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001024052 1001_ $$0P:(DE-Juel1)180106$$aHoltwerth, Alexander$$b0$$eCorresponding author
001024052 245__ $$aClosed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming
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001024052 520__ $$aThe 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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001024052 536__ $$0G:(BMBF)03SF0573$$aLLEC::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)$$c03SF0573$$x2
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001024052 7001_ $$0P:(DE-Juel1)8457$$aXhonneux, André$$b1$$ufzj
001024052 7001_ $$0P:(DE-Juel1)172026$$aMüller, Dirk$$b2$$ufzj
001024052 773__ $$0PERI:(DE-600)3010114-1$$a10.1016/j.ecmx.2024.100561$$gVol. 22, p. 100561 -$$p100561 -$$tEnergy conversion and management: X$$v22$$x2590-1745$$y2024
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