Journal of Process Control 24 (2014) 1282–1291 Contents lists available at ScienceDirect Journal of Process Control j our na l ho me pa g e: www.elsevier.com/locate/jprocont Application of economic MPC to the energy and demand minimization of a commercial building Jingran Ma a , S. Joe Qin a,c,∗ , Timothy Salsbury b a Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, HED 211, Los Angeles, CA 90089, United States b Johnson Controls Inc., 507 E Michigan Street, Milwaukee, WI 53202, United States c School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China a r t i c l e i n f o Article history: Received 10 November 2013 Received in revised form 17 June 2014 Accepted 17 June 2014 Available online 19 July 2014 Keywords: Economic model predictive control Building HVAC Optimization Simulation Field tests a b s t r a c t This paper presents an application case study of an economic model predictive control (EMPC) method for optimizing the building demand and energy cost under the time-of-use price policy. The control strategy is comprised of an economic objective function that accounts for the combination of energy and demand costs with a time-of-use rate structure, a dynamic thermal process and power model of the building thermal mass dynamics, and a set of constraints to ensure the building is operated properly. The optimization is a min–max optimization problem and is converted to a linear program. The EMPC method is implemented in a commercial office building located in Milwaukee, Wisconsin, USA. An internet-based control architecture is developed to carry out tests with the EMPC controller at a remote location. The test results show that the EMPC strategy is capable of shifting the peak demand to off-peak hours and reducing energy costs compared to a baseline case for the building. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction The U.S. Department of Energy reported in 2009 [1], 73% of the nation’s electricity consumption and 40% of greenhouse gas emis- sions can be attributed to buildings. Buildings account for 39% of total energy consumption among all sectors, costing $350 billion per year [2]. Heating, ventilation and air conditioning (HVAC) sys- tems are responsible for about one third of building energy usage [1]. It is believed that most buildings are not operated as effi- ciently as they could be. In general, there are two approaches to achieve energy savings: by installing more energy efficient equipment in buildings, or by managing energy consumption in an efficient way via the building automation systems (BAS) [3]. The first approach requires capital expenditures and any result- ing retrofit action would be disruptive to the building operation. The second approach on the other hand optimizes the operation of HVAC systems with existing equipment by simply changing control settings with advanced control strategies [4]. In addition to reducing overall energy consumption, another important need for building controls is to lower the peak power demand. Owing to the fact that buildings, especially commercial ∗ Corresponding author. Tel.: +1 213 740 0317; fax: +1 213 740 8053. E-mail address: sqin@usc.edu (S.J. Qin). buildings, tend to consume energy simultaneously during peak hours, the peak-average-ratio (PAR) in the electricity grid can be high [5]. Both electricity suppliers and customers are concerned with the peak demand due to economic and environmental chal- lenges. New power plants are built every year merely to cope with the rapidly increasing peak demand, which reduces efficiency in off-peak hours and leads to higher energy costs [6]. Moreover, uncontrolled high peak demand makes it difficult to integrate renewable energy resources. Therefore, it is of great interest to develop advanced technologies to flatten the peak demand relative to base loads. Demand response (DR) is a concept that has recently received extensive attention in buildings as a promising means to lower the peak demand. DR encourages end-users to alter their electric usages from regular patterns, in response to incentives of electricity price [7,8]. Advanced electricity rate structures are now commonly applied by utilities, which include time-of-use (TOU), critical-peak- pricing (CPP) and real-time pricing (RTP). It is demonstrated that under these time varying rate structures, users are able to reduce energy costs by taking certain DR actions [9–11]. The cost-effective strategy for building HVAC control is to shift the energy usage away from the peak hours while all the normal operation criterion such as thermal comfort level and indoor air quality are maintained [12]. Most buildings are operated with a night-setback (NS) strat- egy, in which the HVAC is turned on during the occupied period and turned off otherwise. The setpoints of thermostats are http://dx.doi.org/10.1016/j.jprocont.2014.06.011 0959-1524/© 2014 Elsevier Ltd. All rights reserved.