Real-Time Mixed-Integer Optimization for Improved Economic Performance in HVAC Systems

Michael J. Risbeck, Christos T. Maravelias, James B. Rawlings

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

Optimal operation of large-scale heating, ventilation, and air conditioning (HVAC) systems requires many discrete-valued decisions. For example, in large heating and cooling plants, operators must choose which combination of equipment to activate to meet a given load. Such discrete decisions are typically made by operators using heuristics, which can lead to suboptimal performance. In this paper, we discuss how relevant HVAC decision problems can be transcribed into mixed-integer linear programming (MILP) formulations. We first present the general modeling framework we adopt, which is very similar to the resource task network framework adopted in chemical production scheduling. Second, we discuss a series of reformulations, including linearizations of complex unit models. Third, we present solution methods, including decomposition approaches and the employment of surrogate models to approximate the performance of the system over a long planning horizon. Finally, we demonstrate how the resulting optimization problems can be solved online in closed loop to improve system performance.

Original languageEnglish (US)
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages33-42
Number of pages10
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Publication series

NameComputer Aided Chemical Engineering
Volume44
ISSN (Print)1570-7946

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • economic model predictive control
  • energy building optimization
  • reformulations
  • Scheduling
  • solution methods

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