Industrial Multi-Energy and Production Management Scheme in Cyber-Physical Environments: A Case Study in a Battery Manufacturing Plant

Pei, Wei; Ma, Xin; Deng, Wei; Chen, Xinhe; Sun, Hongjian; Li, Dan

Abstract

Among the various electricity consumer sectors, the consumption level of the industrial sector is often considered as the largest portion of electricity consumption, highlighting the urgent need to implement demand response (DR) energy management. However, implementation of DR for the industrial sector requires a more sophisticated and different scheme compared to the residential and commercial sector. This study explores all the elastic segments of plant multi-energy production, conversion, and consumption. We then construct a real-time industrial facilities management problem as an optimal dispatch model to enclose these elastic segments and production constraints in cyber-physical environments. Moreover, a model predictive-based centralised dispatch scheme is proposed to address the uncertainties of real-time price and renewable energy forecasting while considering the sequence of the production process. Numerical results demonstrate that the proposed scheme can enhance energy efficiency and economics of lithium battery manufacturing plant through responding to the real-time price whilst ensuring the completion of production tasks.

Citation

Pei, W., Ma, X., Deng, W., Chen, X., Sun, H., & Li, D. (2019). Industrial Multi-Energy and Production Management Scheme in Cyber-Physical Environments: A Case Study in a Battery Manufacturing Plant. IET Cyber-Physical Systems: Theory & Applications, 4(1), 13-21. https://doi.org/10.1049/iet-cps.2018.5029

• Journal Article Type: Article
• Acceptance Date: Sep 27, 2018
• Online Publication Date: Oct 3, 2018
• Publication Date: Mar 31, 2019
• Deposit Date: Sep 27, 2018
• Publicly Available Date: Nov 7, 2018
• Journal: IET Cyber-Physical Systems: Theory & Applications
• Electronic ISSN: 2398-3396
• Publisher: Wiley Open Access
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 1
• Pages: 13-21
• DOI: https://doi.org/10.1049/iet-cps.2018.5029
• Public URL: https://durham-repository.worktribe.com/output/1318476