Building energy management and demand response

Abstract

Recently, the energy efficiency of a building has been receiving much attention from the researchers. Many papers discuss the optimizations around the Heating, Ventilation, and Air-Conditioning (HVAC) system, such as sensors and model predictive control (MPC) based thermostats. However, most of them do not consider the energy usage on the drive devices where the fans and pumps are powered by the induction machines which consume about 50% to 75% of total energy consumption in the overall HVAC system. Various drive control strategies could render different energy savings by considering the losses in an induction machine under the different scenarios of a HVAC system. Hence, one of the research works in this thesis investigates the energy efficiency of an induction machine controlled by a MPC based approach for the central fan application in a HVAC system. The integrated HVAC and model predictive flux control (MPFC) drive model is constructed in Matlab/Simulink where a HVAC is formulated by a RC-network model. The simulations are carried out to study the drive performance under various scenarios in a HVAC system where the induction machine’s torque ripple, speed ripple, torque loss, and power consumption are investigated. Besides improving the energy efficiency of system components in a HVAC system, optimizing the Energy Management System (EMS) and Demand Response (DR) of a building is another essential way to reduce the building’s electricity bill. Thus, the other research work is presented in this thesis to shed light on how the EMS with multi-storage systems can be optimally and coordinately controlled in a demand responsive building by providing a price-based integrated automation model for managing various building loads, renewable energy, and multi-storage systems. This work focuses on the optimizing an EMS framework for the Demand Side Management (DSM) instead of the utility side optimization discussed in the literature. It also extends the previous works done by other researchers and addresses their presented issues for deploying the ice storage system. The proposed method is validated by the simulation environment conducted on EnergyPlus™ and CPLEX®. In the proposed EMS framework, the deterministic approach is adopted for the battery and renewable energy optimization that operate in a coordinated manner with the DR programs in a building. The smart price-priority strategy is designed in EnergyPLus™ using EnergyPlus runtime language (Erl) to make a smart decision for coordinated control of the thermal energy storage (TES) device, battery, HVAC-based DR, and the Demand Responses (DRs) of lighting and miscellaneous loads. The simulation results show that the proposed smart price-priority strategy for coordinated control of ice storage with other DR programs can make a proper trade-off between the peak-load reduction and energy consumption in the building with storage systems. In addition, a hybrid cooling and-ventilation control algorithm is developed for a HVAC-based DR event, which shows a similar performance of the conventional global temperature adjustment of all zones in the building for a HVAC-based DR, in terms of the net operating cost and energy consumption.