Economic Model Predictive Control for Building Energy Systems

February 27, 2015, Webb 1100

Joe Qin

USC, Chemical Engineering and Materials Science


Buildings consume a majority of electricity in the US, with HVAC cooling being the largest contributor. Recent development in smart grid operations and electricity pricing strategies bring up an opportunity to optimize the use of off-peak electricity and reduce the on-peak consumption to minimize energy cost and provide grid-friendly consumptions. In this seminar I present a strategy to use economic model predictive control to achieve this objective. The economic objective function in MPC accounts for the daily electricity costs, which includes time-of-use (TOU) energy charge and demand charge. Each time step, a min-max optimization is formulated and converted into a linear programming problem and solved. In a weekly simulation, pre-cooling effect during off-peak period and cooling discharging from the building thermal mass during on-peak period can be observed. Cost savings by MPC are estimated by comparing with the baseline and other open-loop control strategies. The effect of several experimental factors in the MPC configuration is investigated and the best scenario will be selected for practical tests.

Speaker's Bio

Dr. S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is a Co-Director of the Texas-Wisconsin-California Control Consortium where he has been principal investigator for 20 years. He is Vice President of the Chinese University of Hong Kong, Shenzhen, a Specially Appointed Professor at the Northeast University, and is on leave as the Fluor Professor of Process Engineering at the Viterbi School of Engineering of the University of Southern California.

Dr. Qin is a Fellow of IEEE and Fellow of the International Federation of Automatic Control (IFAC). He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, Chang Jiang Professor of Tsinghua University, Thousand Talent Professor of the Northeastern University of China, and an IFAC Best Paper Prize for the model predictive control survey paper published in Control Engineering Practice. He is currently an Associate Editor for Journal of Process Control, IEEE Control Systems Magazine, and a Member of the Editorial Board for Journal of Chemometrics. He has published over 100 papers in SCI journals, with over 5700 ISI WoS citations and an h-index of 39. Dr. Qin’s research interests include process data analytics, process monitoring and fault diagnosis, model predictive control, system identification, building energy optimization, semiconductor process control, and control performance monitoring.