Journal of Water Resources Planning and Management

Water grids are emerging as a means to address water scarcity in urban areas. These water grids are more complex than traditional supply systems, bringing new challenges to water-grid management. This paper seeks to address these challenges by demonstrating the capability of multiobjective optimization to aid in short-term operational planning for water grids. A framework for applying multiobjective optimization to short-term operational planning is demonstrated for a case study based on the South East Queensland Water Grid in Australia. The aim of the case study application is to find short-term (1 year) operating rules that maximize water security, minimize operational cost, and minimize spills from reservoirs. The results of the optimization process are a number of operating options, comprising sets of operating rules that perform optimally in terms of the objectives. The range of operating rules and objective performance found in the optimization process allows the decision-maker to explore the trade-offs in decision-making and to choose a set of operating rules based on their preferences on the management objectives.

Increased demand, reduced supply, or the imposition of new regulations might evince the physical limitations of the current infrastructure of a water-supply system and force structural intervention. The problem consists of determining capacity expansion solutions for multisource water-supply systems from a long-term perspective, with some representation of the uncertainty that can be involved and the risk-averse behavior of the decision makers. The systemic approach proposed includes a detailed simulation of physical processes, such as the water storage in surface reservoirs, the groundwater flow in aquifers, and the water transport, with explicit representation of water quality. Water quality is a crucial element in multisource systems because the quality of the source water often varies. Different capacity expansion solutions can be obtained that explicitly balance the trade­offs between the gains in system performance and the cost of the solution. The application of the systemic approach developed for the western Algarve multimunicipal water-supply system in Portugal shows that the approach described in this research can deal with a real world case study.

In spite of their hydraulic, temporal, and spatial connections, water and wastewater pipeline systems are traditionally planned and designed in a stationary and separated manner. This practice may have, in the early stages, already rooted future system operational risk. An integrated model was developed to evaluate the performance of these two systems, planned and designed according to the traditional water demand quota approaches adopted in China. The integrated model included an agent-based water end-use model to generate water demand and wastewater discharge, which was used to drive a water distribution system model and a drainage system model, both developed by the U.S. Environmental Protection Agency. In a case study in Beijing, the proposed model forecasted lower water demand and wastewater discharge, leading to oversized water systems designed by the traditional approach. However, the greater diurnal fluctuations would cause operational problems during the peak hour, as well as off-peak periods, in both pipeline systems. Vulnerable hotspots for urban water services were thus identified and potential mitigation options discussed.

This paper developed a multiobjective stochastic fractional goal programming (MOSFGP) model for optimal water resources allocation among industries based on analyses of water resources quantity, quality, and uncertainty. The model integrated chance constrained programming, fractional programming, and multiobjective goal programming. The developed model was then applied in a real-world case study. The developed MOSFGP has the following advantages: (1) the model deals with economic and social objectives simultaneously, thus reflecting the actual situation more realistically; (2) the model in fractional form can determine water resources use efficiency directly; (3) water quantity and water quality are coupled in the model; and (4) the model can generate different optimal schemes under different risk probabilities. Based on the results of the MOSFGP model, decision makers could use water resources efficiently and obtain higher economic and social benefits. The model is also of great significance to industrial structure optimization, which means adjusting the industrial structures on the basis of optimal water resources allocation schemes to achieve the goal of economic planning and sustainable development.

Finding the optimal pump operation in water distribution systems, taking into account hydraulic and water quality constraints, is a complex problem due to the nonlinear relationship between dynamic head loss and flow rate and between chlorine decay and water age, and due to the size of the problem. The proposed algorithm, for minimum cost pump scheduling, utilizes the operational graph algorithm applied to hydraulic and quality constraints. The proposed algorithm utilizes a graph algorithm that considers hydraulic and water quality constraints to find the pump scheduling that minimizes pump operational costs. The algorithm has short solution times and therefore is suitable for real-time water system control or, if used offline, for giving a recommendation on the pump operation taking into account hydraulic and water quality constraints. The algorithm results were compared with the best results found by enumeration to show that the operational graph algorithm returns a global minimal solution and, when combined with quality constraints, returns near-optimal results. The proposed algorithm was successfully demonstrated on a 24-h example application with a single pumping unit and, on the C-Town example application, with 11 pumping units and 168 time steps.

The design of contamination warning systems and the performance of forensic tools are dependent on the performance of the event detection algorithms (EDA). However, most current EDA evaluation approaches do not account for the actual changes of common water-quality parameters in response to a contaminant. Thus, the objective of the current study was to develop water-quality models to represent the dynamics of chlorine, hydrogen ion concentration (pH), and conductivity in response to two contaminants [potassium cyanide (KCN) and nicotine] using experimental data. For chlorine-contaminant dynamics, a two-species second-order model was used to represent the reactions between chlorine and the background dissolved organic carbon as well as the contaminant. To simulate the change in pH, an equilibrium model was used to account for various water-quality species and was coupled with the dynamic chlorine model. As for electrical conductivity (EC), a step response, which is a linear relationship to the amount of contaminant added, was used to simulate the change of EC. These water-quality dynamic models were incorporated into EPANET-MSX to more realistically simulate the responses of common water-quality parameters to a contamination event at a network scale, as well as assess current assumptions/evaluation techniques associated with risk assessment for sensor placement and EDA performance. Results demonstrated that the current EDA evaluation approaches, as well as contamination warning system (CWS) design assumptions, may not adequately represent the EDA performance under conditions likely to be observed within a distribution system.

