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 scenarios 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
