Large Water Distribution Networks Research Articles

Defining the optimization strategy for solar energy use in large water distribution networks: A case study from the Valle Inferior irrigation system, Spain

Irrigation modernization in Spain has brought significantly increased energy requirements. To provide the needed power, solar energy is rising in popularity. However, matching the availability of solar energy to irrigation demand remains a challenge. In the Valle Inferior irrigation system, while enough solar energy is produced on an annual basis, only 52 % of the energy consumed for irrigation is from own solar production, mainly due to the practice of night-time irrigation. For this system, this study explores two solar energy optimization scenarios. The first aims to optimize use of own solar energy by adjusting the current 24-h irrigation schedule to 8 or 12 h. The analysis finds that in situations where water availability is limited, this change can enable the annual percentage of own solar energy to be increased to 98 % of the total energy consumed. Nonetheless, such optimization of solar energy use gives rise to difficulties in the system itself, as farmers prefer to irrigate at night. In the second scenario, economic profits are optimized with the sale of excess solar energy. In this scenario, besides water availability and irrigation schedule, the optimum also depends on energy prices. Optimization is thus found to differ, depending on the optimization target, as well as water availability and the irrigation schedule used. This poses new operational challenges for large irrigation system management.

Introducing bacterial community turnover times to elucidate temporal and spatial hotspots of biological instability in a large Austrian drinking water distribution network

Ensuring biological stability in drinking water distribution systems (DWDSs) is important to reduce the risk of aesthetic, operational and hygienic impairments of the distributed water. Drinking water after treatment often changes in quality during transport due to interactions with pipe-associated biofilms, temperature increases and disinfectant residual decay leading to potential biological instability. To comprehensively assess the potential for biological instability in a large chlorinated DWDS, a tool-box of bacterial biomass and activity parameters was applied, introducing bacterial community turnover times (BaCTT) as a direct, sensitive and easy-to-interpret quantitative parameter based on the combination of 3H-leucine incorporation with bacterial biomass. Using BaCTT, hotspots and periods of bacterial growth and potential biological instability could be identified in the DWDS that is fed by water with high bacterial growth potential. A de-coupling of biomass from activity parameters was observed, suggesting that bacterial biomass parameters depict seasonally fluctuating raw water quality rather than processes related to biological stability of the finished water in the DWDS. BaCTT, on the other hand, were significantly correlated to water age, disinfectant residual, temperature and a seasonal factor, indicating a higher potential of biological instability at more distant sampling sites and later in the year. As demonstrated, BaCTT is suggested as a novel, sensitive and very useful parameter for assessing the biological instability potential. However, additional studies in other DWDSs are needed to investigate the general applicability of BaCTT depending on water source, applied treatment processes, biofilm growth potential on different pipe materials, or size, age and complexity of the DWDS.

A Graph-Based Optimization Framework for Large Water Distribution Networks

Water distribution networks (WDNs) have a crucial task: to reliably provide sufficient and high-quality water while optimizing financial resources. Achieving both reliability and resilience is vital. However, oversizing capacities can be costly and detrimental to water quality due to stagnation. Designing WDNs requires the consideration of these factors, resulting in a multi-objective optimization task typically addressed with evolutionary algorithms. Yet, for large WDNs with numerous decision variables, such algorithms become impractical. Complex network analysis offers an efficient approach, particularly with mathematical graphs representing WDNs. Recently, a graph-based multi-objective design approach using a customized measure (demand edge betweenness centrality) and a surrogate method for water quality assessment in large WDNs were developed. This paper combines these graph-based approaches into an optimization framework suitable for complex, real-world WDNs. The framework aims to minimize costs, maximize resilience, and exclude designs with poor water quality. It is demonstrated on a toy example, and its computational efficiency is shown by a real case study with 4000 decision variables, obtaining results in just 18.5 s compared to weeks of computation time with a state-of-the-art evolutionary algorithm.

MAGNets: Model Reduction and Aggregation of Water Networks

Hydraulic models of large water distribution networks can have thousands of components, and real-time simulation of these systems can be slowed down by their complexity. Several methods have been developed for which the size of the water network models can be reduced while the hydraulic performance of the reduced systems remains very similar to the original model. Model reduction allows users to model only components of interest, thus saving expensive computation time. However, while model reduction has been widely adopted for control and optimization purposes, few tools are available to reduce models on command. This paper introduces MAGNets, an open-source Python package capable of reducing and aggregating EPANET-compatible water models using the variable elimination reduction method. The package allows the user to specify the operating point around which the model will be reduced, the nodes that must remain in the network, and the maximum nodal degree of nodes removed. The reduced model preserves pressure heads of the original model with high accuracy and results in faster running times. The reduction algorithm iteratively removes nodes from the full model and updates the adjacent nodal demands and pipe properties until the final reduced model is achieved. To test the effect of the order of node removal on the size of the reduced model and computational complexity, we tested three different strategies for reduction order. Results suggest that dynamically updating reduction order greatly improves model performance compared to static and random orders. To increase usability, the Python package includes twelve benchmark networks for testing and validation. MAGNets allows for the quick and efficient reduction of hydraulic models as a way to facilitate time-sensitive tasks, such as real-time state estimation and control.

