A real-time decision model for industrial load management in a smart grid. (1st December 2016)

• Record Type: Journal Article
• Authors: Yu, Mengmeng; Lu, Renzhi; Hong, Seung Ho
• Is Part Of: Applied energy. Volume 183(2016)
• Page Start: 1488
• Page End: 1497
• Publication Date: 2016-12-01
• Subjects: Industrial manufacturing process -- Real-time price -- Timely decision -- Robust optimization -- Demand response; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.09.021
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Propose a real-time decision model for industrial energy consumption management. Consider both current and future load control in the scheduling horizon. Adopt robust optimization to model the future price uncertainty. Conduct a case study over an entire steel powder manufacturing process. Abstract: The potential impacts of evolving industrial load management into demand response (DR) programs have been widely acknowledged. This paper proposes a real-time decision model for the load management of an industrial manufacturing process in the face of ever-changing real-time prices (RTPs). Due to the inherent dependence between consecutive tasks in a manufacturing process, the decision model must take future load management into consideration. The challenge lies in the uncertainty that future RTPs cannot be known in advance. In view of this, robust optimization was adopted to deal with future price uncertainties, such that the proposed model is able to make timely decisions for industrial load control when receiving the RTP for the current time slot, while considering load scheduling in future time slots. The case study was conducted on a steel powder manufacturing process; simulation results validated the effectiveness of the proposed real-time decision approach from various perspectives.