Record link: 
http://hdl.handle.net/20.500.12708/204788
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Title: 
AI for Energy Intensive Industry: A Hybrid Optimization Approach for Flexibility Service Providers
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Citation: 
Fischer, M., & Hofmann, R. (2024). AI for Energy Intensive Industry: A Hybrid Optimization Approach for Flexibility Service Providers. In ASME 2024 18th International Conference on Energy Sustainability collocated with the ASME 2024 Heat Transfer Summer Conference and the ASME 2024 Fluids Engineering Division Summer Meeting. ASME 2024 18th International Conference on Energy Sustainability, Anaheim, CA, United States of America (the). https://doi.org/10.1115/ES2024-123705
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Publisher DOI: 
10.1115/ES2024-123705
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Fischer, Martin 
Hofmann, René 
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Organisational Unit: 
E302-03 - Forschungsbereich Industrielle Energiesysteme 
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Published in: 
ASME 2024 18th International Conference on Energy Sustainability collocated with the ASME 2024 Heat Transfer Summer Conference and the ASME 2024 Fluids Engineering Division Summer Meeting 
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ISBN: 
9780791887899
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Date (published): 
2024
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Event name: 
ASME 2024 18th International Conference on Energy Sustainability 
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Event date: 
15-Jul-2024 - 17-Jul-2024
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Event place: 
Anaheim, CA, United States of America (the)
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Number of Pages: 
6
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Peer reviewed: 
Yes
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Keywords: 
Flexibility; Genetic Algorithms; Hybrid Optimization; Particle Swarm Optimization
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Abstract: 
This paper presents a hybrid optimization method for industrial prosumers to act as flexibility service providers. The proposed method is applied to an industrial company from the energy intensive industry participating in the European Power Exchange and the Austrian balancing market, specifically focusing on automatic Frequency Restoration Reserve. A company in the food processing industry, with on-site electrical and thermal power generation is used to demonstrate the hybrid approach. There, metaheuristics and Mixed Integer Linear Programming are combined to solve a bidding strategy problem with a subsequent unit commitment problem. As metaheuristics, a Genetic Algorithm and Particle Swarm Optimization were applied and their results compared.
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Project title: 
Industry4Redispatch: 887780 (Klima- und Energiefonds) 
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Research Areas: 
Mathematical and Algorithmic Foundations: 20%
Modeling and Simulation: 10%
Climate Neutral, Renewable and Conventional Energy Supply Systems: 70%
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Science Branch: 
2030 - Maschinenbau: 70%
2020 - Elektrotechnik, Elektronik, Informationstechnik: 20%
1010 - Mathematik: 10%
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Appears in Collections:
Conference Paper

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