The outlook and future water resources management of the South Saskatchewan River Basin (SSRB) in Alberta, Canada, under the potential combined impact of climate change and El Niño Southern Oscillation (ENSO), has been assessed using the water resources management model (WRMM) of Alberta Environment and Parks. The potential combined impact of climate change on the SSRB water management is simulated by WRMM on the basis of the Special Report on Emissions Scenarios (SRES) climate scenarios of the 2040–2069 (2050s) projected by general circulation models of the Intergovernmental Panel on Climate Change (IPCC), together with an active El Niño or La Niña. From results obtained for this study, license holders of the SSRB categorized under district irrigation, junior and future private irrigation, and senior, junior, and future nonirrigation consumptive user groups could be either more or less affected, depending on whether the active climate anomaly will be El Niño or La Niña, than by the impact of climate change alone.

Presented in this paper is a field data–based probabilistic approach to quantifying the expected pipe break rates of water distribution systems. Uncertain demands and variations in the roughness of pipes during their service lives are described as random variables. Sample values of these random variables are generated and input to a distribution system model to determine the resulting minimum and maximum pressures in Monte Carlo simulations. Based on an estimated break rate–maximum pressure relationship, the sample maximum pressures obtained from a Monte Carlo simulation are transformed into a sample of break rates, and the expected pipe break rate can subsequently be determined. The sample minimum pressures are used to gain a better understanding of the distribution network. This probabilistic approach is used for a part of the City of Hamilton’s network in Ontario, Canada. The results show that the frequency of low-pressure events is very small, but a higher minimum pressure criterion would inevitably increase expected pipe break rates. Local field data collection is necessary to use the proposed methodology. Savings resulting from reduced pipe break rates justify costs associated with data collection.

This study introduces a simulation-optimization model for deriving an operating policy for multireservoir systems. Two adjustable monthly rule curves are introduced to each reservoir in the system. The applied rule curves divide the reservoir volume into three zones in each within-year period. For each zone, a release coefficient is specified to indicate releases from the reservoir as a function of the available storage and time of the year. To obtain optimum rule curves and release rules, a self-adaptive genetic algorithm (GA) is developed to maximize the system’s hydropower production, subject to the system’s physical constraints as well as a desirable reliability to satisfy water demands. To evaluate the objective function and constraints, a simulation model based on the network flow technique and linear programming is developed and coupled to the GA. The model is applied to a real three-reservoir system in Iran and the results are discussed and compared to the standard operation policy (SOP).

Energy systems are not only intrinsically interesting, because power is itself of importance; they raise fascinating trade-offs in other areas particularly since there are almost invariably technical, economic, and ecological dimensions to these considerations. This paper illustrates this interplay through a staged process that starts with a direct optimization approach for a pumped-storage facility with the simple goal of achieving an optimal profit given well-forecasted flows and energy prices. Sir Adam Beck Pumping Generating Station, located on the Niagara River, is selected as the subject of the model application. When analyzed for probable changes in current electricity rates, a 1–24% reduction in profit is realized depending on the month. Interestingly, relative to current design the model predicts only modest profit throughout the year with an increasing reservoir footprint. Since considerations other than the purely technical quickly arise, this paper considers the trade-off between hydropower and ecological targets imposed by the 1950 Niagara River Treaty. This exploratory study makes no pretense to forecast likely or advisable developments, but rather considers a possible role that a pumped-storage operation might hypothetically play in the Ontario spot market.

Least-cost operation of water distribution systems (WDS) is a well-known problem in water distribution systems optimization. The formulation of the problem started with deterministic modeling, and the problem was subsequently handled with more sophisticated stochastic models that incorporate uncertainties related to the problem’s parameters. This work applied a recently developed algorithm entitled limited multistage stochastic programming (LMSP) to deal with the stochastic formulation of the least-cost operation of WDS and serves merely as a proof of concept on an illustrative network. The demand is considered as the uncertain parameter in the problem formulation. This algorithm reduces the complexity of the classical multistage stochastic programming (MSP) by adding constraints which result in a linear growth of the problem, as opposed to an exponential growth in the MSP problem. This is accomplished by clustering decision variables based on a postanalysis of the implicit stochastic program of the problem. The clusters allow reduction of the number of decision variables, thus reducing the complexity of the optimization problem. The LMSP is expected to increase the cost because of the additional constraints imposed on the problem; however, a trade-off exists between the computational complexity and the optimality of the objective value to the number of clusters considered. An illustrative example application is provided for demonstrating the suggested methodology abilities.