Adjustable Robust Optimization for Water Distribution System Operation Under Uncertainty

Abstract

AbstractThe optimal operation of water distribution systems (WDS) is a paramount objective for water utilities due to the substantial energy consumption associated with pumping. A major challenge in optimizing WDS operation is addressing uncertainties such as those related to consumer demands. Real‐time operation under uncertainty necessitates a dynamic approach that can utilize the newly observed information and adjust the operational policy accordingly. This study presents an adjustable robust optimization (ARO) approach to tackle this challenge. Unlike static optimization methods, ARO generates a decision rule policy that is dynamically adjusted as new data becomes available and the operational horizon evolves, thereby ensuring adaptability to changing conditions. Furthermore, the study includes a quantitative analysis of typical demand uncertainty that supports the formulation of the ARO model. The proposed method is evaluated through two case studies and compared with traditional folding horizon approaches. The results indicate that the ARO method is competitive with traditional methods in terms of objective value and surpasses them in terms of robustness. An additional advantage of the method is its offline operation capability which enables it to produce decision rules independent of real‐time programs. This feature facilitates various practical applications such as what‐if analyses, maintenance work planning, and preparation for other special events.