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@INPROCEEDINGS{Mork:1020281,
      author       = {Mork, Maximilian and Xhonneux, André and Müller, Dirk},
      title        = {{H}ierarchical {D}istributed {M}odel {P}redictive {C}ontrol
                      for {B}uilding {E}nergy {S}ystems; 36th},
      reportid     = {FZJ-2024-00037},
      pages        = {1-12},
      year         = {2023},
      abstract     = {Buildings contribute to approximately 30 $\%$ of global
                      energy consumption, which renders their energy-efficient
                      control an effective measure to reduce overall energy
                      consumption. This paper presents a hierarchical distributed
                      Model Predictive Control (MPC) for building energy systems
                      based on nonlinear Modelica controllermodels. It combines
                      hierarchical and distributed optimization approaches to
                      split the optimization complexity within the temporal and
                      spatial dimension. The hierarchical optimization approach
                      considers different dynamics in complex building energy
                      systems and ensures both anticipation for systems with high
                      inertia and reactivitywith regard to errors in the
                      forecasting of the disturbance quantities. The distributed
                      optimization approach divides the centralized optimization
                      problem into subproblems to improve the scalability and
                      adaptability of the control framework. The subproblems are
                      solved in a parallel and iterative manner and account for
                      both thermal (heat transfer over zone boundaries) and
                      hydraulic inter-zone coupling (induced by a central, shared
                      Heating, Ventilation and Air Conditioning (HVAC) system). A
                      particular focus of the control approach is placed on
                      robustness with respect to errors in the forecasting of the
                      disturbances that impact the building dynamics. The control
                      performance of the proposed MPC framework is evaluated in a
                      simulative case study of heating and shading control of a
                      nonlinear six-room-building Modelica model, which is exposed
                      to different forecast accuracies for the disturbances of
                      occupancy, solar radiation and air exchange. The case study
                      exhibits the benefits of the control framework in terms of
                      energy consumption, thermal discomfort and computation time
                      in comparison to a reference control concept of a
                      non-hierarchical distributed MPC configuration.},
      month         = {Jun},
      date          = {2023-06-25},
      organization  = {THE 36TH INTERNATIONAL CONFERENCE ON
                       EFFICIENCY, COST, OPTIMIZATION,
                       SIMULATION AND ENVIRONMENTAL IMPACT OF
                       ENERGY SYSTEMS, Las Palmas de Gran
                       Canaria (Spain), 25 Jun 2023 - 30 Jun
                       2023},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1123 - Smart Areas and Research Platforms (POF4-112) /
                      EG2050: LLEC-Verwaltungsbau: Klimaneutraler Verwaltungsbau
                      als aktiver Teil des Living Lab Energy Campus (LLEC)
                      (03EGB0010A)},
      pid          = {G:(DE-HGF)POF4-1123 / G:(BMWi)03EGB0010A},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.34734/FZJ-2024-00037},
      url          = {https://juser.fz-juelich.de/record/1020281},
}