Social Network Community Detection and Hybrid Optimization for Dividing Water Supply into District Metered Areas

Abstract

Water supply utilities need to properly manage their systems to guarantee a quality supply. One way to manage large systems is through division into district metered areas (DMAs). Graph clustering with an unknown number of subdivisions, as in social network theory, has proven highly efficient in this sectorization problem. Several physical and hydraulic features may easily be used as criteria to suitably divide the network. This paper uses social network community detection algorithms to define several DMA scenarios. Configurations mainly depend on nodal demand and elevation, but adaptations may be needed to guarantee full supply in future scenarios related to system growth—and rehabilitation actions may also be required. The problem associated with pipes and valves is first solved with three optimization methods. The best solutions then enter a new optimization process, in which tank dimensions and valve set points are defined. This complex optimization-segregation approach enables an improvement in the hydraulic efficiency of the E-Town network at an affordable cost, and this approach also determines the measures needed to meet the dry season requirements.