Minimizing Pumping Energy Cost in Real-time Operations of Water Distribution Systems using Economic Model Predictive Control

TL;DR

This paper introduces a novel economic model predictive control framework for real-time water distribution system management, optimizing pump operations to minimize energy costs while satisfying water demands and considering variable electricity tariffs.

Contribution

The paper presents a new EMPC approach that incorporates pump number decisions and uses integer programming for cost-effective water system operation.

Findings

Significant reduction in pumping energy costs compared to trigger-level control.

Effective handling of time-varying electricity tariffs in pump scheduling.

Successful application to a benchmark water distribution network.

Abstract

Optimizing pump operations is a challenging task for real-time management of water distribution systems (WDSs). With suitable pump scheduling, pumping costs can be significantly reduced. In this research, a novel economic model predictive control (EMPC) framework for real-time management of WDSs is proposed. Optimal pump operations are selected based on predicted system behavior over a receding time horizon with the aim to minimize the total pumping energy cost. Time-varying electricity tariffs are considered while all the required water demands are satisfied. The novelty of this framework is to choose the number of pumps to operate in each pump station as decision variables in order to optimize the total pumping energy costs. By using integer programming, the proposed EMPC is applied to a benchmark case study, the Richmond Pruned network. The simulation with an EPANET hydraulic simulator is implemented. Moreover, a comparison of the results obtained using the proposed EMPC with those obtained using trigger-level control demonstrates significant economic benefits of the proposed EMPC.