Optimization Of Water Distribution Systems Research Articles

Seismic retrofit optimization of water distribution systems based on the reduction of uncertain damage scenarios

Pre-event interventions, such as retrofit of critical components, are effective strategies to improve the seismic performance and resilience of infrastructure systems. This paper proposes a damage scenario reduction method aims to reduce the computation burden within the seismic retrofit optimization for water distribution systems (WDSs) considering the uncertain damages of pipelines. The optimization framework consists of a multi-objective optimization model for WDS pipeline retrofit and a sequential optimization model for the reduction of uncertain damage scenarios. The multi-objective optimization model is established to minimize the retrofit cost and maximize the seismic performance of WDS. The sequential optimization model, namely weighted optimization-based probabilistic scenarios (OPS + W), was designed to mitigate the computational burden of the multi-objective optimization model, which arises from considering pipeline damage uncertainties through quasi-Monte Carlo (QMC) sampling that generates numerous damage scenarios. The OPS + W selects a few representative damage scenarios from numerous QMC scenarios, aiming to minimize the error of pipeline damage probabilities counted by the representative scenarios, where the error is weighted by the importance of pipeline. The effectiveness of OPS + W is verified through its application in seismic retrofit optimization of two WDS cases and compared with other scenario reduction methods. Application results reveal that the retrofit strategies derived from OPS + W have the highest similarity to those obtained from QMC scenarios while requiring only 3 %–10 % of the computation time compared to using QMC scenarios.

Towards transferable metamodels for water distribution systems with edge-based graph neural networks

Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in the design, control, and optimization of water distribution systems. Recent research highlights the potential of metamodels based on Graph Neural Networks as they efficiently leverage graph-structured characteristics of water distribution systems. Furthermore, these metamodels possess inductive biases that facilitate generalization to unseen topologies. Transferable metamodels are particularly advantageous for problems that require an efficient evaluation of many alternative layouts or when training data is scarce. However, the transferability of metamodels based on GNNs remains limited, due to the lack of representation of physical processes that occur on edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Graph Neural Networks, which extend the set of inductive biases and represent link-level processes in more detail than traditional Graph Neural Networks. Such an architecture is theoretically related to the constraints of mass conservation at the junctions. To verify our approach, we test the suitability of the edge-based network to estimate pipe flowrates and nodal pressures emulating steady-state EPANET simulations. We first compare the effectiveness of the metamodels on several benchmark water distribution systems against Graph Neural Networks. Then, we explore transferability by evaluating the performance on unseen systems. For each configuration, we calculate model performance metrics, such as coefficient of determination and speed-up with respect to the original numerical model. Our results show that the proposed method captures the pipe-level physical processes more accurately than node-based models. When tested on unseen water networks with a similar distribution of demands, our model retains a good generalization performance with a coefficient of determination of up to 0.98 for flowrates and up to 0.95 for predicted heads. Further developments could include simultaneous derivation of pressures and flowrates.

Sustainable Planning & Optimization of Water Distribution System of Vikasnagar, Dehradun

This paper provides a comprehensive overview of the planning process for establishing a distribution network for water supply in Vikasnagar, Dehradun, focusing on the utilization of groundwater sources over surface water sources. It delves into the characteristics of groundwater source, discusses the infrastructure requirements, and addresses the projected water demand for Vikasnagar in the coming decades. Additionally, it highlights the current water scarcity issues faced by Vikasnagar and emphasizes the urgency of implementing sustainable solutions to ensure adequate water supply for the growing population. The sustainable provision of water in Vikasnagar remains a critical challenge due to the depletion of surface water sources. To address this issue, ongoing research endeavors are exploring the utilization of groundwater as a primary source to meet the region's ultimate water demand. Preliminary findings suggest the potential viability of groundwater as a dependable water source for Vikasnagar ultimate demand. However, challenges such as groundwater depletion, contamination risks, and socio-economic implications necessitate further investigation and intervention. In conclusion, ongoing research on groundwater-based water supply in Vikasnagar shows promising prospects for mitigating water scarcity challenges. However, to realize a sustainable water future for the region, concerted efforts must be made to address existing limitations and pave the way for innovative solutions that prioritize both environmental conservation and societal well-being.

