Search

In South Asia, Latin America, and Africa, approximately 1.3 billion people intermittently receive water for domestic use through piped networks, known as intermittent water supply (IWS). IWS poses high operation costs and Non-Revenue Water (NRW) to water utilities, coping costs for water storage and treatment facilities to consumers, inequitable water supply, and health problems. Water scarcity is one of the main causes of water supply intermittency. This study presents a heuristic approach for sectorizing and optimum operation of IWS systems under water scarcity conditions. The methodology employs a two-step strategy: the first is a heuristic method for dividing the network into sectors based on connectivity, direct access to the source, and minimizing cut size between sectors. The second is the application of the evolutionary multiobjective optimization method, a nondominated sorting genetic algorithm (NSGA-II), for the optimum operation of IWS systems considering three objectives: the operational cost, level of equity, and level of pressure among consumers. Design variables for the optimization problem include the status of bridge pipes between sectors and the triggers on/off for the tank levels. A benchmark network was used to demonstrate the effectiveness of the proposed methodology. The results of the application of optimization for the optimal operation of nonsectorized and sectorized networks highlight the potential of improving both equity and pressure uniformity of the IWS system. Sectorization and optimal operation of IWS systems appear to have significant effects on improving pressure uniformity in different parts of the sector, improving equity among consumers and saving energy throughout the day. [ABSTRACT FROM AUTHOR]

Sectorization of a water distribution network (WDN) into district metered areas (DMAs) is a proven solution for proactive leakage control. Traditionally, WDN sectorization is conducted by local experts using a trial-and-error approach, often resulting in the identification of arbitrary solutions. Some recently published methods try to improve WDN sectorization by automating the process, especially by using optimization. Various sectorization criteria, constraints, and limitations are introduced, which often fail to consider the issues faced by poorly managed WDNs such as limited funds and shortage of water balance data. These methods also have poor computational efficiency imposed by optimization methods used. This paper presents a new distribution network sectorization method (DeNSE), that overcomes these deficiencies. This method is based on a heuristic procedure in which WDN sectorization is driven by efficient tracking of water balance data and determining the lowest cost investment needed to maintain the same level of operational performance. The above-mentioned set of criteria is particularly well suited for initial sectorization of WDNs when major uncertainties in water balance data often lead to poor management decisions. The DeNSE method is validated and benchmarked against other sectorization methodologies in a case study of a large, real-world WDN. The results show that DeNSE can identify sound, realistic sectorization solutions that are in some respects better than corresponding solutions reported in the literature. DeNSE also enables high computational efficiency, ensuring its applicability to real-world WDNs. [ABSTRACT FROM AUTHOR]

This paper considers the sectorization of a large water distribution network into district metered areas (DMAs) and simultaneously optimizes rehabilitation of the network with new pipes, control valves, and storage tanks. Because available water resources are much lower in the dry season, both the design and operational settings are optimized to satisfy water-demand, water-quality, and pressure constraints, as well as efficiency indices under stringent conditions. Because of the heterogeneity of the multiple decision variables and the complicated way they interact through the multiple objectives (some complimentary and some conflicting), it is not possible to fully automate the simultaneous sectorization, rehabilitation, and operational optimization. Therefore, a multistage approach is used where engineering judgment and network graph simplification and visualization tools are used to find a good feasible solution that serves as a first guess for further optimization of sectors and operational settings, to achieve feasible solutions with better cost of implementation, demand similarity among DMAs, and better pressure uniformity in operations. A multiobjective agent swarm optimization framework is used to iteratively change the sectors at the boundaries. For the final configuration, sequential linear programming is used to find optimal valve and pump settings. [ABSTRACT FROM AUTHOR]