Distributed cooperative industrial demand response. (February 2020)

• Record Type: Journal Article
• Authors: Allman, Andrew; Zhang, Qi
• Is Part Of: Journal of process control. Volume 86(2020)
• Page Start: 81
• Page End: 93
• Publication Date: 2020-02
• Subjects: Industrial demand response -- Cooperation -- Distributed optimization -- ADMM; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.12.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: New DR framework with cooperation between energy-intensive load and customers Load shares DR savings with customers as incentive to shift production schedules Alternate direction method of multipliers prevents model sharing between agents Benefits from cooperation shown in real-world ammonia production case study Computational results show cost benefits of flexibility, information sharing Abstract: 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.