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| Contribution to a conference proceedings | FZJ-2024-00037 |
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2023
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Please use a persistent id in citations: doi:10.34734/FZJ-2024-00037
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.
Keyword(s): Energy (1st) ; Others (2nd)
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