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Design and Assessment of Energetic Agility Measures in Factories Based on Multivariate Linear Regression


Publication Year: 2019

Author(s): Kuhlmann T, Sauer A

Abstract:

The increase of volatile energy in the electricity grid due to the energy transition poses risks for industrial energy supply. Industrial power supply systems have to adapt to the impending turbulence on the energy market as well as the agility of the influences on the manufacturing systems. Furthermore, developments such as e-mobility or interlinking different energy sources provide challenges to the industrial power supply. The design and assessment of possible measures addressing these changes is a challenging task for planners of industrial energy systems. The presented method first introduces a stepwise procedure of designing agile measures for the factory's energy system. Following the designing phase, the benefit assessment method based on multivariate linear regression analysis in combination with Monte Carlo simulation is described. Finally, a case study demonstrates the usability of the overall method and shows first benefits of an agile energy system. Therefore, the presented method is suitable for evaluating measures that promote agility despite planning uncertainty. With regard to the costs, it is necessary to estimate the additional costs which result from an agile design.

Source of Publication: Procedia CIRP

Vol/Issue: 80:162-167p.

DOI No.: 10.1016/j.procir.2019.01.009

Publisher/Organisation: Elsevier B.V.

Rights: CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/)

URL:
https://www.sciencedirect.com/science/article/pii/S2212827119300113/pdf?md5=84a4272adb6c11068e4895366b36d217&pid=1-s2.0-S2212827119300113-main.pdf

Theme: Sustainable transportation | Subtheme: Energy

Tags: E-mobility, Energy efficiency, Energy systems, Renewable Energy, Agile energy system, Production, Agile manufacturing systems, Electric power systems, Intelligent systems, Life cycle, Monte Carlo methods, Benefit assessments, Different energy sources, Industrial energy system, Industrial power supplies, Multivariate linear regression analysis

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