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Economic Reliability-Aware MPC-LPV based on Chance-Constraints Programming for Water Networks

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Title: Economic Reliability-Aware MPC-LPV based on Chance-Constraints Programming for Water Networks
Authors: Karimi Pour, Fatemeh, Puig Cayuela, Vicenç, Cembrano Gennari, Gabriela
Contributors: Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), European Commission, Ministerio de Economía y Competitividad (España), Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Consejo Superior de Investigaciones Científicas (CSIC)
Publisher Information: IEEE, 2020.
Publication Year: 2020
Subject Terms: 0209 industrial biotechnology, Control predictiu, Aigua -- Abastament, Informàtica::Automàtica i control [Àrees temàtiques de la UPC], Àrees temàtiques de la UPC::Informàtica::Automàtica i control, 02 engineering and technology, Predictive control, 0101 mathematics, 01 natural sciences, Water-supply
Description: This paper proposed an economic reliabilityaware model predictive control (MPC) for the management of drinking water transport networks (DWNs) that includes a new goal to increase the system and components reliability based on a finite horizon stochastic optimization problem with joint probabilistic (chance) constraints. The proposed approach is based on a single-layer economic optimization problem with dynamic constraints. The inclusion of components and system reliability in the MPC model using an LPV modelling approach aims at maximizing the availability of the system by estimating system reliability. The solution of the optimization problem related to the MPC problem is obtained by solving a series of Quadratic Programming (QP) problem. The use of chance-constraint programming allows computing an optimal policy based on a desirable risk acceptability level and managing dynamically volume tank stocks to cope with non-stationary flow demands. Finally, the proposed approach is applied to a part of a real drinking water transport network of Barcelona for demonstrating the performance of the method.
Document Type: Article
Conference object
File Description: application/pdf
DOI: 10.23919/ecc51009.2020.9143825
DOI: 10.13039/501100011033
DOI: 10.13039/501100000780
DOI: 10.13039/501100003329
Access URL: https://upcommons.upc.edu/bitstream/2117/340030/1/0417.pdf
http://hdl.handle.net/2117/340030
http://hdl.handle.net/10261/235571
https://ieeexplore.ieee.org/document/9143825
https://dblp.uni-trier.de/db/conf/eucc/eucc2020.html#PourPC20
http://digital.csic.es/bitstream/10261/235571/1/2432-Economic-reliability-aware-MPC-LPV-based-on-chance-constraints-programming-for-water-networks..pdf
https://digital.csic.es/handle/10261/235571
https://upcommons.upc.edu/handle/2117/340030
https://upcommons.upc.edu/bitstream/2117/340030/1/0417.pdf
Rights: STM Policy #29
CC BY NC ND
Accession Number: edsair.doi.dedup.....40eb91b51b39e3cb7630fa844d52a07c
Database: OpenAIRE
Description
Abstract:This paper proposed an economic reliabilityaware model predictive control (MPC) for the management of drinking water transport networks (DWNs) that includes a new goal to increase the system and components reliability based on a finite horizon stochastic optimization problem with joint probabilistic (chance) constraints. The proposed approach is based on a single-layer economic optimization problem with dynamic constraints. The inclusion of components and system reliability in the MPC model using an LPV modelling approach aims at maximizing the availability of the system by estimating system reliability. The solution of the optimization problem related to the MPC problem is obtained by solving a series of Quadratic Programming (QP) problem. The use of chance-constraint programming allows computing an optimal policy based on a desirable risk acceptability level and managing dynamically volume tank stocks to cope with non-stationary flow demands. Finally, the proposed approach is applied to a part of a real drinking water transport network of Barcelona for demonstrating the performance of the method.
DOI:10.23919/ecc51009.2020.9143825