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@INPROCEEDINGS{Holtwerth:1005151,
      author       = {Holtwerth, Alexander and Xhonneux, André and Müller,
                      Dirk},
      title        = {{D}ata-{D}riven {G}eneration of {M}ixed-{I}nteger {L}inear
                      {P}rogramming {F}ormulations for {M}odel {P}redictive
                      {C}ontrol of {H}ybrid {E}nergy {S}torage {S}ystems using
                      detailed nonlinear {S}imulation {M}odels; 1st},
      reportid     = {FZJ-2023-01339},
      pages        = {160-69},
      year         = {2022},
      abstract     = {The scheduling of hybrid energy systems with battery
                      storage systems (BSS) and hydrogen storage systems (HSS) for
                      the storage of renewable energies is a non-trivial task due
                      to the nonlinear nature of electrolyzers and fuel cells and
                      the volatile electricity generation by renewable energies.
                      Mathematical optimization of the scheduling increases the
                      system efficiency and decreases the share of grid
                      electricity required to cover the electrical demand. Hence,
                      tailor-made models are required for each hydrogen component
                      due to the uniqueness of each hydrogen system. Therefore,
                      the time-consuming work of model generation and validation
                      needs to be done for every system in order to ensure
                      adequate accuracy of the mathematical models used for model
                      predictive control. This work derives and utilizes a
                      simulation model of a hybrid energy system as a substitute
                      for a real-world system. We propose a framework that uses
                      functional mock-up units of detailed simulation models to
                      derive tailormade mixed-integer linear programming (MILP)
                      formulations of the steady-state operational behavior. We
                      combine the derived formulations for the operational
                      behavior of each component into an optimization model of the
                      whole hybrid energy system. The optimization model is then
                      used for model predictive control of the simulation model.
                      The results show that we can generate accuratemodels of the
                      component behavior without detailed knowledge of the
                      simulation model. The resulting optimization model of the
                      whole energy system accurately reflects the simulation model
                      and is, therefore, suitable for model predictive control.},
      month         = {Apr},
      date          = {2022-04-04},
      organization  = {1st International Workshop on Open
                       Source Modelling and Simulation of
                       Energy Systems, Aachen (Germany), 4 Apr
                       2022 - 5 Apr 2022},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1123 - Smart Areas and Research Platforms (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1123},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000852742000007},
      doi          = {10.1109/OSMSES54027.2022.9769104},
      url          = {https://juser.fz-juelich.de/record/1005151},
}