Minimizing Energy Usage of Commercial Buildings with Distributed Economic Model Predictive Control

Nishith Patel

Commercial buildings represent about 20% of U.S. energy consumption corresponding to almost 18 quads of energy. Almost all heating and cooling systems in commercial buildings and educational facilities nowadays rely on temperature controllers whose only goal is to converge to the desired temperature set point and stay there, within a small tolerance. The current gold standard for this control is a method known as model predictive control (MPC), where the aim is to reach the set point over a specified time horizon with minimum controller effort. A much better goal is to minimize total energy (i.e. total cost), using the recent advancements in economic MPC.

However, current economic MPC theory considers only continuous variables. In complex heating and cooling systems, the theory needs to be extended to incorporate discrete variables (for example, turning chillers on and off) and stochastic variables (for example, electricity price forecast). Thermal energy storage can also be utilized to reduce power demand during peak hours. My research focuses on the formulation of this optimization problem, development of system-wide models, construction of a robust control scheme, and implementation of the scheme with our industrial collaborator, Johnson Controls, Inc. I am currently working on formulating the air side problem of these HVAC systems using distributed economic MPC. Using energy more efficiently will not only reduce heating/cooling costs for commercial buildings, but will also reduce total energy consumption.