(724a) Economic Optimization of Large-Scale Embedded Battery Applications

Commercial buildings are responsible for 20% of the total U.S. energy consumption. In particular, large-scale heating, ventilation, and air conditioning (HVAC) systems account for a large portion of these energy expenditures. Due to the lack of optimal control algorithms in buildings (Afram & Janabi-Sharifi, 2014), these systems have become a focus for both academic and industrial research. Many papers have been published in this area showing that significant cost savings can be attained by capitalizing on the time-varying nature of electricity prices, including (Oldewurtel et al., 2010; Mendoza-Serrano & Chmielewski, 2012). Savings are achieved using various forms of energy storage to shift the energy load from peak hours when prices are high to off-peak hours when prices are low (Touretzky & Baldea, 2016; Avci, Erkoc, Rahmani, & Asfour, 2013). Several forms of storage can be used including passive thermal energy storage (e.g., mass of the buildings), active thermal energy storage (e.g., insulated chilled water tanks), and electricity storage (e.g., batteries).

Model predictive control (MPC) is an advanced control method that is well-suited for load shifting due to its ability to forecast and optimize. MPC relies on a model of the system to predict the process variables based on the actions taken by the controller (Rawlings & Mayne, 2009). An optimization problem is then solved using this model to achieve desired control objectives. MPC has been highly successful with thousands of applications in the chemical and petroleum industries alone (Qin & Badgwell, 2003). Economic MPC is one form of MPC in which the objective is to minimize cost or maximize profit. MPC can also handle both continuous decisions such as temperature setpoints and discrete decisions such as when to turn equipment on and off (Rawlings & Risbeck, 2016). Hence, heuristics are no longer needed to make discrete decisions.

Large-scale embedded battery applications are the primary focus of this work. As the cost of batteries decreases, they can be embedded directly into any piece of HVAC equipment that draws power, including roof-top units (RTUs), air-handler units (AHUs), variable refrigerant flow (VRF) units, fans, pumps, and compressors. Large-scale applications, such as university campuses and industrial complexes, may contains hundreds of buildings and thousands of zones. Each building can have a multiplicity of RTUs, VRFs, and/or AHUs. A centralized MPC formulation for these cases is neither feasible nor desirable, so a decomposition is needed. Distributed control can be used for this purpose. Managing the peak demand over the entire campus is also key since most pricing structures include a peak demand charge. The distributed control system design must account for this peak demand charge.

In this talk, we propose a hierarchical control architecture using MPC for the economic optimization of large-scale commercial HVAC systems with embedded batteries. The high-level problem serves as a coordinator for the distributed controllers in each building or subsystem. The aforementioned issues are addressed in this decomposition. We conclude with a large-scale simulation study to demonstrate the savings potential using embedded batteries with the proposed control strategy.

References
Afram, A., & Janabi-Sharifi, F. (2014, Feb). Theory and applications of HVAC control systems—A review of model predictive control (MPC). Build. Environ., 72, 343–355.
Avci, M., Erkoc, M., Rahmani, A., & Asfour, S. (2013, May). Model predictive HVAC load control in buildings using real-time electricity pricing. Energ. Buildings, 60, 199–209.
Mendoza-Serrano, D. I., & Chmielewski, D. J. (2012). HVAC control using infinite-horizon economic MPC. In Decision and control (cdc), 2012 ieee 51st annual conference on (pp. 6963–6968).
Oldewurtel, F., Parisio, A., Jones, C. N., Morari, M., Gyalistras, D., Gwerder, M., ... Wirth, K. (2010). Energy efficient building climate control using stochastic model predictive control and weather predictions. In American Control Conference (ACC), 2010 (pp. 5100–5105). Baltimore, MD, USA.
Qin, S. J., & Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control Eng. Pract., 11(7), 733-764.
Rawlings, J. B., & Mayne, D. Q. (2009). Model predictive control: Theory and design. Madison, WI: Nob Hill Publishing. (576 pages, ISBN 978-0-9759377-0-9)
Rawlings, J. B., & Risbeck, M. J. (2016). Model predictive control with discrete actuators: Theory and application. Accepted to Automatica.
Touretzky, C. R., & Baldea, M. (2016). A hierarchical scheduling and control strategy for thermal energy storage systems. Energ. Buildings, 110, 94–107.