Water Distribution Systems: Integrated Approaches for Effective Utility Management

An extensive review of research advances in distribution system management was conducted to include methods, tools, and frameworks for integration. Sources included archival journals and water industry reports from research institutes, government regulators, and industry media covering case studies of advances that were implemented. Results showed that while effective management of water distribution systems requires an integrated approach, none of the available frameworks for one are in broad use in the water supply industry. Frameworks developed include a management standard of the American Water Works Association and Distribution System Optimization, a methodology for physical, hydraulic, and water quality performance assessment. The intelligent systems framework also offers a promising pathway to integration, but it lacks a definite structure. The voluntary aspect of adopting innovations within the fragmented and uncoordinated nature of the water utility industry poses a barrier to adoption of such innovations. Another barrier is the uncoordinated arrangements of water research stakeholders with different incentive structures. Intelligent water systems offer a way to incentive the utility industry to encourage implementation. They can provide a bottom-up approach where the utility industry can see advantages, as opposed to a top-down approach where they are expected to adopt a method without seeing clear benefits. Research to develop new and improved tools is needed, but the research roadmap should prioritize implementation.

Water Distribution System Optimization Considering Behind-the-Meter Solar Energy with a Hydraulic Power-Based Search-Space Reduction Method

Water Distribution System Optimization Considering Behind-the-Meter Solar Energy with a Hydraulic Power-Based Search-Space Reduction Method

Efficient Operation of Integrated Electrical-Water System for Wind Power Accommodation

This paper investigates the operation of an integrated electrical-water system for wind power accommodation. The efficient operation can be considered as how well to handle the binary variables in a water distribution system (WDS) model. The optimization problem of WDS is NP-hard, and it often takes hours to find the optimal solution. Since wind power varies in minutes, long computation time may hinder the WDS from being fully used by power system to deal with the wind power fluctuation and uncertainties. This paper develops a three-step optimization model to answer the challenges of the WDS optimization problem. The solution hinges on the calculation of the binary variable values in advance. The first step is to construct a linear power-WDS co-optimization (LPWCO) model to find the optimal electrical demand of WDS. The second step develops a tank flow allocation based method and a greedy-based algorithm to determine the binary variable values of WDS. This allows the WDS optimization model to be efficiently solved by commercial solvers in the third step. The proposed three-step model converts the NP-hard optimization problem of WDS into a polynomial-time problem which can be solved quickly. This allows the wind power system to leverage the energy flexibility provided by WDS in the real-time operation. Results of case study show that, compared with existing methods, the proposed method can reduce the wind power curtailment and the operation cost by about 55% and 53%. More importantly, the solution time is reduced dramatically from hours to seconds, suitable for practical application.

Auto-enhanced population diversity and ranking selection-based differential evolutionary algorithm applied to the optimal design of water distribution system

Abstract The differential evolution (DE) algorithm is considered the most powerful evolutionary algorithm (EA) for the optimal design of water distribution systems (WDSs). However, when dealing with large-scale WDS optimization, issues such as premature convergence become a concern. This paper presents an auto-enhanced population diversity and ranking selection-based differential evolutionary (AEPD-RSDE) algorithm for the optimal design of WDSs, which is the first work that incorporates an AEPD strategy to avoid the premature convergence issue and enhance the exploration ability of DE applied to WDS optimization. Besides, the proposed algorithm includes a ranking selection strategy that replaces the tournament selection operator to enhance convergence speed. Three well-known WDSs, i.e., the New York Tunnels (NYT), the Hanoi network (HAN), and the Balerma irrigation network (BIN), were used to validate the proposed algorithm. Results indicate the proposed algorithm is able to find the current best solution, with a success rate of 100% for the NYT and HAN cases and lower average cost solution of €1.921 million for the BIN case relative to other EAs. Instead of solely focusing on ultimate performance comparison, search behavior analyses are conducted between different mutation and selection operators, offering deep insight to guide the development of more advanced EAs.

