Simulation study to evaluate temporal aggregation and variability of stochastic water demands on distribution system hydraulics and transport

As utilities move toward more detailed hydraulic and water quality network models, the potential for the stochastic nature of consumptive demands having an effect on transport characteristics increases. A nonhomogeneous Poisson rectangular pulse model (PRPsym) was used to generate stochastic water demands aggregated at 1-min, 10-min, and 1-h time steps for a small skeletonized network and a large all-pipe network; results were then linked with EPANET to simulate the impacts of water-demand variability on the underlying transport and water quality. In general, the impacts of temporal aggregation-as represented by flow rate and concentration variability and arrival times-increased as the evaluations moved from the main trunk lines toward dead-end nodes or pipes. More specifically, analysis of a Monte Carlo ensemble of results demonstrated that (1) a 10-min temporal aggregation captured most of the arrival time variability relative to the 1-min aggregation step; (2) hourly demand variability (e.g., from one day to another) had a greater effect on transport characteristics than the intra-hour variability from temporal aggregation; and (3) in addition to the expected dead-end nodes and loops, transport characteristics within source blending zones were consistently impacted by stochastic demands at smaller temporal aggregations. A sensitivity analysis illustrated the demand variability associated with temporal aggregation was not further affected by the distribution of demand intensity, but the distribution of demand duration, particularly the variance of demand duration, further affected demand variability through the autocorrelation of demands across time steps. Using the sensitivity results to select parameters associated with greater demand variability, another Monte Carlo ensemble was developed for the large network that only slightly increased the spatial extent of nodes within the distribution system that resulted in significant changes in transport characteristics attributable to the effects of spatial aggregation. These results provide additional insight into the magnitude of impact from demand variability (from both modeling aggregation and daily variations) and the spatial extent of such effects on transport characteristics relative to the typical modeling assumptions of utilizing deterministic, 1-h demands.