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  5. Experimental setup for the evaluation of optimization strategies for flexible energy resources

Experimental setup for the evaluation of optimization strategies for flexible energy resources

Publication date
2024-02-19
Document type
Konferenzbeitrag
Author
Reinpold, Lasse Matthias  
Wagner, Lukas Peter  
Reiche, Leif-Thore  
Fay, Alexander  
Organisational unit
Automatisierungstechnik  
DOI
10.1109/oncon60463.2023.10430717
URI
https://openhsu.ub.hsu-hh.de/handle/10.24405/22746
Conference
2nd Industrial Electronics Society Annual On-Line Conference (ONCON 2023) ; Online ; December 8–10, 2023
Publisher
IEEE
Book title
2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON)
ISBN
979-8-3503-5797-4
Part of the university bibliography
✅
Additional Information
Language
English
Abstract
To counteract the intermittence of renewable energy generation, flexible energy resources can shift their energy consumption to times of abundant renewable energy availability. A high flexibility potential lies within industrial production facilities. To exploit this potential, optimization strategies can be employed to determine the optimal time and rate of production. While many optimization strategies have been proposed, only a small fraction of strategies are evaluated on real production resources. Therefore, this work proposes an experimental setup to evaluate optimization strategies before deploying them in an industrial production facility. The experimental setup consists of an experimental distillation unit, an optimization model to generate optimized operating schedules, and a simulation model of the distillation unit to test optimized schedules before running them on the real distillation unit. The development of the two models is described, and a case study is conducted to minimize the electricity costs of operating the real distillation unit. The models show good agreement with experimental results. The optimized schedule is executed by the distillation unit with an RMSE of less than 2%. The final production volume deviates from the optimized schedule by 2.4%.
Version
Published version
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