Identification of Indicators for Developing an Integrated Study on Urban Water Supply System, Planning, and Management

The increasing population growth, urbanization, climate change, mismanagement of water resources, and poorly planned water distribution systems with negligible integration with spatial planning are increasing water stress in many countries across the globe. Even though optimization of water distribution systems and integrated water management practices are being implemented in many cities and regions, there is a lack of an integrated study considering the urban water supply system, planning, and management. This paper identifies the various parameters and indicators to be considered along with the relevant tools and techniques for optimizing water resources, optimum water resource allocation, and integrated water supply and management. During the research, 116 indicators were identified through a literature review, followed by a three-round Delphi survey to attain consensus from 30 experts. At the end of the third round, 72 indicators achieved maximum consensus among the experts (W=0.741). The identified indicators can be used for developing an integrated model for urban water supply systems, planning, and management.

Improved selection strategy for multi‐objective evolutionary algorithms with application to water distribution optimization problems

AbstractMulti‐objective evolutionary algorithms (MOEAs) have been applied to water distribution system (WDS) optimization problems for over two decades. The selection strategy is a key component of an MOEA that determines the composition of a population, and thereby the evolutionary search process, which imitates natural selection by granting fitter individuals an increasing opportunity to reproduce. This paper proposes the convex hull contribution (CHC) selection strategy for generational MOEAs (CHCGen) as a novel selection strategy that is based on the CHC of solutions to the Pareto front in the objective space. Numerical experiments using a general MOEA framework demonstrate that the CHCGen selection strategy is able to outperform existing popular selection strategies (e.g., crowding distance, hypervolume contribution, and hybrid replacement selection). Moreover, it is illustrated that the CHCGen selection strategy is able to improve the performance of existing MOEAs such as NSGA‐II and GALAXY. The conclusions are based on the results of six bi‐objective WDS problems.

A novel self-adaptation and sorting selection-based differential evolutionary algorithm applied to water distribution system optimization

Abstract The differential evolution (DE) algorithm has been demonstrated to be the most powerful evolutionary algorithm (EA) to optimally design water distribution systems (WDSs), but issues such as slow convergence speed, limited exploratory ability, and parameter adjustment remain when used for large-scale WDS optimization. This paper proposes a novel self-adaptation and sorting selection-based differential evolutionary (SA-SSDE) algorithm that can solve large-scale WDS optimization problems more efficiently while having the greater ability to explore global optimal solutions. The following two unique features enable the better performance of the proposed SA-SSDE algorithm: (1) the DE/current-to-pbest/n mutation and sorting selection operators are used to speed up the convergence and thus improve the optimization efficiency; (2) the parameter adaptation strategy in JADE (an adaptive differential evolution algorithm proposed by Zhang & Sanderson 2009) is introduced and modified to cater for WDS optimization, and it is capable of dynamically adapting the control parameters (i.e., F and CR values) to the fitness landscapes characteristic of larger-scale WDS optimization problems, allowing for greater exploratory ability. The proposed SA-SSDE algorithm found new best solutions of $7.068 million, €1.9205 million, and $30.852 million for three well-known large networks (ZJ164, Balerma454, and Rural476), having the convergence speed of 1.02, 1.92, and 5.99 times faster than the classic DE, respectively. Investigations into the searching behavior and the control parameter evolution during optimization are carried out, resulting in a better understanding of why the proposed SA-SSDE algorithm outperforms the classic DE, as well as the guidance for developing more advanced EAs.

