Synthetic Load Profile Generation for Production Chains in Energy Intensive Industrial Subsectors
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Abstract
The generation of synthetic load profiles offers the possibility to easily
and efficiently depict the dynamic energy consumption and generation of single consumers.
Therefore, it is vital for evaluating future challenges for the physical energy system, support
the forecast models of grid operators and energy suppliers and improve deriving demand side
management measures for consumers. In this paper, we present Ganymed as a suitable
software for assessing energy consumption and generation behaviour of production chains in
energy intensive industrial subsectors. A dynamic user interface allows a swift and easy
application and adaption of processes and production routes. The underlying methodology is
based upon discrete-event simulation as a case study is applied to prove the functionality of
Ganymed. Within this case study, we modelled a part of a production chain of an existing
cement plant and compared the generated load profiles to measured ones. The results show
good approximations to the measured load profile with an average deviation of 4.1%.
and efficiently depict the dynamic energy consumption and generation of single consumers.
Therefore, it is vital for evaluating future challenges for the physical energy system, support
the forecast models of grid operators and energy suppliers and improve deriving demand side
management measures for consumers. In this paper, we present Ganymed as a suitable
software for assessing energy consumption and generation behaviour of production chains in
energy intensive industrial subsectors. A dynamic user interface allows a swift and easy
application and adaption of processes and production routes. The underlying methodology is
based upon discrete-event simulation as a case study is applied to prove the functionality of
Ganymed. Within this case study, we modelled a part of a production chain of an existing
cement plant and compared the generated load profiles to measured ones. The results show
good approximations to the measured load profile with an average deviation of 4.1%.
Details
Originalsprache | Deutsch |
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Titel | Konferenzband - EnInnov 2022 |
Status | Veröffentlicht - 18 Feb. 2022 |
Veranstaltung | 17. Symposium Energieinnovation 2022EnInnov2022 - Online, Graz, Österreich Dauer: 16 Feb. 2022 → 18 Feb. 2022 https://www.tugraz.at/events/eninnov2022/home/ |
Konferenz
Konferenz | 17. Symposium Energieinnovation 2022EnInnov2022 |
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Kurztitel | EnInnov2022 |
Land/Gebiet | Österreich |
Ort | Graz |
Zeitraum | 16/02/22 → 18/02/22 |
Internetadresse |