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Mixed-integer optimization methods for online scheduling in large-scale HVAC systems

  • Michael J. Risbeck
  • Christos T. MaraveliasEmail author
  • James B. Rawlings
  • Robert D. Turney
Original paper
  • 49 Downloads

Abstract

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.

Keywords

Large-scale HVAC systems Online optimization Closed-loop scheduling 

Notes

Acknowledgements

Funding, equipment models, and sample data provided by Johnson Controls, Inc. Additional funding provided by the National Science Foundation (Grant #CTS-1603768).

Supplementary material

11590_2018_1383_MOESM1_ESM.pdf (248 kb)
Supplementary material 1 (pdf 248 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Wisconsin–MadisonMadisonUSA
  2. 2.Johnson ControlsMilwaukeeUSA
  3. 3.University of California–Santa BarbaraSanta BarbaraUSA

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