LAPSE:2023.13173
Published Article
LAPSE:2023.13173
Methodology for Generating Synthetic Load Profiles for Different Industry Types
Anna Sandhaas, Hanhee Kim, Niklas Hartmann
February 28, 2023
Abstract
To achieve its climate goals, the German industry has to undergo a transformation toward renewable energies. To analyze this transformation in energy system models, the industry’s electricity demands have to be provided in a high temporal and sectoral resolution, which, to date, is not the case due to a lack of open-source data. In this paper, a methodology for the generation of synthetic electricity load profiles is described; it was applied to 11 industry types. The modeling was based on the normalized daily load profiles for eight electrical end-use applications. The profiles were then further refined by using the mechanical processes of different branches. Finally, a fluctuation was applied to the profiles as a stochastic attribute. A quantitative RMSE comparison between real and synthetic load profiles showed that the developed method is especially accurate for the representation of loads from three-shift industrial plants. A procedure of how to apply the synthetic load profiles to a regional distribution of the industry sector completes the methodology.
Keywords
electrical load profiles, energy system modeling, industrial load profiles, industry
Suggested Citation
Sandhaas A, Kim H, Hartmann N. Methodology for Generating Synthetic Load Profiles for Different Industry Types. (2023). LAPSE:2023.13173
Author Affiliations
Sandhaas A: Institute of Sustainable Energy Systems, Offenburg University of Applied Sciences, 77652 Offenburg, Germany [ORCID]
Kim H: Institute of Sustainable Energy Systems, Offenburg University of Applied Sciences, 77652 Offenburg, Germany
Hartmann N: Institute of Sustainable Energy Systems, Offenburg University of Applied Sciences, 77652 Offenburg, Germany
Journal Name
Energies
Volume
15
Issue
10
First Page
3683
Year
2022
Publication Date
2022-05-17
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15103683, Publication Type: Journal Article
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LAPSE:2023.13173
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https://doi.org/10.3390/en15103683
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