When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
The transition to smart grids has raised the need to investigate and enhance building energy flexibility. Energy flexibility, defined as the capacity of the building to respond to the needs of the grid, is critical to address the grid's challenges of balancing supply and demand and integrating renewable energy sources. To achieve these goals, it is necessary to develop control-oriented models able to provide reliable predictions, to be implemented in smart controllers, and generalizable in order to facilitate their widespread deployment in buildings.
This thesis investigates methods to enhance the energy flexibility potential of school buildings through simulation and experimental studies. It contributes a general methodology for the development of data driven grey-box thermal models and the implementation of model-based predictive control (MPC). The methodology is applied to an archetype fullyelectric school building near Montréal, Québec, Canada. This approach is scalable and transferable to other institutional or mid-size commercial buildings.
To streamline the implementation of MPC, the proposed approach employs grey-box low-order resistance-capacitance (RC) thermal network models, a clustering of weather conditions to identify typical anticipated scenarios, and several near-optimal setpoint profiles corresponding to each cluster. Archetype control-oriented models for zones with convective systems and zones with radiant floor systems are developed and calibrated with measured data. The impact of model resolution and structure on the energy flexibility quantification is investigated. The calibrated models are used to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. For the experimental study, the developed MPC framework is applied in six classrooms, and the results are compared with four classrooms with the reactive control system as reference cases. The energy flexibility is quantified based on a proposed building energy flexibility index (BEFI). Results indicated that the school building can provide 45% to 95% energy flexibility (load shifting relative to reference) during onpeak hours while satisfying thermal comfort constraints.
Finally, this thesis presents an MPC methodology for the integration of air-based photovoltaic/thermal (PV/T) systems to further enhance the energy flexibility in school buildings so that in addition to the production of solar electricity, they can be used to preheat fresh air for the classrooms during the heating season. The PV/T system electrical capacity is set equal to the peak electricity demand in the classrooms. A data-driven greybox model for the classrooms is calibrated with measured data, and a PV/T model as a renewable energy retrofit measure for energy efficiency and flexibility is developed. These models are integrated to apply MPC to the school building and reduce peak demand during morning and evening. Three scenarios are investigated and compared: 1) A reference case without any on-site renewable system, 2) Integration of a PV system and MPC strategies under a demand response scenario, and 3) Integration of an air-based PV/T system and MPC strategies under a demand response scenario. Results show that using an MPC along with PV/T integration can significantly reduce peak demand during morning and evening high demand periods for the grid. The proposed methodology helps institutional buildings to facilitate their integration into future smart grid and smart cities.