An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load

Qi Zhang, Michael F. Morari, Ignacio E. Grossmann, Arul Sundaramoorthy, Jose M. Pinto

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

To ensure the stability of the power grid, backup capacities are called upon when electricity supply does not meet demand due to unexpected changes in the grid. As part of the demand response efforts in recent years, large electricity consumers are encouraged by financial incentives to provide such operating reserve in the form of load reduction capacities (interruptible load). However, a major challenge lies in the uncertainty that one does not know in advance when load reduction will be requested. In this work, we develop a scheduling model for continuous industrial processes providing interruptible load. An adjustable robust optimization approach, which incorporates recourse decisions using linear decision rules, is applied to model the uncertainty. The proposed model is applied to an illustrative example as well as a real-world air separation case. The results show the benefits from selling interruptible load and the value of considering recourse in the decision-making.

Original languageEnglish (US)
Pages (from-to)106-119
Number of pages14
JournalComputers and Chemical Engineering
Volume86
DOIs
StatePublished - Mar 4 2016

Bibliographical note

Funding Information:
The authors gratefully acknowledge the financial support from the National Science Foundation under Grant No. 1159443 and from Praxair .

Keywords

  • Adjustable robust optimization
  • Demand response
  • Interruptible load
  • Mixed-integer linear programming
  • Production scheduling

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