Model Predictive Energy Management for Building Microgrids with IoT-based Controllable Loads

This thesis develops an economic scheduling framework for a building microgrid with internet of things (IoT) based flexible loads to synchronize the buildings’ controllable components, with occupant behavior and environmental conditions. We employ model predictive control (MPC) methods to minimize building operating costs, while maximizing the utilization of the on-site resources. The main research thrusts are: 1) Developing the building microgrid model; 2) Defining different building operation strategies; 3) Minimizing the building’s daily operating costs. Simulation results show that the proposed approach provides superior energy cost savings and peak load reduction in comparison with other operation controls, such as All from Utility (AFU), AFU with installed IoT-based Building Energy Management System (BEMS), and MPC-Mix Integer Linear Programming (MILP) without IoT-based BEMS. An economic analysis is also conducted to provide a road map for the implementation of installing advanced energy efficiency technologies across loads in building microgrid and integrating them with the building microgrid’s control strategy