Procedure Model for Data-Driven Decision Support Regarding the Integration of Renewable Energies into Industrial Energy Management

Authors: M. Graus, K. Westhoff, X. Xu

Abstract:

The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: Data analytics, green production, industrial energy management, optimization, renewable energies, simulation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128113

References:

[1] Bundesministerium für Wirtschaft und Energie (BMWi), „Energie der Zukunft – Vierter Monitoring-Bericht zur Energiewende – Langfassung“, online available at: https://www.bmwi.de/DE/Mediathek/publikationen,did=739122.html, 2015, p. 4 et seq.
[2] Acatech, Forschungsunion, “Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0”, online available at: https://www.bmbf.de/files/Umsetzungsempfehlungen_Industrie4_0.pdf, 2013, pp. 23-31.
[3] K. Bleicher, “Das Konzept Integriertes Management”, Frankfurt/Main, Campus Verlag Frankfurt/New York, 2004, pp. 80-84.
[4] Fadi Shrouf, Joaquin Ordieres-Meré, Alvaro García-Sánchez, Miguel Ortega-Mier, “Optimizing the production scheduling of a single machine to minimize total energy consumption costs”, in Journal of Cleaner Production 67, 2014, pp. 197-207.
[5] S. Bougain, D. Gerhard, C. Nigischer, S. Ugurlu, “Towards energy management in production planning software based on energy consumption as a planning resource”, in Procedia CIRP 26, 2015, pp. 139-144
[6] M. J. Schniederjans, D. Schniederjans, C. M. Starkey, „Business Analytics Principles, Concepts, and Applications: What, Why and How.”, Upper Saddle River, New Jersey, Pearson Education, 2014, pp. 96-99.
[7] N. Lin, „Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics”, Upper Saddle River, New Jersey, 2015, pp. XVI et seq.
[8] M. Graus “Integration einer Datenanalytik in Energieinformationssysteme produzierender Unternehmen”, in Smart Energy 2016, pp. 44-55.
[9] M. Roscher, M. Graus, “Energiewende in der Industrie”, in VDE Kongress 2016, Internet der Dinge, 07. -08.11.2016 in Mannheim, 2016.
[10] R. Agrawal, K. Lin, H. S. Sawhney, K. Shim, “Fast similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases”, in Proceedings of 21th International Conference on Very Large Data Bases. Zurich, 1995, pp. 490-500.
[11] N. Löhndorf, M. Riel, S. Minner, “Simulation Optimization for the Stochastic Economic Lot Scheduling Problem with Sequence-Dependent Setup Times”, in International Journal of Production Economics, Volume 157, 2014, pp. 170-176.