Robust Multi-Objective Design Optimization of Water Distribution System under Uncertainty

The multi-objective design optimization of water distribution systems (WDS) is to find the Pareto front of optimal designs of WDS for two or more conflicting design objectives. The most popular conflicting objectives considered for the design of WDS are minimization of cost and maximization of resilience index which are considered for the current study. Robust multi-objective optimization is to find the optimal set of the Pareto front considering demand is uncertain. The robustness is controlled by a single parameter that defines the size of the uncertainty set it can vary. The study explores ellipsoidal uncertainty set with different sizes and co-variance matrices. A combined simulation–optimization framework with a combination of self-adaptive multi-objective cuckoo search (SAMOCSA) and the fmincon optimization algorithm is proposed to solve the robust multi-objective design problem. The proposed algorithm is applied to medium and large WDS. The main contribution of this paper is to study the effect of demand uncertainty and the correlation on the WDS designs in a multi-objective framework. The study shows that the inclusion of correlation into the multi-objective design framework can significantly affect the optimal designs.

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

AbstractSurrogate models replace computationally expensive simulations of physically‐based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data‐driven techniques to develop metamodels of urban water networks (UWNs). In this article, we review 31 recent articles on ML‐based metamodeling of UWNs to outline the state‐of‐the‐art of the field, identify outstanding gaps, and propose future research directions. For each article, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including (a) the curse of dimensionality, hindering implementation for large case studies; (b) black‐box deterministic nature, limiting explainability and applicability; and (c) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. Recently developed neural network architectures, which extend deep learning methods to graph data structures, are preferred candidates for advancing surrogate modeling in UWNs. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi‐objective optimization. Lastly, the development and comparison of ML‐based metamodels can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks.

Multi-objective operational optimization toward improved resilience in water distribution systems

Abstract Resilience has currently attracted increasing interest in the optimization of water distribution systems (WDSs). Most research mainly focuses on optimal design problems. However, the system operation has not been investigated adequately regarding resilience. Therefore, we proposed an integral format of the demand-weighted modified resilience index (IMRI), which can capture the overall resilient performance throughout the operational period. This indicator was incorporated into the multi-objective operation optimization model as one of the objectives. Two benchmark networks were considered as case studies. The resulting Pareto fronts show a clear competing relationship between cost and resilience. Operating conditions in pumps, reservoirs and tanks at each regulation step were characterized by methods of resilience decomposition, which proved valuable intuitively for resilience regulation. A framework for explicit resilience assessment was also developed to examine directly the overall performance in statistics about those optimal solutions obtained. Explicit resilience results show that the IMRI can effectively quantify the resilience of system operation in the temporal dimension. Furthermore, scheduling more pumps, higher trigger-on levels of tanks and a wider range of trigger-level control could yield a more resilient solution to the operation of WDSs.

Optimization of Water Distribution System within Tented Camps

This paper proposes an optimized, gravity-looped water network for tented camps as a replacement of the water trucking method, which depends on conveying water from resource to water tanks set up within camps by trucks, allowing people to carry water through containers to their tent; this method is used to supply people live in tented camps with water when providing water cannot be met in other ways. The
 cost of installing the gravity-looped network is minimized using Linear Programming to select pipe diameters from commercially available pipes. The method proposed was developed by Alperovits and Shamir (1977) and modified by Goulter and Coal (1986); linear formulations were solved by MATLAB, builds upon results obtained from EPANET. Tulol camp, located in Syria, was chosen as a case study, being supplied with water by trucking. Diameter changes, after optimization, are observed where the total cost of pipes decreased by 9.75%.

Lost in Optimization of Water Distribution Systems: Better Call Bayes

The main goal of this paper is to show that Bayesian optimization can be regarded as a general framework for the data-driven modelling and solution of problems arising in water distribution systems. Scenario-based hydraulic simulation and Monte Carlo are key tools in modelling in water distribution systems. The related optimization problems fall into a simulation/optimization framework in which objectives and constraints are often black box. Bayesian optimization (BO) is characterized by a surrogate model, usually a Gaussian process but also a random forest, as well as neural networks and an acquisition function that drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible, and sample efficient, making it particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given, for instance, by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection of contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes, showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms are proposed for multi-objective detection problems using two different acquisition functions.