Multilevel Partitioning with Multiple Strategies for Complex Water Distribution Network

The operation and dispatching of large-scale water supply systems is becoming more and more complex, and effective partitioning is a good response measure to this increasing complexity. This paper presents a multilevel partitioning method using freely configurable strategies suitable for multiobjective situations such as allocation of water sources, pressure regulation, and flow measurement. The method mainly relies on two strategies. Using Strategy A, water supply features are constructed based on water sources and a partition clustering is implemented that is suitable for the overall partitioning of a water supply from multiple sources. Using Strategy B, important pipes are first extracted to simplify the search path and then various constraints (pressure boundary, flow shunt, cross-river management) are used to select control points and partition by graph theory searching. This strategy is suitable for refining partitioning of the structure of a specific source. A combination of the aforementioned strategies was applied to the first-level and second-level partitioning in a case study. By flexibly configuring strategies and constraints, good partitioning is achieved and multiple management goals are accomplished satisfactorily. This research enhances understanding of the operational issues in complex water systems. The overall management of water networks can be disassembled into tasks at all levels and with their own objectives performed in stages. The solution provided in this study has the advantages of flexibility and effectiveness for a variety of complex water distribution situations, contributing insights into the real challenges of partitioning large water distribution networks.

Sensor Placement for Leak Localization in Water Distribution Networks Based on Graph Convolutional Network

Nowadays, the research on optimal sensor placement (OSP) is very active in the field of leak detection and localization in the water distribution network (WDN). Considering that most of the WDNs are irregular networks, this article proposes a new method for OSP applied to irregular network system conditions. From the initial set of a limited number of sensors placed by node data features, the proposed method makes a completely new change to the limited number of sensor layouts. The solution is based on a graph convolutional network (GCN) algorithm, which combines node features and graph structure features. In order to verify the effectiveness of the algorithm, the nodes selected by the algorithm in this article are used to locate the leakage of the pipeline network. By simulating real leakage scenarios, the method is applied to a small WDN (Anytown) and a large WDN (Net3). The results show that the method proposed in this article outperforms other comparison algorithms to a certain extent.

Topological and hydraulic metrics-based search space reduction for optimal re-sizing of water distribution networks

Abstract Pipe re-sizing of water distribution networks (WDNs) aims at improving the service performance to the required level, while minimizing the cost of replacing pipes in the network. The main challenge comes from the identification of the most effective pipes to re-size from a large number of interacting components. Performing a global search over all pipes in large WDNs does not guarantee a feasible and efficient solution due to the enormous search space, even by employing advanced algorithms, e.g., evolutionary algorithms. This paper proposes a novel method to reduce the search space for optimal re-sizing based on topological metrics from Complex Network Theory and hydraulic metrics, while providing suboptimal solutions comparable to the full search solutions, i.e., considering all pipes as candidates. The topological metrics are based on the edge-betweenness tailored for WDN analysis. Hydraulic metrics are unit head loss and flow rates of pipes computed based on simulation of the WDN in the current configuration. The optimal re-sizing plans obtained, particularly those using edge betweenness, were tested on a real WDN. The results are comparable with the full search solutions but they are much more efficient to obtain and require replacing mostly contiguous pipes, i.e., easier for practical fieldwork.

Quality zones automatically identified in water distribution networks by applying data clustering methods to conductivity measurements

This paper presents a clustering study showing how conductivity measured every five minutes by 215 probes over four years can be used to determine specific quality zones for a large Water Distribution Network (WDN): 8500 km of pipes, 4.6 M customers. Conductivity time-series are compared using Dynamic Time Warping. Then, probes are ordered using a density-based method, and probe clusters are extracted automatically. The clusters are a sound representation of water quality in the WDN, both in terms of water origin and water residence time. More specifically, zones directly impacted by plants or by external water imports, mixing zones and zones influenced by tanks, can be isolated and analyzed. Globally, 82% of the probes were found to be clustered, consistent with expert knowledge on the WDN operation; 13% were unclassified; 3% were erroneously clustered; and 1% seemed to be reasonably clustered, without any physical understanding yet. Besides providing users with an increased understanding of water quality in WDNs, conductivity-based clusters offer an interesting prior tool for contamination warning systems.

