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@PHDTHESIS{Baader:962430,
      author       = {Baader, Florian Joseph},
      othercontributors = {Bardow, André and Mitsos, Alexander},
      title        = {{S}imultaneous real-time scheduling of multi-energy systems
                      and dynamic production processes},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2023-07390},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2023},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2023},
      abstract     = {Volatile renewable electricity production causes a temporal
                      mismatch of supply and demand, which can be reduced by
                      consumers using optimal production scheduling to shift their
                      demand in time. However, if production processes with
                      nonlinear dynamics must be scheduled simultaneously with
                      multi-energy systems (MESs) introducing discrete on/off
                      decisions, the resulting optimization problems are typically
                      not solvable in real-time today. This thesis reformulates
                      simultaneous dynamic scheduling (SDS) problems of MESs and
                      nonlinear production processes to mixed-integer linear
                      programs (MILPs), which can be solved in real-time. These
                      MILP reformulations rely on tailored scheduling models
                      consisting of three piece-wise affine (PWA) parts: (1)
                      process output models, (2) data-driven process energy demand
                      models, and (3) MILP energy system models. For part 1, we
                      present two alternatives: First, we use a scale-bridging
                      model (SBM), which is easy-to-apply but requires heuristic
                      tuning. We use two cooled continuous stirred tank reactor
                      (CSTR) case studies to show that our approach can capture
                      the major part of the nonlinear potential in real time, and
                      a heated distillation column case study to show that our
                      approach can reduce the number of states substantially.
                      Second, as a rigorous alternative to the heuristically tuned
                      SBM, we derive dynamic ramping constraints (DRCs), which are
                      first restricted to flat processes with only one scheduling
                      relevant variable. These DRCs consider linear dynamics of
                      high order with PWA constraints. We use that, for flat
                      processes, a nonlinear model can be transferred to a linear
                      model by coordinate transformation. Again, we use a CSTR
                      case study to show that our approach can capture the major
                      part of the nonlinear potential. For non-flat processes, we
                      develop heuristic DRCs, based on simulation experiments. For
                      the heated distillation column, these heuristic DRCs perform
                      similarly to SBMs regarding both optimization runtime and
                      operational costs. Lastly, we show that DRCs can also
                      consider multiple scheduling-relevant variables by applying
                      DRCs to an electrolyzer with slow temperature dynamics. We
                      outperform a quasi-steady-state scheduling through optimized
                      temperature dynamics. This thesis thus offers reformulations
                      that strike reasonable compromises between optimization
                      runtime and solution quality for SDS of production processes
                      and MESs. While SBMs can be applied with less effort and
                      knowledge, DRCs offer more dynamic flexibility for flat
                      processes.},
      cin          = {416710},
      ddc          = {620},
      cid          = {$I:(DE-82)416710_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2023-07390},
      url          = {https://publications.rwth-aachen.de/record/962430},
}