Nonlinear Distributed Model Predictive Control for multi-zone building energy systems

This paper presents a distributed Model Predictive Control (MPC) approach for multi-zone building energy systems based on nonlinear Modelica controller models. The method considers both thermal and hydraulic coupling among different building zones. The iterative and parallel distributed optimization approach builds upon an uncooperative approach for thermal coupling using the Nash equilibrium approach and a cooperative approach for the hydraulic coupling using the Alternating Direction Method of Multipliers (ADMM). Apart from thermal coupling through walls, the modeling takes thermal coupling through doors into account using a data-driven approach, which calculates the inter-zone air exchanges based on temperature differences between door-coupled zones. The hydraulic coupling enables consideration of interactions between the zones introduced by a shared, central Heating, Ventilation and Air Conditioning (HVAC) system. The distributed MPC framework is structured in an easy-scalable, plug-and-play composition, where local systems are automatically assigned to the global coordination scheme. The distributed MPC method is applied to a simulative nonlinear case study, consisting of a six-room-building Modelica model considering both thermal and hydraulic interactions. The benefits of the proposed approach are demonstrated and compared against centralized and decentralized control concepts in terms of energy consumption, discomfort and computation time. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a distributed Model Predictive Control (MPC) approach for multi-zone building energy systems with thermal and hydraulic coupling among different building zones. The method uses an iterative and parallel distributed optimization approach and considers thermal coupling through walls and doors. The hydraulic coupling enables consideration of interactions between zones. The distributed MPC framework is easy to scale and automatically assigns local systems to the global coordination scheme.