Hierarchical Model Predictive Control for complex building energy systems

Mork, Maximilian; Xhonneux, André; Müller, Dirk

doi:10.1002/bapi.202000031

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…The hierarchical MPC divides the optimized problem within the temporal dimension into two layers of different dynamics and time scales as proposed in [23]. For the upper, slow MPC layer, a longer prediction horizon and sampling period are chosen, whereas for the lower, fast layer, the horizon and sampling period are smaller.…”

Section: Hierarchical Mpcmentioning

confidence: 99%

“…All rooms are hydraulically coupled to a central, shared TABS implemented as CCA in the floor, which is operated via a central control input regulating the supply water mass flow. Each room is equipped with an identically dimensioned CCA subsection, a convector, simplified pumps and an external window including external Venetian blinds for active solar shading [23]. Two pumps per room supply the CCA subsection and the hydraulically uncoupled convector with supply water mass flow rates calculated by the optimization (at fixed supply temperatures).…”

Section: Case Studymentioning

confidence: 99%

“…An upper centralized layer calculates reference temperature trajectories for a lower layer consisting of several local controllers, which solve their local problems in an isolated, decentralized way based on local information and the upper temperature trajectories to be tracked. In previous works, the concepts of hierarchical [23] and distributed [24] building MPC based on nonlinear Modelica models have been individually developed. The hierarchical MPC splits the optimization problem into two layers of different dynamics, which are equipped with different prediction horizons and sampling periods to guarantee both anticipation and reactivity.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Distributed Model Predictive Control for Building Energy Systems

Mork

Xhonneux

Müller

2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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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 controller models. 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 reactivity with 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.

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Section: Hierarchical Mpcmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Distributed Model Predictive Control for Building Energy Systems

Mork

Xhonneux

Müller

2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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“…In the CAB, distributed and hierarchical MPC concepts will be implemented; these are currently developed and evaluated based on simulative test beds [79]. Controller and simulation models are created based on the open-source Modelica simulation library AixLib [80].…”

Section: Combination Of Protocols Used In the Climate-neutral Adminis...mentioning

confidence: 99%

Enhancing Building Monitoring and Control for District Energy Systems: Technology Selection and Installation within the Living Lab Energy Campus

et al. 2022

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With regard to climate change, it is imperative to reduce greenhouse gas emissions. One solution approach is to increase energy efficiency in buildings. Buildings contribute a high share of the total global energy usage. As the rate of new building constructions is low, measures applicable to existing buildings become paramount. Before applying new approaches on a large scale, it is necessary to evaluate them in a representative, realistic environment. Living labs such as the Living Lab Energy Campus (LLEC) at Forschungszentrum Jülich (FZJ) facilitate innovative monitoring and control approaches in a real-world setting. In this work, we investigate the required steps for bringing sensor and control networks, comprising more than 1800 devices, into 18 existing and new buildings. This enables both room-level monitoring and control, as well as the integration of building-wide automation. By introducing an ICT infrastructure, we pave the way towards holistic approaches on a district level. We describe the workflows used for selected instrumentation variants and show first insights from the operation of the resulting infrastructure. We show that the investigated instrumentation variants exhibit similar characteristics; however, they affect control behavior differently. We emphasize that instrumentation should be planned in the context of existing infrastructure. Moreover, we present and evaluate sample measurements obtained from different buildings.

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“…Some constraints in the complex industrial system [2]- [4] can be built in optimization problems through this control method, and further processed online depend on the MPC algorithm. Therefore, the appearance of MPC has brought huge economic benefits to the industrial control field [5]- [7]. Centralized predictive control is one of the model predictive control methods, which has been widely used in many conventional industrial control systems [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Distributed Model Predictive Control for Linear Constrained Systems Based on Time-Varying Terminal Sets

Zhu

Xue

2021

IEEE Access

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A novel distributed model predictive control (DMPC) strategy with time-varying terminal set for linear constrained systems is presented in this paper. To decrease the load of computation of DMPC while ensuring the global optimization, the nominal system is introduced by treating the influence of neighboring subsystems as a bounded disturbance. Then, under the distributed control structure, a distributed predictive control optimization problem containing the nominal state and input can be designed for each subsystem. Furthermore, different from most DMPC approaches, a novel approach to design a terminal constraint set that can be updated in every update time based on the predicted state of the system is proposed. Additionally, the analysis of feasibility and the stability of the proposed DMPC algorithm are described under kinds of the system constraints. Finally, experimental simulation is shown to prove validity by the control scheme in this paper.INDEX TERMS Distributed model predictive control, time-varying terminal constraint set, linear constrained system.

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Hierarchical Model Predictive Control for complex building energy systems

Cited by 12 publications

References 17 publications

Hierarchical Distributed Model Predictive Control for Building Energy Systems

Hierarchical Distributed Model Predictive Control for Building Energy Systems

Enhancing Building Monitoring and Control for District Energy Systems: Technology Selection and Installation within the Living Lab Energy Campus

Distributed Model Predictive Control for Linear Constrained Systems Based on Time-Varying Terminal Sets

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