Bachelor's thesis Lina Meyer

 

Comparative Study of Simulation-Assisted Approaches for Distributed Model Predictive Control in Building Energy Systems

The basic control framework Copyright: EBC The basic control framework structure for implementation of for distributed model predictive control approaches

Building energy systems (BES) are subject to a constant optimization process aiming at meeting a variety of energy efficiency and comfort requirements. In that regard, many proposals for novel control approaches have been made, among which various are based on model predictive control (MPC). Main challenges preventing a breakthrough of MPC in BES can be identified in a lack of flexibility and a demand for high online computational power. In this work, a distributed MPC (DMPC) algorithm using look-up tables to store optimization outputs is compared to an iterative DMPC approach. Both algorithms have neighboring subsystems that share information among each other. They can be selected in a Python control framework, which is presented in this work. After elaborating on the theoretical concept and suitability including the application of both algorithms to linear models, real-life tests on an air handling unit conclude the comparative study.