Stochastic model predictive control approaches applied to drinking water networks - Grosso, Juan M.; Velarde, Pablo; Ocampo‐Martinez, Carlos; Maestre, José M.; Puig, Vicenç (Optimal control applications & methods, 2017)
Summary 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.