Journal of Water Resources Planning and Management

This paper analyses the cost-effectiveness of two different variants of real-time pressure control (RTC) of valves in water distribution networks (WDNs), namely, the local and remote RTC. The system considered for RTC application is made up of a WDN pressure zone fed through a single-control valve. Whereas both variants of RTC are based on dynamically adjusting the valve settings, the main difference lies in the source of the measurements used, that is, local measurements taken at the valve site and remote measurements from the critical node, respectively. The analysis was carried out considering RTC installation costs and operational costs associated with water production and expected pipe burst repairs, while taking as benchmark the no-control scenario. The two kinds of operational cost were evaluated through pressure-driven extended-period simulation with stochastic nodal demands and empirical modeling, respectively. The results pointed out that the local RTC is more cost-effective for small pressure zones. Resorting to remote RTC is justified only for larger systems.

Extensive uncertainties exist in many resources and environmental management problems, which can be interrelated and thus amplify the complexity and nonlinearity of study systems. The interactions from dependent random variables pose significant impacts on the potential management strategies. In this study, an inexact copula-based stochastic programming (ICSP) method was developed to deal with interactive uncertainties with interval and stochastic characteristics as well as to address nonlinear dependence among multiple random variables. Specifically, the impacts of their interactions among random variables were revealed based on the concept of copula. ICSP can also reflect the risk of violating system constraints with linear and nonlinear dependences. The developed ICSP method was then applied to planning water resources management problems; results (i.e., system benefit, economic penalty, water allocation, and flood diversion) under a variety of risk levels have been generated. Results are useful for generating desired strategies for water allocation and flood diversion under various individual and joint probabilities. Compared with the conventional joint-probabilistic chance-constrained programming (JCCP) approach, ICSP can better reveal multiple uncertainties and their interrelationships under nonlinear conditions and generate more robust solutions.

This article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA-II, the built-in solver in the MATLAB Global Optimization Toolbox (MLOT), and a newly-developed hybrid MOEA called GALAXY. A variety of parameter combinations of each MOEA is systemically applied to examine their impacts on optimization efficiency. Results suggest that the traditional MOEAs suffer from severe parameterization issues. For NSGA-II, the distribution indexes of crossover and mutation operators were found to have dominant impacts, while the probabilities of the two operators played a secondary role. For MLOT, the two-point and the scattered crossover operators accompanied by the adaptive-feasible mutation operator gained the best Pareto fronts, provided the crossover fraction is set to lower values. In contrast, GALAXY was the most robust and easy-to-use tool among the three MOEAs, owing to its elimination of various associated parameters of searching operators for substantially alleviating the parameterization issues. This study contributes to the literature by showing how to improve the robustness of identifying optimal solutions through better selection of operators and associated parameter settings for real-world UDS applications.

A new methodology is presented for real-time operation of water distribution systems (WDS) under the critical condition of limited electrical energy. The critical conditions could arise due to electric grid failure, extreme drought, or other severe conditions related to natural and artificial disasters such as sabotage, vandalism, terrorism, or war. The methodology presented considers both quantity and quality requirements of various water demands. The basic objective of optimizing water distribution system operations under limited availability of electrical power and/or water is to satisfy the required (requested) demand for service areas (or pressure zones) while meeting system pressure and water quality requirements of the system. The approach combines a genetic algorithm optimization procedure with the simulator EPANET in the framework of an optimal control problem. Combining the simulator and the genetic algorithm is accomplished within a MATLAB framework. The new methodology is illustrated using an example system incorporating both a WDS and electrical power distribution system (PDS) cooling-water system to evaluate the operations of the WDS under limited power supply conditions.

