Abstract:
This thesis proposes a design of a supervisory model predictive controller for a heating-ventilation-air-conditioning (HVAC) control system.
The control objective is to minimize the operating cost and take into account of electrical load shaving and thermal comfort of users.
To ensure that thermal comfort is well regulated, we utilize the Predicted Mean Vote (PMV) as an indicator and determine an acceptable bound of a desired set-point temperature.
The control design consists of two layers, namely, a supervisory control (SC) layer and a model predictive control (MPC) layer.
For the SC layer, we explore a configuration including the choice of predesign controller, the analysis of steady-state response, and the possible range of the set-point temperature.
We incorporate the effect of set-point temperature, air velocity, outside air temperature, heat load inside zone onto the HVAC electrical power.
Then, we search for an optimal profile of the set-point temperature that minimizes a weighted sum of a total operating cost (TOC) and a thermal comfort cost (TCC).
Moreover, exploration of trade-off between TOC and TCC helps us to achieve both control objectives efficiently.
For the MPC layer, we formulate the control design with the objective of tracking the optimal set-point temperature and minimizing the control inputs.
We apply the proposed control design to a complex dynamical model of HVAC system with volumetric flow rate, electrical power of heat exchanger, and removed moisture as control variables and test with various weather conditions.
When the allowable PMV of the zone is specified within a certain comfort level, the TOC of the proposed supervisory MPC is significantly reduced compared with that of the nominal operation.
The maximum electricity demand of HVAC system is reduced and the electrical power profile is smoothly shaved.
Furthermore, the zone relative humidity is well regulated and corresponds to the operating condition of the SC layer.
