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Experimental operation of a solar-driven climate system with thermal energy storages using mixed-integer nonlinear MPC

Adrian Bürger (adrian.buerger***at***hs-karlsruhe.de)
Daniel Bull (daniel.bull***at***hs-karlsruhe.de)
Parantapa Sawant (parantapa.sawant***at***hs-offenburg.de)
Markus Bohlayer (markus.bohlayer***at***hs-karlsruhe.de)
Andreas Klotz (klan1025***at***hs-karlsruhe.de)
Daniel Beschütz (beda1033***at***hs-karlsruhe.de)
Angelika Altmann-Dieses (angelika.altmann-dieses***at***hs-karlsruhe.de)
Marco Braun (marco.braun***at***hs-karlsruhe.de)
Moritz Diehl (moritz.diehl***at***imtek.uni-freiburg.de)

Abstract: 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.

Keywords: Model predictive control, mixed-integer nonlinear programming, renewable energy systems, solar thermal climate systems, experimental study

Category 1: Applications -- Science and Engineering (Control Applications )

Category 2: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

Category 3: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation: Preprint submitted to Optimal Control Applications and Methods

Download: [PDF]

Entry Submitted: 10/10/2019
Entry Accepted: 10/10/2019
Entry Last Modified: 10/10/2019

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