Coupling pressure management and energy recovery in water distribution networks (WDNs) is an appealing topic in the field of optimal management of water systems. The use of turbines or pumps as turbines (PATs) allows for reducing leakage within the network while also recovering energy that would otherwise dissipate, by means of pressure reducing valves (PRVs). Unlike water supply systems, where operating conditions remain essentially constant over time, a real-time control (RTC) is required in WDNs because of the variability of pressure and flow discharge during the day. To this end, a prototype for RTC of a WDN was presented and discussed in a recent paper, in which constant rotational speed of the impeller was considered. To overcome such a limitation and maximize energy recovery, an inverter was coupled to the prototype. A mathematical model of the prototype was developed and the decision variables were calculated by solving an optimization problem via the YALMIP tool of MATLAB at varying conditions of inflow discharge, pressure head at the inlet, and desired pressure head at the control node. The mathematical model was further validated through preliminary laboratory experiments, showing good agreement between simulated and actual values.

The private sector has been increasingly involved in the construction, management, and operation of water systems. There are several models for private-sector participation, but concessions have been made among the preferred choice of local, state, and federal governments. The literature identifies the uncertainty surrounding long-term, capital-intensive infrastructure projects as the main motives leading to renegotiations. The results of renegotiations are biased toward increasing tariffs, and compensation paid by the public sector only more rarely negatively affects the private partner. Renegotiations are perceived as being the main pitfall of concessions. This paper will analyze a unique data set of renegotiations of 31 water concessions in Portugal covering a period from 1995 to 2015. Results show that lower consumption and additional investments are among the main motives for renegotiation, which can occur at any point during the lifetime of a concession. The analysis confirms some initial hypotheses, such as larger concessions and longer contracts being more likely to be renegotiated. Indirectly, the occurrence of elections tends to increase renegotiations.

Drinking water advisories (DWAs) are a major issue facing many First Nations communities across Canada. This paper analyzes drinking water system data matched to DWA data using a probabilistic neural network (PNN) model to find key factors that influence the occurrence, frequency, duration, and cause of DWAs. First, for all data across Canada and subsequently for a number of data sets for individual provinces, the analyses were completed using an ensembles approach of running the same data set multiple times (in this case, five) to ensure that factors identified were representative of the entire data set and not just the training set. The results were compared with those from previous studies that used data mining techniques. Accuracies above 74% were achieved and key factors influencing each of the models were identified. The PNN models are a practical and powerful diagnostic tool for identifying key system attributes influencing DWAs, which can then be used to develop effective targeted solutions to mitigate the core issues that result in DWAs. These types of models can be used in other applications to identify influential factors and guide decision makers.

One question in the increasingly frequent discussions about energy management among water utilities is whether energy management policies have any effect on their energy footprints. This study examines the question using new data from public water systems in the United States. A statistically significant difference was found, indicating that water utilities with documented energy management policies, plans, or programs use about 30% less energy, on average, than those without, even after considering other important factors such as water system size, water source type, and climate conditions. This finding suggests that voluntary, organization-specific policies should be included among other best practices for sustainably managing water utilities’ energy use.

This paper presents a simple valve simulation method integrable into the global gradient algorithm (GGA), which is widely used in hydraulic analysis of water distribution systems. In the proposed method, valves are treated as pipes whose resistance is adjusted in the course of the GGA iteration to meet the valve setting. This method features a unified approach to pressure control and flow control valves. Comparison tests on example networks have shown that the proposed method computes the same results as an independent optimization-based method in a comparable number of iterations and copes with the cases where EPANET 2 produces incorrect results. The proposed method may easily be integrated into existing GGA-based hydraulic solvers, in particular into that of EPANET 2.

It is a major challenge for water companies worldwide to quickly react to bursts so that water loss is reduced and service improved. This paper proposes a novel data-driven method for district metering areas (DMAs) with multiple pressure sensors that detects bursts in a timely manner. With the use of data correlation between multiple pressure sensors, the problem of burst detection is transformed into one of outlier detection. A distance-based algorithm is employed to distinguish outliers (i.e., burst-induced data) from normal data by evaluating the similarity (or dissimilarity) between data. Furthermore, approximate location information of bursts can be provided by comparing abnormality degrees, which represent different magnitudes of pressure drop at each sensor. The method was applied in a gravity-based DMA with four pressure sensors to evaluate its performance. The results show that the method can detect relatively large bursts and raises few false alarms. In addition, the method is able to identify the pressure sensor nearest to the burst event, thus giving some approximate location information.