Distributed cooperative industrial demand response

Andrew Allman, Qi Zhang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Industrial demand response, whereby energy-intensive industrial processes shift their production to take advantage of time-varying electricity prices, is a popular topic of recent research. In a traditional demand response problem, the industrial process will have customers which set their own schedule for purchases of the industrial product, which the industrial process must meet. However, by introducing cooperation between an industrial process and its customers, additional cost savings are possible. In this work, we present an optimization framework for cooperative demand response, whereby the industrial process provides economic incentives to customers to shift their schedules to better align with the utility costs seen by the industrial process. As the industrial process and its customers are likely to be different entities which do not share their operational models, we propose a distributed algorithm for solving the problem using the alternate direction method of multipliers, which enables solving the problem with minimal information sharing. The ability of this algorithm to obtain solutions which improve upon the status quo is showcased through multiple case studies. We further demonstrate that cooperation provides the most benefit when the industrial process has limited ability to store its product and when the customer has a high degree of flexibility. We also show that this idea is practically applicable through a case study where an industrial process supplies hydrogen and nitrogen to an ammonia producer.

Original languageEnglish (US)
Pages (from-to)81-93
Number of pages13
JournalJournal of Process Control
StatePublished - Feb 2020

Bibliographical note

Funding Information:
The authors gratefully acknowledge financial support from the University of Minnesota and the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper.

Publisher Copyright:
© 2019


  • ADMM
  • Cooperation
  • Distributed optimization
  • Industrial demand response


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