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A framework for researching energy optimization of factory operations

Grosch, Benedikt Emanuel ; Ranzau, Heiko ; Dietrich, Bastian ; Kohne, Thomas ; Fuhrländer-Völker, Daniel ; Sossenheimer, Johannes ; Lindner, Martin ; Weigold, Matthias (2024)
A framework for researching energy optimization of factory operations.
In: Energy Informatics, 2022, 5 (Suppl 1)
doi: 10.26083/tuprints-00026611
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: A framework for researching energy optimization of factory operations
Language: English
Date: 10 September 2024
Place of Publication: Darmstadt
Year of primary publication: 2022
Place of primary publication: Cham
Publisher: Springer Nature
Journal or Publication Title: Energy Informatics
Volume of the journal: 5
Issue Number: Suppl 1
Collation: 13 Seiten
DOI: 10.26083/tuprints-00026611
Corresponding Links:
Origin: Secondary publication service
Abstract:

Energy optimization of factory operations has gained increasing importance over recent years since it is understood as one way to counteract climate change. At the same time, the number of research teams working on energy-optimized factory operations has also increased. While many tools are useful in this area, our team has recognized the importance of a comprehensive framework to combine functionality for optimization, simulation, and communication with devices in the factory. Therefore, we developed a framework that provides a standardized interface to research energy-optimized factory operations with a rolling horizon approach. The optimization part of the framework is based on the OpenAI gym environment. The framework also provides connectors for multiple communication protocols including Open Platform Communication Unified Architecture and Modbus via Transmission Control Protocol. These facilities can be utilized to implement rolling horizon optimizations for factory systems easily and directly control devices in the factory with the optimization results. In this article, we present the framework and show some examples to prove the effectiveness of our approach.

Uncontrolled Keywords: Industrial internet of things, Industrial demand side integration, Rolling horizon optimization
Identification Number: 29
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-266117
Additional Information:

The authors gratefully acknowledge fnancial support of the Project “KI4ETA” (Grant Number 03EN2053A) by the German Federal Ministry for Economic Afairs.

Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 650 Management
Divisions: 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW)
Date Deposited: 10 Sep 2024 07:37
Last Modified: 10 Sep 2024 07:38
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/26611
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