Record link: 
http://hdl.handle.net/20.500.12708/193242
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Title: 
FIWARE-based Architecture for Smart Local Energy Communities
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Citation: 
Thoma, M., Steindl, G., & Kastner, W. (2023). FIWARE-based Architecture for Smart Local Energy Communities. In 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE). 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki-Espoo, Finland. IEEE. https://doi.org/10.1109/ISIE51358.2023.10228053
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Publisher DOI: 
10.1109/ISIE51358.2023.10228053
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Thoma, Max 
Steindl, Gernot 
Kastner, Wolfgang 
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Organisational Unit: 
E191-03 - Forschungsbereich Automation Systems 
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Published in: 
2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE) 
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ISBN: 
979-8-3503-9971-4
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DOI of the book: 
10.1109/ISIE51358.2023
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Date (published): 
31-Aug-2023
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Event name: 
2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE) 
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Event date: 
19-Jun-2023 - 21-Jun-2023
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Event place: 
Helsinki-Espoo, Finland
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Number of Pages: 
4
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Publisher: 
IEEE
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Peer reviewed: 
Yes
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Keywords: 
energy communities; energy flow optimization; virtual commissioning
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Abstract: 
In this paper, we propose a system architecture based on the FIWARE framework for Smart Local Energy Communities (SLECs). An SLEC utilizes real-time data from energy producers and consumers to actively optimize the energy flow within the community. The Next Generation Service Interface-Linked Data (NGSI-LD) data model is used to provide meta and context information within the framework to enable an adaptive optimizing algorithm. Furthermore, the architecture utilizes a co-simulation framework for the virtual commissioning of new devices within an SLEC. This allows the exploration of device integration or new control strategies before they are deployed in the real system.
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Project title: 
Demonstration einer partizipativ gestalteten erneuerbaren Energiegemeinschaft zur Erhöhung der Resilienz: 884792 (FFG - Österr. Forschungsförderungs- gesellschaft mbH) 
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Link (external): 
https://ieeexplore.ieee.org/document/10228053
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Research Areas: 
Automation and Robotics: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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