Applications of the single-port linear Thevenin theorem for focused and efficient analysis of a sub-network connected with a large existing pipe network

ABSTRACT An existing water distribution network (WDN) may need to be expanded by adding a sub-network for the newly developed areas. The size of the problem becomes larger when the stochastic nature of domestic demands, optimal design and layouts, control, and operation of various hydraulic components are considered. In this study, the single-port Thevenin theorem used in electrical circuits is applied to reduce a large WDN with its equivalent network consisting of a single source and a single pipe. The equivalent network is then attached to a sub-network for focused analysis. The accuracy and robustness of the proposed network reduction procedure are investigated on realistic WDNs for various sub-network demands using steady and extended period simulations. A simplified approach is also presented to achieve the same objective but constrained by the level of accuracy. Hydraulic engineers can use the proposed methodology as an efficient network reduction tool.

Exploiting digitalisation to plan interventions on large water distribution networks

Cities rely heavily on the services provided by water distribution networks. These networks are large and complex, consisting of thousands of kilometres of buried pipes and dozens of facilities where water is treated, pumped and stored. Infrastructure managers are entrusted with the planning and execution of interventions on these assets to ensure that the provided service exceeds the minimum levels mandated by stakeholders at all times. This is a difficult task due to the spatial extent of these networks, shrinking budgets and the complexity of coordinating with multiple stakeholders. Previously, Kerwin and Adey presented an approach to address these concerns, using a small example network, leaving open the question of how this approach would work on a large real-world network. This paper fills this gap by discussing the simplifications needed to apply the methodology to a larger network and demonstrating its advantages with three applications: (a) estimating the budget requirements needed to implement various intervention strategies, (b) communicating project-level trade-offs of different intervention strategies and budget scenarios and (c) investigating how the intervention-planning activities of other networks could affect these estimates. The methodology is demonstrated on a large distribution network consisting of 14 pressure zones for a 5-year period.

A hybrid model-based method for leak detection in large scale water distribution networks

During the past decades, the problem of finding leaks in Water Distribution Networks (WDN) has been challenging. The quicker detection of leaks, on one hand prevents water loss and helps avoiding economic and environmental leakage consequences. On the other hand, increasing the speed of leak detection increases the false leak detection that imposes high costs. In this paper, we propose a fast hybrid method using AI algorithms and hydraulic relations for detecting and locating leaks and identifying the volume of losses material. The proposed method relies on simple and cost-effective flow sensors installed on each junction in the pipeline network. We demonstrate how influential features for leak detection would be generated by using hydraulic equations like Hazen–Williams, Darcy–Weisbach and pressure drop. Through exploiting Decision Tree, KNN, random forest, and Bayesian network we build predictive models and based on the pipeline topology, we locate leaks and their pressure. Comparing the results of applying the proposed method on various leak scenarios shows that the proposed method in this paper, outperforms other existing methods.

An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

Geospatial and Hydraulic Simulation to Design District Metered Areas for Large Water Distribution Networks

AbstractWater distribution systems can be divided into district metered areas (DMAs) to improve their management. DMAs are individual service regions within a distribution system that have a define...

A stochastic framework for hydraulic performance assessment of complex water distribution networks: Application to connectivity detection problems

This paper is concerned with the hydraulic performance assessment of large scale water distribution networks in presence of uncertainty. In particular, the associate connectivity detection problem is examined in detail. For this purpose, a Bayesian system identification methodology is combined with an efficient hydraulic simulation model. A number of hydraulic model classes are defined as potential connectivity events. Based on information from flow rates in the pipes, the proposed updating technique provides estimates of the most probable connectivity scenarios. Such scenarios correspond to the model classes that maximize their evidences or posterior probabilities. The effectiveness of the proposed identification framework is illustrated by applying the connectivity detection approach to a real water distribution system.

Simulation-Based Analysis of a Real Water Distribution Network to Support Emergency Management

Water distribution systems have a safety-critical role and are vulnerable to a range of intentional threats. Committing to emergency preparedness, critical and missing metrics are the residual water storage in tanks and the estimated operative autonomy (in hours). This article presents a high-fidelity simulator developed to calculate the mentioned metrics and to evaluate the impact of adverse events, such as faults and/or cyber-attacks on a real water distribution system. This simulator utilizes a refined model of the network for accurate results; a commercial simulator to trustworthily solve the hydraulic equations; and an ad-hoc module to follow the evolution in time of the event. A large real water distribution network has been modeled thanks to real data provided by an operator and used as a case study. A comprehensive impact analysis of the single failure has been carried out. Results show that the simulator has been able to elicit unforeseen use cases and correctly quantify the required metrics. This article has been implemented in the framework of the European project ATENA.

