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  5. Cost optimized scheduling in modular electrolysis plants

Cost optimized scheduling in modular electrolysis plants

Publication date
2024-06-05
Document type
Konferenzbeitrag
Author
Henkel, Vincent  
Kilthau, Maximilian  
Gehlhoff, Felix  
Wagner, Lukas  
Fay, Alexander  
Organisational unit
Automatisierungstechnik  
DOI
10.1109/icit58233.2024.10540907
URI
https://openhsu.ub.hsu-hh.de/handle/10.24405/24227
Scopus ID
2-s2.0-85191577398
Conference
25th International Conference on Industrial Technology (ICIT 2024) ; Bristol, United Kingdom ; March 25–27, 2024
Project
eModule (03HY116)
Publisher
IEEE
Book title
2024 IEEE International Conference on Industrial Technology (ICIT)
ISBN
979-8-3503-4026-6
Is part of
https://openhsu.ub.hsu-hh.de/handle/10.24405/24231
Peer-reviewed
✅
Part of the university bibliography
✅
Additional Information
Language
English
Keyword
Alternating Direction Method of Multipliers
Modular Electrolysis Plants
Multi-Agent System
Abstract
In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.
Version
Published version
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