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@ARTICLE{Holtwerth:1024052,
      author       = {Holtwerth, Alexander and Xhonneux, André and Müller,
                      Dirk},
      title        = {{C}losed loop model predictive control of a hybrid
                      battery-hydrogen energy storage system using mixed-integer
                      linear programming},
      journal      = {Energy conversion and management: X},
      volume       = {22},
      issn         = {2590-1745},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-01942},
      pages        = {100561 -},
      year         = {2024},
      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.},
      cin          = {IEK-10},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / 1121 - Digitalization and Systems
                      Technology for Flexibility Solutions (POF4-112) /
                      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) / LLEC -
                      Living Lab Energy Campus (LLEC-2018-2023)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(DE-HGF)POF4-1121 /
                      G:(BMBF)03SF0573 / G:(DE-HGF)LLEC-2018-2023},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001206649000001},
      doi          = {10.1016/j.ecmx.2024.100561},
      url          = {https://juser.fz-juelich.de/record/1024052},
}