Stochastic model predictive control approaches applied to drinking water networks

Abstract:

Control of drinking water networks is an arduous task, given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances due to the stochastic water demand/consumption and optimize operational costs, this paper proposes three stochastic model predictive control (MPC) approaches, namely, chance-constrained MPC, tree-based MPC, and multiple-scenario MPC. A comparative assessment of these approaches is performed when they are applied to real case studies, specifically, a sector and an aggregate version of the Barcelona drinking water network in Spain. Copyright © 2016 John Wiley & Sons, Ltd.

Año de publicación:

2017

Keywords:

• management of water systems
• system disturbances
• stochastic programming
• Model Pbkp_redictive Control

Fuente:

scopus