Abstract

This thesis presents an experimental and theoretical study of a reduced-order modelling methodology and dynamic response of convectively heated buildings and active thermal storage. A methodology was developed for the generation of control-oriented building models which can be used within model predictive control (MPC) or other model-based control strategies to satisfy occupant comfort and improve building-grid interaction. A methodology to identify and evaluate MPC strategies is presented to improve a building's energy flexibility. There is an emphasis on modelling building thermal mass and a dedicated thermal storage device. The two applications for reduced-order thermal modelling (buildings and dedicated active thermal energy storage devices) require different modelling approaches for control applications. Several case studies are introduced and are typical Quebec construction with convective-based heating systems: a detached low-mass house, a low-mass retail building, and a warehouse (with active thermal storage device).

The residential building study outlined a methodology for multi-level control-oriented modelling with several zones and multiple floors. This multi-level approach allows the user to “zoom in and out” so that models at each control level remain manageable. In the second case study, implementation of MPC was presented for a conventional bank building to reduce the yearly utility bill and avoid the summer peak load penalty. A cost savings of 25% on the yearly electric utility bill and a peak power reduction of 38% were achieved. With the new optimized operation, the cost per square meter for the bank would decrease from $30.19/m2 to $22.57/m2, or a yearly savings of $7.62/m2.

The last case study comprises a 1650 m2 warehouse equipped with a dedicated active high-temperature thermal energy storage device. A methodology was presented for the development and analysis of control-oriented models for enhanced operation of the electric thermal storage device. The goal was to maximize the building energy flexibility the building could provide to the grid by evaluating the Building Energy Flexibility Index (BEFI). A BEFI of 55% to 100% was achieved. The average demand during the critical times was reduced between 36 kW and 65 kW and the utility cost to the customer can be reduced by 12-30%.