Title Stochastic model predictive control approaches applied to drinking water networks
Authors Grosso, Juan M. , VELARDE RUEDA, PABLO ANIBAL, Ocampo-Martinez, Carlos , Maestre, Jose M. , Puig, Vicenc
External publication Si
Means Optim Control Appl Methods
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 1.61400
SJR Impact 0.82500
Publication date 01/07/2017
ISI 000405077500005
DOI 10.1002/oca.2269
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 (c) 2016 John Wiley & Sons, Ltd.
Keywords management of water systems; model predictive control; stochastic programming; system disturbances
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