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Experimental operation of a solar‐driven climate system with thermal energy storages using mixed‐integer nonlinear model predictive control
Author(s) -
Bürger Adrian,
Bull Daniel,
Sawant Parantapa,
Bohlayer Markus,
Klotz Andreas,
Beschütz Daniel,
AltmannDieses Angelika,
Braun Marco,
Diehl Moritz
Publication year - 2021
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.2728
Subject(s) - model predictive control , nonlinear system , integer (computer science) , control theory (sociology) , work (physics) , computer science , thermal , nonlinear model , environmental science , engineering , control (management) , meteorology , mechanical engineering , physics , artificial intelligence , quantum mechanics , programming language
This work presents the results of experimental operation of a solar‐driven climate system using mixed‐integer nonlinear model predictive control (MPC). The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed‐integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed‐integer nonlinear MPC application.