Distributed zone MPC of pressure management for water distribution network systems

In the pressure management of large scale water distribution network (WDN), a distributed zone model predictive control (MPC) is proposed to keep the terminal water head in thedesired pressure range for satisfying the customer's demand, avoiding frequently operating of the actuator and reducing the correlation between subsystems. To ensure the existence of feasible solutions of constrained distributed zone MPC, a new reference trajectory of the tank level is introduced as an optimised variable. With the consideration of the desired pressure range constraints on the new reference trajectory and some physical constraints on the corresponding physical variables, a distributed zone MPC is presented to minimise the weighted sum of the three terms in the proposed performance index. To achieve the convergence of distributed zone MPC optimisation problem, an augmented Lagrangian formulation is applied to the distributed coordinated strategy. The proposed distributed zone MPC is applied to the WDN in the Shinan district of Shanghai, and the effectiveness of the method is illustrated.

Model Reduction and Outer Approximation for Optimizing the Placement of Control Valves in Complex Water Networks

The optimal placement and operation of pressure control valves in water distribution networks is a challenging engineering problem. When formulated in a mathematical optimization framework, this problem results in a nonconvex mixed integer nonlinear program (MINLP), which has combinatorial computational complexity. As a result, the considered MINLP becomes particularly difficult to solve for large-scale looped operational networks. We extend and combine network model reduction techniques with the proposed optimization framework in order to lower the computational burden and enable the optimal placement and operation of control valves in these complex water distribution networks. An outer approximation algorithm is used to solve the considered MINLPs on reduced hydraulic models. We demonstrate that the restriction of the considered optimization problem on a reduced hydraulic model is not equivalent to solving the original larger MINLP, and its solution is therefore sub-optimal. Consequently, we investigate the trade-off between reducing computational complexity and the potential sub-optimality of the solutions that can be controlled with a parameter of the model reduction routine. The efficacy of the proposed method is evaluated using two large scale water distribution network models.

A Stochastic Framework for Reliability and Sensitivity Analysis of Large Scale Water Distribution Networks

The hydraulic reliability and sensitivity analysis of large scale water distribution systems in presence of uncertainty is considered in this work. The assessment of the network reliability and sensitivity is performed by an efficient Markov chain Monte Carlo method, namely Subset simulation. Prescribed nodal heads of storage tanks, nodal demands and pipe roughness coefficients are modeled as uncertain parameters and described in a probabilistic manner. Failure is assumed to occur when the minimum nodal head in the network is lower than a minimum allowable value. The efficiency of the proposed method is demonstrated with the analysis of a real water distribution network consisting of a large number of nodes and pipes (of the order of thousands). The corresponding reliability problem represents a high dimensional problem. The approach gives an important insight into the performance, reliability and sensitivity of a class of complex utility networks.

Management of Water Losses in Water Supply and Distribution Networks in Turkey

Water losses in water supply and distribution systems have been a serious problem in Turkey and all over the world causing economic costs and environmental drawbacks. Total water losses is the summation of real (physical) and apparent (commercial) losses. Main activities to reduce water losses include digitizing the components of network using Geographical Information Systems (GIS), dividing large water distribution networks (WDNs) into District Metered Areas (DMAs), monitoring, carrying out yearly water balance, hydraulic modelling, pressure management, active leakage control, recording water consumption of the users, reducing meter errors and illegal water consumption. The Ministry of Forestry and Water Affairs in Turkey issued a new regulation for water losses reduction in 2014 based on the methods used to determine and reduce the losses. This is followed by issuing practical description of the technical procedure to reduce the losses in 2015. The new legislation aims to reduce total water losses in all water authorities below 25% within certain time periods. All the water authorities are required to carry out yearly water balance and report them to the ministry together with forms to follow up the efforts towards water losses reduction. In order to apply the prescribed techniques in a successful way, first of all water losses problem should be understood well, technical and human resource capacity should be improved, and sufficient financial resources should be allocated. Also, the water utilities should have a strategic plan and a strong commitment to reduce water losses.

Reliability-based design of Water Distribution Networks using Self-Adaptive Differential Evolution algorithm

This paper presents the application of Self-Adaptive Differential Evolution (SADE) algorithm for reliability-based design of Water Distribution Networks (WDNs). The algorithm is first applied and tested for deterministic design of various benchmark WDNs. Reliability-based design is then performed on the benchmark WDNs as well as real WDN in India by employing the SADE algorithm considering the uncertainty in nodal water demands. Monte Carlo simulation approach is used to incorporate the uncertainty in nodal demands; and pressure driven analysis is adopted for simulating the distribution of flows and pressures in pipe networks with the help of EPANET toolkit. A thorough comparison is made between the performance of SADE and DE methods. The results illustrate that SADE performs much better than DE since SADE converges faster with a higher success rate, making it a more preferable option for WDN design problem. The results of the study also illustrate that the optimal cost increases with increasing uncertainty level, but the percentage increase in cost is problem specific, which requires a robust tool for handling such complexities. The consistent results obtained by the application of SADE algorithm makes it a preferable option for reliability-based design of large WDNs including real world problems.