@conference {244,
	title = {Development and Testing of Model Predictive Control for a Campus Chilled Water Plant with Thermal Storage},
	booktitle = {2010 ACEEE Summer Study on Energy Efficiency in Buildings},
	year = {2010},
	publisher = {Omnipress},
	organization = {Omnipress},
	address = {Asilomar, California, USA},
	abstract = {<p>A Model Predictive Control (MPC) implementation was developed for a university campus chilled water plant. The plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. A detailed model of the chilled water plant and simplified models of the campus buildings were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers{\textquoteright} performance data, and calibrated using measured data collected and archived by the control system. A dynamic model of the chilled water storage tank was also developed and calibrated. A semi-empirical model was developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a MPC algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The paper describes the development and testing of the MPC implementation and discusses lessons learned and next steps in further research.</p>},
	issn = {0-918249-60-0},
	author = {Brian E. Coffey and Philip Haves and Michael Wetter and Brandon Hencey and Francesco Borrelli and Yudong Ma and Sorin Bengea}
}
@proceedings {241,
	title = {Model Predictive Control of Thermal Energy Storage in Building Cooling Systems},
	journal = {American Control Conference},
	year = {2010},
	month = {06/2010},
	address = {Baltimore, Maryland, USA},
	abstract = {A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reductionin the central plant electricity cost and improvement of its efficiency.},
	author = {Yudong Ma and Francesco Borrelli and Brandon Hencey and Brian E. Coffey and Sorin Bengea and Philip Haves}
}
