Synthetic load profile generation for production chains in energy intensive industrial subsectors via a bottom-up approach

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Synthetic load profile generation for production chains in energy intensive industrial subsectors via a bottom-up approach. / Binderbauer, Paul Josef; Kienberger, Thomas; Staubmann, Thomas.
in: Journal of Cleaner Production, Jahrgang 331.2022, Nr. 10 January, 130024, 10.01.2022.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

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@article{4459598940964fc6bb9aadb124bc283f,
title = "Synthetic load profile generation for production chains in energy intensive industrial subsectors via a bottom-up approach",
abstract = "Iron & Steel, Pulp & Paper, Non-Metallic Minerals and Chemical & Petrochemical are the most energy intensive subsectors, even though they utilise only a limited range of production processes compared to other sectors like Machinery or Food & Beverages. To support future efforts for decarbonising the European industry, this study aims to develop a methodology to correctly and dynamically depict all relevant production processes of the mentioned subsectors and to generate synthetic load profiles (LP)1 based upon their consumption and generation behaviour. In a first step, the energy intensive subsectors and their main production processes are identified. A standardised research approach is used to correctly depict their characteristics e.g. runtime, energy consumption and generation, unit sizes etc. Next, a methodology for modelling the timely behaviour of these production processes and for generating synthetic LPs is developed. This method is based upon the bottom-up approach of discrete-event simulation combined with stochastics. The developed methodology is then implemented into the simulation software Ganymed. Finally, the results of this methodology are validated via a case study, modelling the primary steel production route of an Austrian steel mill. In overall, the synthetic electricity LP shows good approximations to the measured one with a mean absolute percentage error of 6.08% for the simulated five days in total. However, a stronger deviation of the generated LP compared to the measured counterpart can be noted at the last two days. This deviation results from a reduction of the capacity during the real life production. This, however, can be taken into account in the synthetic generation given a more extensive data basis. Consequently, Ganymed can be deemed as a suitable software for generating energy consumption and generation behaviour of processes and production chains of energy intensive industries.",
keywords = "Energy model, Industry, Load profile, Load profile generation, Simulation",
author = "Binderbauer, {Paul Josef} and Thomas Kienberger and Thomas Staubmann",
note = "Publisher Copyright: {\textcopyright} 2021",
year = "2022",
month = jan,
day = "10",
doi = "10.1016/j.jclepro.2021.130024",
language = "English",
volume = "331.2022",
journal = "Journal of Cleaner Production",
issn = "0959-6526",
publisher = "Elsevier",
number = "10 January",

}

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TY - JOUR

T1 - Synthetic load profile generation for production chains in energy intensive industrial subsectors via a bottom-up approach

AU - Binderbauer, Paul Josef

AU - Kienberger, Thomas

AU - Staubmann, Thomas

N1 - Publisher Copyright: © 2021

PY - 2022/1/10

Y1 - 2022/1/10

N2 - Iron & Steel, Pulp & Paper, Non-Metallic Minerals and Chemical & Petrochemical are the most energy intensive subsectors, even though they utilise only a limited range of production processes compared to other sectors like Machinery or Food & Beverages. To support future efforts for decarbonising the European industry, this study aims to develop a methodology to correctly and dynamically depict all relevant production processes of the mentioned subsectors and to generate synthetic load profiles (LP)1 based upon their consumption and generation behaviour. In a first step, the energy intensive subsectors and their main production processes are identified. A standardised research approach is used to correctly depict their characteristics e.g. runtime, energy consumption and generation, unit sizes etc. Next, a methodology for modelling the timely behaviour of these production processes and for generating synthetic LPs is developed. This method is based upon the bottom-up approach of discrete-event simulation combined with stochastics. The developed methodology is then implemented into the simulation software Ganymed. Finally, the results of this methodology are validated via a case study, modelling the primary steel production route of an Austrian steel mill. In overall, the synthetic electricity LP shows good approximations to the measured one with a mean absolute percentage error of 6.08% for the simulated five days in total. However, a stronger deviation of the generated LP compared to the measured counterpart can be noted at the last two days. This deviation results from a reduction of the capacity during the real life production. This, however, can be taken into account in the synthetic generation given a more extensive data basis. Consequently, Ganymed can be deemed as a suitable software for generating energy consumption and generation behaviour of processes and production chains of energy intensive industries.

AB - Iron & Steel, Pulp & Paper, Non-Metallic Minerals and Chemical & Petrochemical are the most energy intensive subsectors, even though they utilise only a limited range of production processes compared to other sectors like Machinery or Food & Beverages. To support future efforts for decarbonising the European industry, this study aims to develop a methodology to correctly and dynamically depict all relevant production processes of the mentioned subsectors and to generate synthetic load profiles (LP)1 based upon their consumption and generation behaviour. In a first step, the energy intensive subsectors and their main production processes are identified. A standardised research approach is used to correctly depict their characteristics e.g. runtime, energy consumption and generation, unit sizes etc. Next, a methodology for modelling the timely behaviour of these production processes and for generating synthetic LPs is developed. This method is based upon the bottom-up approach of discrete-event simulation combined with stochastics. The developed methodology is then implemented into the simulation software Ganymed. Finally, the results of this methodology are validated via a case study, modelling the primary steel production route of an Austrian steel mill. In overall, the synthetic electricity LP shows good approximations to the measured one with a mean absolute percentage error of 6.08% for the simulated five days in total. However, a stronger deviation of the generated LP compared to the measured counterpart can be noted at the last two days. This deviation results from a reduction of the capacity during the real life production. This, however, can be taken into account in the synthetic generation given a more extensive data basis. Consequently, Ganymed can be deemed as a suitable software for generating energy consumption and generation behaviour of processes and production chains of energy intensive industries.

KW - Energy model

KW - Industry

KW - Load profile

KW - Load profile generation

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=85120878811&partnerID=8YFLogxK

U2 - 10.1016/j.jclepro.2021.130024

DO - 10.1016/j.jclepro.2021.130024

M3 - Article

AN - SCOPUS:85120878811

VL - 331.2022

JO - Journal of Cleaner Production

JF - Journal of Cleaner Production

SN - 0959-6526

IS - 10 January

M1 - 130024

ER -