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Stochastic model predictive control approaches applied to drinking water networks
Author(s) -
Grosso Juan M.,
Velarde Pablo,
OcampoMartinez Carlos,
Maestre José M.,
Puig Vicenç
Publication year - 2016
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2269
Subject(s) - model predictive control , task (project management) , computer science , water supply , control (management) , tree (set theory) , consumption (sociology) , operations research , mathematical optimization , engineering , environmental engineering , artificial intelligence , mathematics , mathematical analysis , social science , systems engineering , sociology
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.