IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v209y2023icp533-545.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812300424X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.03.121?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kelley, Morgan T. & Pattison, Richard C. & Baldick, Ross & Baldea, Michael, 2018. "An MILP framework for optimizing demand response operation of air separation units," Applied Energy, Elsevier, vol. 222(C), pages 951-966.
    2. Borri, Emiliano & Tafone, Alessio & Romagnoli, Alessandro & Comodi, Gabriele, 2021. "A review on liquid air energy storage: History, state of the art and recent developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    3. He, Xiufen & Liu, Yunong & Rehman, Ali & Wang, Li, 2022. "Feasibility and performance analysis of a novel air separation unit with energy storage and air recovery," Renewable Energy, Elsevier, vol. 195(C), pages 598-619.
    4. Fernández, David & Pozo, Carlos & Folgado, Rubén & Guillén-Gosálbez, Gonzalo & Jiménez, Laureano, 2017. "Multiperiod model for the optimal production planning in the industrial gases sector," Applied Energy, Elsevier, vol. 206(C), pages 667-682.
    5. He, Xiufen & Liu, Yunong & Rehman, Ali & Wang, Li, 2021. "A novel air separation unit with energy storage and generation and its energy efficiency and economy analysis," Applied Energy, Elsevier, vol. 281(C).
    6. Karakurt, Izzet & Aydin, Gokhan, 2023. "Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries," Energy, Elsevier, vol. 263(PA).
    7. Kong, Fulin & Liu, Yuxin & Tong, Lige & Guo, Wei & Qiu, Yinan & Wang, Li, 2022. "Optimization of co-production air separation unit based on MILP under multi-product deterministic demand," Applied Energy, Elsevier, vol. 325(C).
    8. Jiang, Sheng-Long & Peng, Gongzhuang & Bogle, I. David L. & Zheng, Zhong, 2022. "Two-stage robust optimization approach for flexible oxygen distribution under uncertainty in integrated iron and steel plants," Applied Energy, Elsevier, vol. 306(PB).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Zhang, Liu & Zheng, Zhong & Chai, Yi & Zhang, Kaitian & Lian, Xiaoyuan & Zhang, Kai & Zhao, Liuqiang, 2024. "Enhancing robustness: Multi-stage adaptive robust scheduling of oxygen systems in steel enterprises under demand uncertainty," Applied Energy, Elsevier, vol. 359(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Kong, Fulin & Liu, Yuxin & Tong, Lige & Guo, Wei & Qiu, Yinan & Wang, Li, 2022. "Optimization of co-production air separation unit based on MILP under multi-product deterministic demand," Applied Energy, Elsevier, vol. 325(C).
    3. Wang, Kaiwen & Tong, Lige & Yin, Shaowu & Yang, Yan & Zhang, Peikun & Liu, Chuanping & Zuo, Zhongqi & Wang, Li & Ding, Yulong, 2024. "Novel ASU–LAES system with flexible energy release: Analysis of cycle performance, economics, and peak shaving advantages," Energy, Elsevier, vol. 288(C).
    4. Qi, Meng & Park, Jinwoo & Lee, Inkyu & Moon, Il, 2022. "Liquid air as an emerging energy vector towards carbon neutrality: A multi-scale systems perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Ding, Xingqi & Zhou, Yufei & Duan, Liqiang & Li, Da & Zheng, Nan, 2023. "Comprehensive performance investigation of a novel solar-assisted liquid air energy storage system with different operating modes in different seasons," Energy, Elsevier, vol. 284(C).
    6. Liu, Yuxin & Yu, Dongling & Tong, Lige & Zhang, Peikun & Guo, Wei & Zuo, Zhongqi & Wang, Li & Ding, Yulong, 2024. "Improved liquid air energy storage process considering air purification: Continuous and flexible energy storage and power generation," Renewable Energy, Elsevier, vol. 231(C).
