Chance-constrained model predictive control for drinking water networks

This paper addresses a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problem with joint probabilistic (chance) constraints. In this approach, water demands are considered additive s...

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Detalhes bibliográficos
Autores: Grosso Pérez, Juan Manuel|||0000-0002-4300-1500, Ocampo-Martínez, Carlos|||0000-0001-9251-6044, Puig Cayuela, Vicenç|||0000-0002-6364-6429, Joseph Duran, Bernat
Formato: artículo
Fecha de publicación:2014
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/23369
Acesso em linha:https://hdl.handle.net/2117/23369
https://dx.doi.org/10.1016/j.jprocont.2014.01.010
Access Level:acceso abierto
Palavra-chave:Water-supply -- Management -- Mathematical models
Chance constraints
Drinking water networks
MPC
Reliability
Robustness
Aigua -- Abastament -- Control
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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oai_identifier_str oai:upcommons.upc.edu:2117/23369
network_acronym_str ES
network_name_str España
repository_id_str
spelling Chance-constrained model predictive control for drinking water networksGrosso Pérez, Juan Manuel|||0000-0002-4300-1500Ocampo-Martínez, Carlos|||0000-0001-9251-6044Puig Cayuela, Vicenç|||0000-0002-6364-6429Joseph Duran, BernatWater-supply -- Management -- Mathematical modelsChance constraintsDrinking water networksMPCReliabilityRobustnessAigua -- Abastament -- ControlÀrees temàtiques de la UPC::Informàtica::Automàtica i controlThis paper addresses a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problem with joint probabilistic (chance) constraints. In this approach, water demands are considered additive stochastic disturbances with non-stationary uncertainty description, unbounded support and known (or approximated) quasi-concave probabilistic distribution. A deterministic equivalent of the stochastic problem is formulated using Boole's inequality to decompose joint chance constraints into single chance constraints and by considering a uniform allocation of risk to bound these later constraints. The resultant deterministic-equivalent optimisation problem is suitable to be solved with tractable quadratic programming (QP) or second order cone programming (SOCP) algorithms. The reformulation allows to explicitly and easily propagate uncertainty over the prediction horizon, and leads to a cost-efficient management of risk that consists in a dynamic back-off to avoid frequent violation of constraints. Results of applying the proposed approach to a real case study - the Barcelona DWN (Spain) - have shown that the network performance (in terms of operational costs) and the necessary back-off (to cope with stochastic disturbances) are optimised simultaneously within a single problem, keeping tractability of the solution, even in large-scale networks. The general formulation of the approach and the automatic computation of proper back-off within the MPC framework replace the need of experience-based heuristics or bi-level optimisation schemes that might compromise the trade-off between profits, reliability and computational burden. © 2014 Elsevier Ltd.Peer Reviewed20142014-05-0120142014-07-01journal articlehttp://purl.org/coar/resource_type/c_6501AOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/23369https://dx.doi.org/10.1016/j.jprocont.2014.01.010reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 318556 Efficient Integrated Real-time Monitoring and Control of Drinking Water Networksopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/233692026-05-27T15:37:01Z
dc.title.none.fl_str_mv Chance-constrained model predictive control for drinking water networks
title Chance-constrained model predictive control for drinking water networks
spellingShingle Chance-constrained model predictive control for drinking water networks
Grosso Pérez, Juan Manuel|||0000-0002-4300-1500
Water-supply -- Management -- Mathematical models
Chance constraints
Drinking water networks
MPC
Reliability
Robustness
Aigua -- Abastament -- Control
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Chance-constrained model predictive control for drinking water networks
title_full Chance-constrained model predictive control for drinking water networks
title_fullStr Chance-constrained model predictive control for drinking water networks
title_full_unstemmed Chance-constrained model predictive control for drinking water networks
title_sort Chance-constrained model predictive control for drinking water networks
dc.creator.none.fl_str_mv Grosso Pérez, Juan Manuel|||0000-0002-4300-1500
Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Joseph Duran, Bernat
author Grosso Pérez, Juan Manuel|||0000-0002-4300-1500
author_facet Grosso Pérez, Juan Manuel|||0000-0002-4300-1500
Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Joseph Duran, Bernat
author_role author
author2 Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Joseph Duran, Bernat
author2_role author
author
author
dc.subject.none.fl_str_mv Water-supply -- Management -- Mathematical models
Chance constraints
Drinking water networks
MPC
Reliability
Robustness
Aigua -- Abastament -- Control
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Water-supply -- Management -- Mathematical models
Chance constraints
Drinking water networks
MPC
Reliability
Robustness
Aigua -- Abastament -- Control
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description This paper addresses a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problem with joint probabilistic (chance) constraints. In this approach, water demands are considered additive stochastic disturbances with non-stationary uncertainty description, unbounded support and known (or approximated) quasi-concave probabilistic distribution. A deterministic equivalent of the stochastic problem is formulated using Boole's inequality to decompose joint chance constraints into single chance constraints and by considering a uniform allocation of risk to bound these later constraints. The resultant deterministic-equivalent optimisation problem is suitable to be solved with tractable quadratic programming (QP) or second order cone programming (SOCP) algorithms. The reformulation allows to explicitly and easily propagate uncertainty over the prediction horizon, and leads to a cost-efficient management of risk that consists in a dynamic back-off to avoid frequent violation of constraints. Results of applying the proposed approach to a real case study - the Barcelona DWN (Spain) - have shown that the network performance (in terms of operational costs) and the necessary back-off (to cope with stochastic disturbances) are optimised simultaneously within a single problem, keeping tractability of the solution, even in large-scale networks. The general formulation of the approach and the automatic computation of proper back-off within the MPC framework replace the need of experience-based heuristics or bi-level optimisation schemes that might compromise the trade-off between profits, reliability and computational burden. © 2014 Elsevier Ltd.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-05-01
2014
2014-07-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/23369
https://dx.doi.org/10.1016/j.jprocont.2014.01.010
url https://hdl.handle.net/2117/23369
https://dx.doi.org/10.1016/j.jprocont.2014.01.010
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 318556 Efficient Integrated Real-time Monitoring and Control of Drinking Water Networks
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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