Streaming Smart Meter Data Integration to Enable Dynamic Demand Assignment for Real-Time Hydraulic Simulation

Abstract

Water distribution system models have long been widely used for design and planning purposes. Their application for supporting real-time operational decisions has been also gaining increasing interest over the past decade. Accurate end-user nodal demands are critical to the reliability of hydraulic simulations for real-time decision support. Conventionally, nodal demands are set to a handful of periodically updated ensembles of demand patterns, which cannot represent the vast heterogeneity and volatility of demands. With advances in metering technology, consumption data with unprecedentedly high temporal and spatial resolutions are available to water utilities on a real-time basis. A framework is developed here to create a dynamic demand assignment hydraulic model, in which consumption data are assigned to nodes to update the water network model with the streaming data from the data center and without interruption of the hydraulic simulation run. The developed framework is cloud-based and scalable, making it suitable for water distribution systems of all sizes. The framework modifies the core EPANET engine to directly assign updated demands in order to overcome current software limitations. The model is applied and demonstrated using a real-world case study in the US. The results show the importance of the real-time demand assignment for the reliability of hydraulic models for making real-time operational decisions and the realization of digital twins of water infrastructure systems.