Author(s): Ernesto Arandia; Lu Xing; Jim Uber; Ehsan Shafiee

Linked Author(s): M. Ehsan Shafiee

Keywords: Dynamic water network optimization; Nonlinear predictive control; Operation optimization

Abstract: This paper aims to create a network monitoring and management system that optimizes energy cost through real-time demand forecasts and control. The proposed system is based on a digital twin of the distribution system that integrates the real-time SCADA data and a nonlinear model predictive control (NMPC) optimization framework that determines pumping operations and storage levels to minimize energy costs. The proposed framework is applied to optimize the annual operation of the Southern Nevada Water Authority. The results show that the optimized control can lead to a 10% to 20% reduction in energy costs.

DOI: https://doi.org/10.3850/iahr-hic2483430201-508

Year: 2024