Date of Award
Open Access Master's Thesis
Master of Science in Mechanical Engineering (MS)
Administrative Home Department
Department of Mechanical Engineering-Engineering Mechanics
Committee Member 1
Committee Member 2
Buildings are a major source of energy consumption. In the United States, buildings are responsible for more than 70% of all power consumption. Over 40% of this building power consumption is from the Heating, Ventilation, and Air Conditioning (HVAC) systems. Modern technologies such as building Energy Storage Systems (ESS), renewable energy sources, and advanced control algorithms allow for so-called Smart Buildings to increase energy efficiency. Smart Buildings further benefit from existing in a Smart Grid environment, where information such as pricing and anticipated power load is sent over two way communitcation between the grid operator and the power consumer.
The traditional control systems for these HVAC systems are often simple and do not exploit the principles of optimal control. This study applies Model Predictive Control (MPC) and ESS to the problem of controlling a Smart Building in a Smart Grid environment.
Simulations are performed for various optimal control objective functions. These objectives include price minimization, energy minimization, and an introduced Building to Grid (B2G) index optimization. The B2G optimization aims to both decrease the price of power for the consumer while avoiding large spikes in power consumption to maintain a steady load profile which benefits the grid operator. The results show that MPC has potential for large performance increases in Building Energy Management, while meeting the constraints for B2G integration.
Dobbs, Jeremy, "MODEL PREDICTIVE CONTROL OF BUILDING ENERGY MANAGEMENT SYSTEMS IN A SMART GRID ENVIRONMENT", Open Access Master's Thesis, Michigan Technological University, 2015.