IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v182y2019icp559-569.html
   My bibliography  Save this article

A data-driven approach for industrial utility systems optimization under uncertainty

Author

Listed:
  • Zhao, Liang
  • You, Fengqi

Abstract

Energy optimization of utility system helps to reduce the operating cost and save energy for the industrial plants. Widespread uncertainties such as device efficiency and process demand pose new challenges for this issue. A hybrid modeling framework is presented by introducing the operating data into mechanism model to adapt the changes of device efficiency and operating conditions. Mathematical models of boilers, steam turbines, and letdown valves are then developed in the framework. Based on the process historical data of a real-world plant, a Dirichlet process mixture model is used to capture the support information of uncertain parameters. Bridging data-driven robust optimization (DDRO) and utility system optimization under uncertainty, a robust mixed-integer nonlinear programming (MINLP) model is developed by utilizing the derived uncertainty set. The robust counterpart of the developed model can be reformulated as a tractable MINLP problem including conic quadratic constraints that could be solved efficiently. A real-world case study is carried out to demonstrate the effectiveness of the proposed approach in protecting against uncertainties and achieving a good trade-off between optimality and robustness of the operational decisions for industrial utility systems.

Suggested Citation

  • Zhao, Liang & You, Fengqi, 2019. "A data-driven approach for industrial utility systems optimization under uncertainty," Energy, Elsevier, vol. 182(C), pages 559-569.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:559-569
    DOI: 10.1016/j.energy.2019.06.086
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219312186
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.06.086?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.

    Citations

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


    Cited by:

    1. Wang, Qipeng & Zhao, Liang, 2023. "Data-driven stochastic robust optimization of sustainable utility system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. Li, Hanxiu & Zhao, Liang, 2023. "Life cycle assessment and multi-objective optimization for industrial utility systems," Energy, Elsevier, vol. 280(C).
    3. Yu Huang & Weizhen Hou & Yiran Huang & Jiayu Li & Qixian Li & Dongfeng Wang & Yan Zhang, 2020. "Multi-Objective Optimal Operation for Steam Power Scheduling Based on Economic and Exergetic Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    4. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    5. Tan, Mao & Li, Zibin & Su, Yongxin & Ren, Yuling & Wang, Ling & Wang, Rui, 2024. "Dual time-scale robust optimization for energy management of distributed energy community considering source-load uncertainty," Renewable Energy, Elsevier, vol. 226(C).
    6. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    7. Park, Haryn & Kim, Jin-Kuk & Yi, Sung Chul, 2023. "Optimization of site utility systems for renewable energy integration," Energy, Elsevier, vol. 269(C).
    8. Han, Yulin & Zheng, Jingyuan & Luo, Xiaoyan & Qian, Yu & Yang, Siyu, 2023. "Multi-scenario data-driven robust optimisation for industrial steam power systems under uncertainty," Energy, Elsevier, vol. 263(PD).

    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:energy:v:182:y:2019:i:c:p:559-569. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/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.