Fuhrländer-Völker, D.; Grosch, B.; Weigold, M.: Modelling and Control of Aqueous Parts Cleaning Machines for Demand Response. In: Herberger, D.; Hübner, M.; Stich, V. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 1. Hannover : publish-Ing., 2023, S. 790-800. DOI:
https://doi.org/10.15488/13498
Zusammenfassung: |
With the aim of enabling better utilization of renewable power and reducing the environmental impact of industrial sites, we propose an approach for implementing electric demand response. Cleaning machines provide significant potential for demand response due to their large water tanks, which can be used for thermal energy storage. Furthermore, many batch cleaning machines allow process interruptions without impacting the cleaning result. We show that utilizing inherent energy storage and process interruptions are practical ways to implement demand response.
Hence, we present a mathematical demand response model of an aqueous parts cleaning machine and integrate it in a cyber-physical production system. The mathematical demand response model is used to determine the energy consumption of the machine resulting from the cleaning process and the tank heater. The model is divided into an event-based part describing the individual steps of the cleaning process and a time-based part representing the energy required by the tank heater to satisfy specified tank temperature limits.
In addition to the mathematical model, we present the data model required for communication with the physical machine. We validate the mathematical model and the complete cyber-physical production system including a real machine in a field test in the ETA research factory for their demand response capabilities.
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Lizenzbestimmungen: |
CC BY 3.0 DE - http://creativecommons.org/licenses/by/3.0/de/
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Publikationstyp: |
BookPart |
Publikationsstatus: |
publishedVersion |
Erstveröffentlichung: |
2023 |
Schlagwörter (deutsch): |
Konferenzschrift
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Schlagwörter (englisch): |
Carbon Neutral Production, Energy-flexibility, Cyber-physical Production System, Model Predictive Control, Data Model, Inherent Energy Storage, Single Machine Scheduling
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Fachliche Zuordnung (DDC): |
620 | Ingenieurwissenschaften und Maschinenbau
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