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024 7 _ |a 10.1016/j.ecmx.2024.100561
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100 1 _ |a Holtwerth, Alexander
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245 _ _ |a Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming
260 _ _ |a Amsterdam
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520 _ _ |a 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.
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536 _ _ |a 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)
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700 1 _ |a Xhonneux, André
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700 1 _ |a Müller, Dirk
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773 _ _ |a 10.1016/j.ecmx.2024.100561
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