DMA Segmentation and Multiobjective Optimization for Trading off Water Age, Excess Pressure, and Pump Operational Cost in Water Distribution Systems

This study presents a heuristic multiobjective approach for segmenting and operating water distribution systems (WDS). The methodology employs a two-pronged strategy: the first is a heuristic method for dividing the network into clusters (i.e., district metering areas) based on connectivity analysis. The second is the application of the evolutionary multiobjective optimization method non-dominated sorting genetic algorithm (NSGA)-II for trading off the operational cost, excess pressure (serving as a proxy to leakage reduction), and water age (acting as a surrogate to water quality) in the WDS. Three example applications of increasing complexities with various cluster partitioning are explored, showing a clear trade-off among the objectives. This study introduces an unprecedented heuristic approach for jointly solving the multiobjective problem under a given system partitioning. However, by enforcing a priori clustering formation (rather than including it in the optimization), optimality, completeness, and precision are compromised in favor of computational speed and effort. Thus, additional sensitivities need to be conducted outside of the optimization for the clusters' impact. Challenges of extending this study are in embedding the clusters' formations in the optimization considering other objectives such as residual capacity, developments of other optimization frameworks outside of the generic link of simulation-optimization, and uncertainty inclusion (e.g., in demands). All data and codes are included for allowing full replications and comparisons.