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A real-time decision model for industrial load management in a smart grid

Authors
Yu, MengmengLu, RenzhiHong, Seung Ho
Issue Date
Dec-2016
Publisher
Pergamon Press Ltd.
Keywords
Industrial manufacturing process; Real-time price; Timely decision; Robust optimization; Demand response
Citation
Applied Energy, v.183, pp 1488 - 1497
Pages
10
Indexed
SCI
SCIE
SCOPUS
Journal Title
Applied Energy
Volume
183
Start Page
1488
End Page
1497
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/12136
DOI
10.1016/j.apenergy.2016.09.021
ISSN
0306-2619
1872-9118
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. (C) 2016 Elsevier Ltd. All rights reserved.
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