title: Automation Architecture for Demand Response on Aqueous Parts Cleaning Machines
Creators: name: Fuhrländer-Völker, Daniel
Publisher: name: UNSPECIFIED
Year: 2023

description: The share of fluctuating renewable energy in the electricity grid is increasing strongly, primarily in industrialised countries. The industrial sector accounts for a large share of the total electrical energy consumption and current research shows that it is possible to adapt this energy consumption to the fluctuating renewable energy generation using demand response (DR). Especially aqueous parts cleaning machines have a high DR potential. However, only a few approaches exist that show how DR measures can be implemented on real production machines. This work develops a method that enables the execution of DR measures on aqueous parts cleaning machines. The so-called demand response automation architecture design (DRAAD) method consists of a DR potential analysis, a DR automation architecture including a DR automation program and a DR data model, as wall 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 DR potential analysis uses only the machine documentation and simple calculations, such that it can be carried out by employees of the machine manufacturer. The framework for the DR automation architecture is a cyber-physical production system. This consists of the physical aqueous parts cleaning machine, its digital twin, external elements such as the energy market and a cyber-physical interface representing the communication in the cyber-physical production system. The digital twin includes the DR automation program, the DR data model and the DR process model, which is used by the DR control algorithm, in the digital master. The digital twin also includes a digital shadow and digital services. The object-oriented DR automation program implements sensor, actuator, and system objects as well as functions that enable the execution of the DR methods store energy inherently and interrupt process. In addition to DR functions, functional safety functions are included. The communication between DR automation program and DR control algorithm is modelled in the DR data model. This includes all data points needed for the calculation (observing) and execution (controlling) of the two DR actions. The DR control algorithm, a model predictive control algorithm, minimises the energy cost of the aqueous parts cleaning machine based on varying energy prices. Both DR measures are implemented and the approach is scalable and transferable to diff erent aqueous parts cleaning machines. The DRAAD method is applied and validated on the aqueous parts cleaning machine MAFAC KEA in the ETA research factory. The DR potential analysis of the machine results in a DR power potential of 87 % of the machine’s rated power for store energy inherently and a DR energy power potential of 99 % of the energy consumption of the reference cleaning process for interrupt process. In the field test, a power change of 49 % and an energy shift of 82 % can be retrieved.