Economic model predictive control for building energy systems

In the United States, buildings account for nearly three quarters of electricity consumption and about 40% of greenhouse gas emissions. The heating, ventilation and air-conditioning (HVAC) systems are responsible for approximately one third of the energy usage in commercial buildings. The inefficiency in operation and control of HVAC systems in most of the current buildings places significant energy saving potentials. Even more importantly, under advanced electricity rate structures in the nowadays utility market with demand response, the energy cost for building HVAC systems can be very high due to the high peak power demand associated with the ordinary control strategies.;In this dissertation, we present a cost-effective supervisory control strategy for the building HVAC systems. The goal of this work is to propose and demonstrate an advanced control solution to optimize building energy cost under the time-of-use rate structure, while maintaining the thermal comfort level and indoor air quality. Model Predictive Control (MPC) is the core methodologies investigated in this work, which is carried out in two stages.;In the first stage, we develop a simulation framework, in which a commercial building model crated in EnergyPlus acts as the building to be controlled. Since the simulation model is not suitable to be directly used in MPC, system identification is performed to obtain the empirical models, which relate the thermostat setpoints to the zone temperature as well as power consumption. Balanced model reduction technique is then applied to lower the model order, while the major input-output dynamic relation is captured.;The MPC problem is formulated utilizing the identified models with reduced-order. Due to the slow building thermal dynamics, an economic objective is combined with the underlying dynamic models, which forms the Economic MPC (EMPC). The optimization in terms of building energy cost has a min-max objective function to account for the combination of energy and demand charges, and a number of constraints to represent allowed temperature range and model relation. The optimization is converted to a linear programming and solved effectively in each time step, giving the optimal zone temperature setpoints.;The effectiveness of EMPC is demonstrated by a weekly simulation. Substantial cost savings are brought by EMPC over the baseline and other open-looped strategies. The simulation system established in this work can also be used as a test-bed for other control algorithms.;In the second stage of this work, the proposed EMPC method is implemented in a large office building located in Milwaukee, WI. The EMPC controller is located at USC and connected to the building automation system (BAS) via the Internet. Field tests results show that the EMPC strategy is capable of shifting significant portion of power consumption out of peak hours, therefore brings cost savings to the building.;In addition, another application of EMPC to the power dispatch problem in Microgrid is described. It shows the potential of EMPC in the area of renewable energy integration. A Microgrid with renewable generation resources and controllable energy storages is considered. Simulation-based EMPC structure is formulated to minimize the overall power generation cost within the Microgrid.