    7. He, Xiufen & Guo, Wei & Liu, Yunong & Zuo, Zhongqi & Wang, Li, 2024. "Utmost substance recovery and utilization for integrated technology of air separation unit and liquid air energy storage and its saving benefits," Renewable Energy, Elsevier, vol. 225(C).
    8. Zhang, Liu & Zheng, Zhong & Chai, Yi & Zhang, Kaitian & Lian, Xiaoyuan & Zhang, Kai & Zhao, Liuqiang, 2024. "Enhancing robustness: Multi-stage adaptive robust scheduling of oxygen systems in steel enterprises under demand uncertainty," Applied Energy, Elsevier, vol. 359(C).
    9. Severino, Gonzalo & Rivera, José & Parot, Roberto & Otaegui, Ernesto & Fuentes, Andrés & Reszka, Pedro, 2024. "Workforce and task optimization to guarantee oxygen bottling under a COVID-19 pandemic scenario: A Chilean case study," International Journal of Production Economics, Elsevier, vol. 271(C).
    10. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    11. Tafone, Alessio & Borri, Emiliano & Cabeza, Luisa F. & Romagnoli, Alessandro, 2021. "Innovative cryogenic Phase Change Material (PCM) based cold thermal energy storage for Liquid Air Energy Storage (LAES) – Numerical dynamic modelling and experimental study of a packed bed unit," Applied Energy, Elsevier, vol. 301(C).
    12. Fan, Xiaoyu & Xu, Hao & Li, Yihong & Li, Junxian & Wang, Zhikang & Gao, Zhaozhao & Ji, Wei & Chen, Liubiao & Wang, Junjie, 2024. "A novel liquid air energy storage system with efficient thermal storage: Comprehensive evaluation of optimal configuration," Applied Energy, Elsevier, vol. 371(C).
    13. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    14. Xiao, Dongliang & Lin, Zhenjia & Chen, Haoyong & Hua, Weiqi & Yan, Jinyue, 2024. "Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences," Applied Energy, Elsevier, vol. 357(C).
    15. Dzido, Aleksandra & Wołowicz, Marcin & Krawczyk, Piotr, 2022. "Transcritical carbon dioxide cycle as a way to improve the efficiency of a Liquid Air Energy Storage system," Renewable Energy, Elsevier, vol. 196(C), pages 1385-1391.
    16. Mkono, Christopher N. & Chuanbo, Shen & Mulashani, Alvin K. & Mwakipunda, Grant Charles, 2023. "Deep learning integrated approach for hydrocarbon source rock evaluation and geochemical indicators prediction in the Jurassic - Paleogene of the Mandawa basin, SE Tanzania," Energy, Elsevier, vol. 284(C).
    17. Sapnken, Flavian Emmanuel & Hong, Kwon Ryong & Chopkap Noume, Hermann & Tamba, Jean Gaston, 2024. "A grey prediction model optimized by meta-heuristic algorithms and its application in forecasting carbon emissions from road fuel combustion," Energy, Elsevier, vol. 302(C).
    18. Dang, Zheng & Wang, Xiaoming & Bie, Shizhen & Su, Xianbo & Hou, Shihui, 2024. "Experimental study of water occurrence in coal under different negative pressure conditions: Implication for CBM productivity during negative pressure drainage," Energy, Elsevier, vol. 303(C).
    19. Sayed, Enas Taha & Alami, Abdul Hai & Abdelkareem, Mohammad Ali & Wilberforce, Tabbi & Kamarudin, Siti Kartom & Olabi, A.G., 2024. "Real direct urea fuel Fell operation using standalone Ni-based metal-organic framework prepared by ball mill at room temperature," Energy, Elsevier, vol. 305(C).
    20. Gandhi, Akhilesh & Zantye, Manali S. & Faruque Hasan, M.M., 2022. "Cryogenic energy storage: Standalone design, rigorous optimization and techno-economic analysis," Applied Energy, Elsevier, vol. 322(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:209:y:2023:i:c:p:533-545. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.