Stochastic model predictive control approaches applied to drinking water networks
Journal Article (2017)
Optimal Control Applications and Methods
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.
automation, control theory, optimisation.
management of water systems; model predictive control; stochastic programming; system disturbances
J.M. Grosso, P. Velarde, C. Ocampo-Martínez, J.M. Maestre and V. Puig. Stochastic model predictive control approaches applied to drinking water networks. Optimal Control Applications and Methods, 2017, to appear.