Multi-Objective Operation-Leakage Optimization and Calibration of Water Distribution Systems

This study aims to develop and solve a multi-objective water distribution systems optimization problem incorporating pumps’ optimal scheduling and leakage minimization. An iterative optimization model was presented for calibrating and computing leakages in water distribution systems to recognize the critical impact of leakage control on system operation. The multi-dimensional and nonlinear optimization model, incorporating pump control, consumer demands, storage, and other water distribution systems’ components, was constructed and was minimized using a multi-objective genetic algorithm coupled with hydraulic simulations. The model was demonstrated on two example applications with increasing complexity through base runs and sensitivity analyses. Results showed that leakage minimization competes against pumping, mainly when significant differences occur between demands during low and high energy tariffs. Pumping during the periods with high electricity tariffs (when the demands are high) generated pressure distribution that decreased the overall leakage related to pump scheduling that replicated the natural inclination to pump as much as possible at low tariffs (when the demands are low). The optimal fronts were found to be very sensitive to the leakage exponent value, and changing its value indeed contradicted the balance between minimizing the leakage and the energy cost significantly. Altogether, the idea presented in this paper was found capable of facilitating the decision-makers to conveniently select between the energy-efficient pump scheduling and pump scheduling reflecting minimum leakage based on the system operator’s preferences. The research also paves the way to rebuild the optimization model by incorporating water distribution reliability and water quality that, in some cases, may also contradict the choice between energy cost and leakage minimization.

Multi-objective optimization of water distribution system: a hybrid evolutionary algorithm

The optimal operation of water distribution systems is complicated due to multiple objectives that are in conflict, such as water quality versus cost. This work proposes to combine Strength Pareto Evolutionary Algorithm (SPEAII) with Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, called MOPSO-SPEAII, to establish a multi-objective model which handles water quality, costs and storage-reliable requirement. The main idea is that genetic operators are combined with particle swarm operators such that the fitness of SPEAII results is evaluated using MOPSO. An optimization-simulation model is prepared by linking the hybrid algorithm with EPANET software, and it is employed for a typical case study from the literature. The model outcomes verify that the MOPSO-SPEAII is more stable compared to SPEAII in terms of closeness to global minimum and can be used as a robust decision tool. However, the model application for a real sized system increases the computational intensity of the model.

Commit to Distribution System Optimization

Long Beach Water Department (LBWD) joined AWWA's Partnership for Safe Water Distribution System Optimization program in 2010 with a commitment to optimize its distribution system. A multidepartment team has since collected performance data and conducted a self‐assessment program; collaborated and adopted guidance from the Partnership's optimization program, including support from Partnership committee members; and achieved profound, positive operational changes.

Analytical Optimization Approach for Simultaneous Design and Operation of Water Distribution–Systems Optimization

AbstractThis paper presents an analytical algorithm for simultaneous least-cost design and operation of looped water distribution systems (WDSs). This method could be used to replace evolutionary m...

Assessment of optimal water distribution systems design under steady-state and transient conditions due to pipe roughness uncertainty

This paper presents a methodology to find the optimal design or rehabilitation of water distribution system (WDS) under both steady-state and transient conditions. Then, assessing the obtained optimal solution and studying its stability, reliability, due to uncertainty in pipe roughness coefficients (CHW). The WDS optimization objective is to minimize the rehabilitation cost by installing new pipes parallel to the existing ones achieving all hydraulic constraints. For this purpose, particle swarm optimization (PSO) is used as an optimization tool and Monte Carlo simulation (MCS) is applied to generate multiple realizations of CHW for different coefficient of variation (COVCHW). A comparison between crisp shape and continuous shape (S-shape) fuzzy membership functions is carried out to study the hydraulic availability indices to nodal pressure head values resulted from pipe roughness uncertainty. Both steady-state nodal and system reliabilities are estimated while, only the transient system reliability is calculated for the obtained optimal solution. The proposed methodology is applied on the New York City tunnel network (NYCTN) and the corresponding reliability is analyzed. Results show that as pipe roughness uncertainty increases, WDS reliability decreases.