Mixed-integer optimization methods for online scheduling in large-scale HVAC systems
- 114 Downloads
Due to time-varying utility prices, peak demand charges, and variable-efficiency equipment, optimal operation of heating ventilation, and air conditioning systems in campuses or large buildings is nontrivial. Given forecasts of ambient conditions and utility prices, system energy requirements can be reduced by optimizing heating/cooling load within buildings and then choosing the best combination of large chillers, boilers, etc., to meet that load while accounting for switching constraints and equipment performance. With the presence of energy storage, utility costs can be further reduced by temporally shifting production, which adds an additional layer of complexity. Furthermore, due to changes in market and weather conditions, it is necessary to revise a given schedule regularly as updated information is received, which means the problem must be tractable in real time (e.g., solvable within 15 min). In this paper, we present a mixed-integer linear programming model for this problem along with reformulations, decomposition approaches, and approximation strategies to improve tractability. Simulations are presented to illustrate the effectiveness of these methods. By removing symmetry from identical equipment, decomposing the problem into subproblems, and approximating longer-timescale behavior, large instances can be solved in real time to within 1% of the true optimal solution.
KeywordsLarge-scale HVAC systems Online optimization Closed-loop scheduling
Funding, equipment models, and sample data provided by Johnson Controls, Inc. Additional funding provided by the National Science Foundation (Grant #CTS-1603768).
- 2.Albadi, M.H., El-Saadany, E.F.: Demand response in electricity markets: an overview. In: 2007 IEEE Power Engineering Society General Meeting, IEEE, pp. 1–5 (2007)Google Scholar
- 18.Pantelides, C.C.: Unified frameworks for optimal process planning and scheduling. In: Proceedings on the Second Conference on Foundations of Computer Aided Operations, pp. 253–274 (1994)Google Scholar
- 24.Rawlings, J.B., Mayne, D.Q., Diehl, M.M.: Model Predictive Control: Theory, Computation and Design. Nob Hill Publishing, Madison (2017)Google Scholar
- 35.Mendoza-Serrano, D.I., Chmielewski, D.J.: HVAC control using infinite-horizon economic MPC. In: IEEE 51st Annual Conference on Decision and Control (CDC), pp. 6963–6968 (2012)Google Scholar
- 37.Rawlings, J.B., Mayne, D.Q.: Model Predictive Control: Theory and Design. Nob Hill Publishing, Madison (2009)Google Scholar
- 39.Patel, N.N.R., Risbeck, M.J., Rawlings, J.B., Wenzel, M.M.J., Turney, R.D.: Distributed economic model predictive control for large-scale building temperature regulation. In: American Control Conference, Boston, MA, pp. 895–900 (2016)Google Scholar
- 41.ElBsat, M.N., Wenzel, M.J.: Load and electricity rates prediction for building wide optimization applications. In: 4th International High Performance Buildings Conference at Purdue, West Lafayette, IN (2016)Google Scholar