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@PHDTHESIS{Papadimitriou:1022412,
      author       = {Papadimitriou, Chrysanthi},
      othercontributors = {Mitsos, Alexander and Baldea, Michael},
      title        = {{D}ynamic optimization strategies for scheduling of
                      chemical processes under time-varying electricity prices},
      volume       = {43},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-10005},
      series       = {Aachener Verfahrenstechnik series - AVT.SVT - Process
                      systems engineering},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {Demand-side management enhances grid stability by aligning
                      electricity consumption with power availability in markets
                      with a high share of renewables. In this context, process
                      scheduling enables electricity-intensive processes to
                      enhance efficiency and meet production constraints while
                      profiting from spot markets by adjusting production to price
                      fluctuations. Achieving such flexibility requires
                      incorporating process dynamics into scheduling decisions,
                      particularly when system and price time scales align. While
                      energy market volatility suggests online process
                      decision-making, embedded dynamics induce computationally
                      challenging dynamic optimization problems. This dissertation
                      develops solution approaches for dynamic scheduling and
                      methods to exploit electricity spot markets, addressing
                      economic and computational barriers in industrial
                      applications.We first extend a global dynamic optimization
                      approach to nonconvex scheduling with dynamics embedded,
                      using Hammerstein–Wiener models trained on experimental
                      data from an electrochemical process. The method
                      demonstrates strong economic potential in scheduling
                      succinic acid recovery but is unsuitable for real-time use.
                      To address this, we introduce control grid refinement to
                      accelerate computations and reach high-quality solutions,
                      though computational intractability remains.Next, we
                      highlight the impact of electricity price scenario selection
                      in demand-response studies. To avoid arbitrary choices and
                      scenario explosion, we propose a framework for generating
                      day-ahead and intraday price profiles from historical data.
                      The method captures key data features, requires minimal
                      computational effort and yields results comparable to
                      state-of-the-art techniques over a full year of
                      operation.Systematic consideration of day-ahead and intraday
                      prices enables market participation across different time
                      scales, aligning with process dynamics in scheduling and
                      control. We propose a two-economic-layer scheme for
                      scheduling and control of processes participating in both
                      markets. Building on integrated dynamic scheduling and
                      economic model predictive control, we perform scheduling
                      under day-ahead prices followed by intraday adjustments and
                      market trading. Applied to an air separation process, the
                      scheme, unlike common methods, achieves consistently high
                      economic performance.Building on the proposed methodologies,
                      we investigate the trade-off between model fidelity and
                      optimization complexity in dynamic scheduling within
                      integrated scheduling and control. We compare full-order and
                      reduced-order models under local optimization with
                      scale-bridging models versus global optimization under
                      varying formulations. Application to air separation shows
                      local optimization with full-order models offering an
                      improved economic and control performance, while local
                      optimization with nonlinear reduced-order models is suitable
                      when process data are available.},
      cin          = {416710},
      ddc          = {620},
      cid          = {$I:(DE-82)416710_20140620$},
      pnm          = {GRK 2379 - GRK 2379: Hierarchische und hybride Ansätze
                      für moderne inverse Probleme (333849990)},
      pid          = {G:(GEPRIS)333849990},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-10005},
      url          = {https://publications.rwth-aachen.de/record/1022412},
}