Control of a PVT-heat-pump-system based on reinforcement learning : operating cost reduction through flow rate variation

Publikationstyp
Journal Article
Publikationsdatum
2022-04-02
Sprache
English
Author
John, Daniel 
Kaltschmitt, Martin 
Institut
Umwelttechnik und Energiewirtschaft V-9 
DOI
10.15480/882.4316
TORE-URI
http://hdl.handle.net/11420/12389
Enthalten in
Energies 
Volume
15
Issue
7
Article Number
2607
Citation
Energies 15 (7): 2607 (2022)
Publisher DOI
10.3390/en15072607
Scopus ID
2-s2.0-85128010290
Publisher
Multidisciplinary Digital Publishing Institute
This study aims to develop a controller to operate an energy system-consisting of a photovoltaic thermal (PVT) system combined with a heat pump, using the reinforcement learning approach to minimize the operating costs of the system. For this, the flow rate of the cooling fluid pumped through the PVT system is controlled. This flow rate determines the temperature increase of the cooling fluid while reducing the temperature of the PVT system. The heated-up cooling fluid is used to improve the heat pump’s coefficient of performance (COP). For optimizing the operation costs of such a system, first an extensive simulation model has been developed. Based on this technical model, a controller has been developed using the reinforcement learning approach to allow for a cost-efficient control of the flow rate. The results show that a successfully trained control unit based on the reinforcement learning approach can reduce the operating costs with an independent validation dataset. For the case study presented here, based on the implemented methodological approach, including hyperparameter optimization, the operating costs of the investigated energy system can be reduced by more than 4% in the training dataset and by close to 3% in the validation dataset.
Schlagworte
PVT
reinforcement learning
solar-assisted heat pump
control approaches
operating cost analysis
DDC Class
600: Technik
620: Ingenieurwissenschaften
Projekt(e)
Open-Access-Publikationskosten / 2022-2024 / Technische Universität Hamburg (TUHH) 
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG) 
More Funding Information
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Projektnummer 491268466 and the Hamburg University of Technology (TUHH) in the funding programme Open Access Publishing.
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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