Journal Article

FZJ-2024-01942

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

Holtwerth, A. (Corresponding author)FZJ* ; Xhonneux, A.FZJ* ; Müller, D.FZJ*

2024
Elsevier Amsterdam

This record in other databases:    

Please use a persistent id in citations: doi:10.1016/j.ecmx.2024.100561  doi:10.34734/FZJ-2024-01942

Abstract: The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings. This work focuses on evaluating potential improvements in the operational strategy for a hybrid battery-hydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rule-based controller to show the potential benefits of model predictive control compared to a conventional approach. Furthermore, an in-depth analysis of different factors that impact the effectiveness of the model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource sizes on the performance relative to a rule-based controller is conducted. The model predictive controller reduced the energy consumption by at least 3.9 % and up to 17.9% compared to a rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horions are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.

Keyword(s): Energy (1st) ; Others (2nd)

---

Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112) (POF4-112)
2. 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112) (POF4-112)
3. LLEC::P2G++ / Saisonale Speicherung in gekoppelten, regenerativen Energiesystemen mittels Power-to-Gas (P2G): Demonstration großskaliger Wasserstoffspeicherung mittels innovativer LOHC-Technologie im Verbund mit einer KWK-Anlage, dynamischer Pipeline und (03SF0573) (03SF0573)
4. LLEC - Living Lab Energy Campus (LLEC-2018-2023) (LLEC-2018-2023)

Appears in the scientific report 2024

---

Database coverage:
; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; IF >= 5 ; JCR ; Mirror Journal ; SCOPUS ; Web of Science Core Collection

---