilistic concepts and methods, and 2) Reliability Surrogate Measures which use easy to compute indexes, based on intuitive judgment, and that are expected to correlate with reliability. This thesis develops two estimators of stochastic reliability (MRE and HRE – Mechanical and Hydraulic Reliability Estimators), that also work as reliability surrogate measures, getting important features from both types of reliability measures. To test their applicability, a framework to evaluate the reliability of realistic WDSs using pressure-driven analysis under extended period simulation is also developed. The framework includes the use of a method to produce synthetic networks, and its further application to complete five case studies based on real systems from Colombia. Then a method to perform pressure-driven analysis, under extended period simulation, using the proven network solver EPANET 2.0, is introduced. Additionally, given that an efficient optimization procedure was required to deal with the large case studies, a method named NSGA-II+OPUS was developed and tested. Based on the results of a comparative and correlation analysis, it can be concluded that the proposed estimators are both easy to compute and implement in an optimization routine, and consistently representative of the reliability of the systems. Moreover, thanks to the new pressure-driven analysis method, the computation of stochastic reliability is accessible by an extensive evaluation of different functionalities.