The thermal storage potential in commercial buildings is an enormous resource for providing various ancillary services to the grid. In this paper, we show how fans in Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings alone can provide substantial frequency regulation service, with little change in their indoor environments. A feedforward architecture is proposed to control the fan power consumption to track a regulation signal. The proposed control scheme is then tested through simulations based on a calibrated high fidelity non-linear model of a building. Model parameters are identified from data collected in Pugh Hall, a commercial building located on the University of Florida campus. For the HVAC system under consideration, numerical experiments demonstrate how up to 15% of the rated fan power can be deployed for regulation purpose while having little effect on the building indoor temperature. The regulation signal that can be successfully tracked is constrained in the frequency band , where minutes and seconds. Our results indicate that fans in existing commercial buildings in the U.S. can provide about 70% of the current national regulation reserve requirements in the aforementioned frequency band. A unique advantage of the proposed control scheme is that assessing the value of the ancillary service provided is trivial, which is in stark contrast to many demand-response programs.

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Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

We propose a novel stochastic agent-based model of occupancy dynamics in a building with an arbitrary number of zones and occupants. Simulation of the model yields time-series of the location of each agent (a software representation of an occupant). The model is meant to provide realistic simulation of occupancy dynamics in nonemergency situations. Comparison of the model's prediction of distributions of random variables such as first arrival time of a building is provided against those estimated from measurements in commercial buildings. We also propose a lower complexity graphical model of occupancy evolution in multi-zone buildings. The graphical model captures information on mean occupancy and correlation among occupancy at various zones in the building. The agent-based model can be used in conjunction with building performance simulation tools, while the graphical model is more suitable for real-time applications, such as occupancy estimation with noisy sensor measurements.

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