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A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage

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  • Kong, Fulin
  • Liu, Yuxin
  • Shen, Minghai
  • Tong, Lige
  • Yin, Shaowu
  • Wang, Li
  • Ding, Yulong

Abstract

Air separation units (ASUs) integrated with liquid air energy storage (LAES) have the potential to balance grid demand and improve production profits. This study aims to enhance the demand response of a novel co-production air separation system (NCASS) by maximizing the energy arbitrage of a LAES system. We developed a mixed-integer nonlinear programming model to minimize operating costs by using a proxy model to represent the ASU production space and a black box model to represent the material and energy flow balance of LAES. The model is based on rigorous mathematical programming and, considers material and energy flow balance, analyzes the production and delivery processes and constraints for each product, and matches equipment start-up and shutdown time constraints. The case study shows that the model achieves a high level of matter and energy matching between the distillation process and the LAES process, maximizing the LAES peak-shaving capacity and improving system economics. During the 24 h schedule period, the total electricity consumption of NCASS increases by 18.94% during the valley electricity period and decreases by 20.59% during the flat and peak electricity period. The economic benefits increase by 9.51 k$, accounting for 14.29% of the total costs. This study contributes to the wider application of NCAS with LAES technology for sustainable energy management.

Suggested Citation

  • Kong, Fulin & Liu, Yuxin & Shen, Minghai & Tong, Lige & Yin, Shaowu & Wang, Li & Ding, Yulong, 2023. "A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage," Renewable Energy, Elsevier, vol. 209(C), pages 533-545.
    • Handle: RePEc:eee:renene:v:209:y:2023:i:c:p:533-545
    DOI: 10.1016/j.renene.2023.03.121
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    References listed on IDEAS

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    Cited by:

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    2. Che, Gelegen & Zhang, Yanyan & Tang, Lixin & Zhao, Shengnan, 2023. "A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants," Applied Energy, Elsevier, vol. 345(C).

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