Automation Architecture for Demand Response on Aqueous Parts Cleaning Machines
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Beschreibung
Increasing renewable energy prompts the need for demand response (DR) in the industrial sector. Especially aqueous parts cleaning machines have a high DR potential, but there are limited strategies for implementing DR on real production machines. This work introduces the demand response automation architecture design (DRAAD) method for implementing DR measures on aqueous parts cleaning machines. DRAAD involves a DR potential analysis, a DR automation architecture including a DR automation program and a DR data model, as well as a DR control algorithm.
The DR potential analysis analyses the technical DR potential of the machine components for the DR method store energy inherently and of the cleaning process for the DR method interrupt process. The framework for the DR automation architecture is a cyber-physical production system, consisting of the physical machine and its digital twin. The digital twin includes the DR automation program, the DR data model, and the DR process model, used by the DR control algorithm.
The object-oriented DR automation program integrates sensor, actuator, and system objects to execute the DR methods of storing energy and interrupting the process, as well as functional safety functions. Communication with the DR control algorithm is modelled in the DR data model. The DR control algorithm, a model predictive control algorithm, minimises the energy cost of the aqueous parts cleaning machine based on varying energy prices.
The DRAAD method is tested on the MAFAC KEA cleaning machine at the ETA research factory. The machine's DR potential analysis reveals an 87% power potential for store energy inherently and a 99% energy consumption potential for interrupt process. In the field test, a 49% power change and an 82% energy shift are achieved.
Autor*in
Daniel Fuhrländer-Völker
Themen in »Automation Architecture for Demand Response on Aqueous Parts Cleaning Machines«
Cyber-physical production system digital twin data model model predictive control energy-flexibilit