Energy efficiency control of direct expansion air conditioning systems

Abstract

The dynamic mathematical models for direct expansion air conditioning (DX A/C) systems with respect to indoor carbon dioxide (CO2) concentration, relative humidity and air temperature and the coupling effects among them have been built in this thesis. To reduce the energy cost and improve the energy efficiency for DX A/C systems while maintaining both indoor air quality (IAQ) and thermal comfort at acceptable levels, a hierarchical control structure is proposed in this thesis. This control structure includes two levels. The upper level is an open loop optimal controller to generate the optimal setpoints of indoor CO2 concentration, relative humidity and air temperature for the lower level controller. The lower level designs a closed-loop model predictive control (MPC) controller to optimize the transient processes reaching the setpoints where the energy efficiency improvement and energy cost savings are achieved. In Chapter 2, the control objective is to improve both IAQ and thermal comfort as well as energy efficiency for a DX A/C system. The details of a hierarchical control structure in this chapter are as follows: In the upper layer, an energy-optimised open loop controller is proposed based on an optimization of energy consumption of the DX A/C system and given reference points of indoor CO2 concentration, relative humidity and air temperature to generate a unique and optimised steady state for the lower layer controller. In the lower layer, the closed-loop MPC controller is proposed such that the indoor CO2 concentration, relative humidity and air temperature follow the steady state computed by the upper layer, whereas the energy efficiency is improved. To facilitate the MPC design, the nonlinear DX A/C control system is linearized around the optimised steady state. In Chapter 3, the control objective is to lower the energy cost and consumption of a DX A/C system while maintaining both IAQ and thermal comfort at comfort levels. To achieve this purpose, an autonomous hierarchical control (AHC) structure is designed and described below. The upper level is an open loop nonlinear optimal controller, which optimizes the predicted mean vote (PMV) index and the energy cost for the DX A/C system under a time-of-use (TOU) price structure of electricity according to the changing environment over a 24-hour period, to generate the tradeoff setpoints of indoor CO2 concentration, relative humidity and air temperature for the lower level controller. The lower layer is formed as a closed-loop MPC to track the trajectory reference points calculated by the optimization layer. This AHC strategy means the upper controller can adaptively and automatically set the setpoints and the lower layer adaptively and optimally tracks them, minimizing energy consumption and costs. In addition, in this chapter, the volumes of outside air allowed to enter the DX A/C system are regarded as varying with the changing circumstance over a day and are optimized by the AHC. Moreover, a supply fan to steer the pressure swing absorption with a built-in proportional-integral (PI) controller is proposed to lower the indoor CO2 concentration such that it would reduce the complexity of computation for the AHC and the cost of hardware. In Chapter 4, the control objective is to reduce energy cost, improve energy efficiency, and reduce communication resources, computational complexity and conservativeness, as well as peak demand for a multi-zone building multi-evaporator air conditioning (ME A/C) system while maintaining multi-zones’ thermal comfort and IAQ at comfort levels. To realize this objective and to consider the interaction effects between rooms, we present an autonomous hierarchical distributed control (AHDC) method. The upper level is an open loop nonlinear optimizer, which only collects measurement information and solves a distributed steady state optimization problem to adaptively and automatically generate time-varying and optimised reference points of indoor CO2 concentration, relative humidity and air temperature for the lower-layer controllers, by minimizing the demand and energy costs of a multi-zone building ME A/C system under the TOU price structure of electricity according to the changing circumstance during the day. The lower level also uses local information to track the trajectory references calculated by the upper-layer distributed controller, via distributed MPC controllers. The proposed hierarchical control strategy is distributed in two layers since they use only local information from the working zone and its neighbours. To validate the performance of these hierarchical control strategies for DX A/C systems, simulation tests are performed in this thesis. In Chapter 2, simulations are provided to show that the closed-loop regulation of the MPC controller and the energy-optimised open loop controller can maintain indoor CO2 concentration, relative humidity and air temperature at their desired setpoints with small deviations and reduce the effect of indoor cooling and pollutant loads. The simulations also demonstrate that the controllers are superior to conventional controllers in terms of energy efficiency. In Chapter 3, the simulation tests show that the AHC strategy can reduce more energy consumption and cost than the baseline strategy. In addition, the tests demonstrate that the AHC scheme is not sensitive to the physical parameters of the DX A/C system. In Chapter 4, to show the performance of the two-layer distributed control strategies, a case study is given. The simulation tests demonstrate that the AHDC strategy is capable of shifting demand from peak hours to off-peak hours and reducing the energy cost for a multi-zone building ME A/C system while maintaining multi-zones’ IAQ and thermal comfort at comfort levels.