Automation architecture for harnessing the demand response potential of aqueous parts cleaning machines

Author:

Fuhrländer-Völker DanielORCID,Magin Jonathan,Weigold Matthias

Abstract

AbstractTo reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory.

Funder

Bundesministerium für Bildung und Forschung

Bundesministerium für Wirtschaft und Energie

Technische Universität Darmstadt

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Reference37 articles.

1. Xu X, Wei Z, Ji Q, Wang C, Gao G (2019) Global renewable energy development: influencing factors, trend predictions and countermeasures. Resour Policy 63:101470. https://doi.org/10.1016/j.resourpol.2019.101470

2. Strobel N, Fuhrländer-Völker D, Weigold M, Abele E (2020) Quantifying the demand response potential of inherent energy storages in production systems. Energies 13(16):4161. https://doi.org/10.3390/en13164161

3. Eurostat: supply, transformation and consumption of electricity (2021). https://ec.europa.eu/eurostat/databrowser/view/nrg_cb_e/. Accessed 14 Dec 2021

4. U.S. Energy Information Administration: Monthly Energy Review April 2021: 7. Electricity, Washington, DC. https://www.eia.gov/totalenergy/data/monthly/. Accessed 9 May 2021

5. Schraml P (2018) Methode zur Reduktion Maximaler Elektrischer Lasten Spanender Werkzeugmaschinen: Dissertation. Schriftenreihe des PTW. Shaker, Düren

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