Optimal load dispatch for industrial manufacturing process based on demand response in a smart grid

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

  • Xinhui Lu
  • Kaile Zhou
  • Chi Zhang
  • Shanlin Yang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number035503
Journal / PublicationJournal of Renewable and Sustainable Energy
Volume10
Issue number3
StatePublished - May 2018

Abstract

As a key smart grid technology, demand response (DR) can effectively balance the supply and demand of electricity in the power system, which both reduces the energy consumption costs and improves the stability of the power grid. The industrial sector is one of the major consumers of electricity, and it is of great practical significance and potential to implement DR programs in the industrial sector. In this study, the state-task network is first implemented to model an industrial manufacturing process. Then, a new optimal load dispatch model for the industrial manufacturing process based on DR in a smart grid environment is proposed. In the model, the energy storage system (ESS) and distributed energy resources (DERs) are included. Finally, a stamping process of automobile manufacturing is selected as a case study, in which the effects of DR, ESS, and DERs on the industrial load dispatch are discussed, respectively. The results show that parts of the electricity demand can be shifted from peak to off-peak periods through DR, which can reduce the energy consumption costs for the industrial manufacturing process. Meanwhile, the results reveal that the energy costs can be reduced further by managing the electricity storage of the ESS and through the deployment of DERs.

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Optimal load dispatch for industrial manufacturing process based on demand response in a smart grid. / Lu, Xinhui; Zhou, Kaile; Zhang, Chi; Yang, Shanlin.

In: Journal of Renewable and Sustainable Energy, Vol. 10, No. 3, 035503, 05.2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review