IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p1908-d1346120.html
   My bibliography  Save this article

Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System

Author

Listed:
  • Gengshun Liu

    (Guoneng Xinjiang Ganquanbao Comprehensive Energy Co., Ltd., Urumqi 830019, China)

  • Xinfu Song

    (Economic and Technological Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830002, China)

  • Chaoshan Xin

    (Economic and Technological Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830002, China)

  • Tianbao Liang

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yang Li

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Kun Liu

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Due to the large proportion of China’s energy consumption used by industry, in response to the national strategic goal of “carbon peak and carbon neutrality” put forward by the Chinese government, it is urgent to improve energy efficiency in the industrial field. This paper focuses on the optimization of an integrated energy system with supply–demand coordination in an industrial park. This optimization is formulated as a “node-flow” model. Within the model, each node is designed according to the objective function of its own operation and the energy coupling relationship. The flow model is designed based on the energy flow interaction relationship between each node. Based on the “node-flow” model, an edge–cloud information interaction mechanism based on energy transfer balance between nodes is proposed to describe the way the system interacts with information, and a distributed iterative optimization algorithm based on edge–cloud collaboration is designed to realize the optimization decision of each node. The performance of the method proposed in this paper is demonstrated using a practical case study of an industrial park integrated energy system in Xinjiang. The results show that the proposed model can effectively improve the utilization efficiency of multi-energy synergy and complementation in the industrial park, and the proposed algorithm can shorten the solution time by more than 50% without significantly affecting the accuracy of the solution.

Suggested Citation

  • Gengshun Liu & Xinfu Song & Chaoshan Xin & Tianbao Liang & Yang Li & Kun Liu, 2024. "Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1908-:d:1346120
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/1908/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/5/1908/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gu, Haifei & Li, Yang & Yu, Jie & Wu, Chen & Song, Tianli & Xu, Jinzhou, 2020. "Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives," Applied Energy, Elsevier, vol. 262(C).
    2. Feng, Jing-Chun & Yan, Jinyue & Yu, Zhi & Zeng, Xuelan & Xu, Weijia, 2018. "Case study of an industrial park toward zero carbon emission," Applied Energy, Elsevier, vol. 209(C), pages 65-78.
    3. X. Zhao & P. B. Luh & J. Wang, 1999. "Surrogate Gradient Algorithm for Lagrangian Relaxation," Journal of Optimization Theory and Applications, Springer, vol. 100(3), pages 699-712, March.
    4. Zhu, Xu & Yang, Jun & Pan, Xueli & Li, Gaojunjie & Rao, Yingqing, 2020. "Regional integrated energy system energy management in an industrial park considering energy stepped utilization," Energy, Elsevier, vol. 201(C).
    5. Hu, Hejuan & Sun, Xiaoyan & Zeng, Bo & Gong, Dunwei & Zhang, Yong, 2022. "Enhanced evolutionary multi-objective optimization-based dispatch of coal mine integrated energy system with flexible load," Applied Energy, Elsevier, vol. 307(C).
    Full references (including those not matched with items on IDEAS)

    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. Xingyun Yan & Lingyu Wang & Mingzhu Fang & Jie Hu, 2022. "How Can Industrial Parks Achieve Carbon Neutrality? Literature Review and Research Prospect Based on the CiteSpace Knowledge Map," Sustainability, MDPI, vol. 15(1), pages 1-29, December.
    2. Zhao, Wenna & Ma, Kai & Yang, Jie & Guo, Shiliang, 2024. "A multi-time scale demand response scheme based on noncooperative game for economic operation of industrial park," Energy, Elsevier, vol. 302(C).
    3. Yan, Kun & Gao, Hanbo & Liu, Rui & Lyu, Yizheng & Wan, Mei & Tian, Jinping & Chen, Lyujun, 2024. "Review on low-carbon development in Chinese industrial parks driven by bioeconomy strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    4. Zhang, Boqun & Wang, Yuanfeng & Pan, Lei & Guo, Xiaohui & Liu, Yinshan & Shi, Chengcheng & Xue, Shaoqin & Wang, Liping & Chang, Xinlei & Fan, Lei, 2025. "Net zero carbon park planning framework: Methodology, application, and economic feasibility analysis," Energy, Elsevier, vol. 325(C).
    5. Baihao Qiao & Hui Xu & Yitong Liu & Jinglong Ye & Hejuan Hu & Li Yan & Tao Wei, 2025. "Economic, Low-Carbon Dispatch of Seasonal Park Integrated Energy System Based on Adjustable Cooling–Heating–Power Ratio," Energies, MDPI, vol. 18(19), pages 1-25, September.
    6. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "Low-carbon economic dispatch and energy sharing method of multiple Integrated Energy Systems from the perspective of System of Systems," Energy, Elsevier, vol. 244(PA).
    7. Li, Yanbin & Hu, Weikun & Zhang, Feng & Li, Yun, 2025. "Multi-objective collaborative operation optimization of park-level integrated energy system clusters considering green power forecasting and trading," Energy, Elsevier, vol. 319(C).
    8. Fan, Songli & Xu, Guodong & Jiang, Baoping & Wu, Zhengtian & Xing, Haijun & Gao, Yang & Ai, Qian, 2025. "Low-carbon economic operation of commercial park-level integrated energy systems incorporating supply and demand flexibility," Energy, Elsevier, vol. 323(C).
    9. Xue, Ruoyu & Wang, Shanshan & Long, Wenqi & Gao, Gengyu & Liu, Donghui & Zhang, Ruiqin, 2021. "Uncovering GHG emission characteristics of industrial parks in Central China via emission inventory and cluster analysis," Energy Policy, Elsevier, vol. 151(C).
    10. Haibing Wang & Chengmin Wang & Weiqing Sun & Muhammad Qasim Khan, 2022. "Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach," Energies, MDPI, vol. 15(19), pages 1-15, October.
    11. Yao, Wenliang & Wang, Chengfu & Yang, Ming & Wang, Kang & Dong, Xiaoming & Zhang, Zhenwei, 2023. "A tri-layer decision-making framework for IES considering the interaction of integrated demand response and multi-energy market clearing," Applied Energy, Elsevier, vol. 342(C).
    12. Mingcheng Zuo & Yuan Xue, 2024. "Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization," Mathematics, MDPI, vol. 12(6), pages 1-24, March.
    13. Chen, Haoxun & Luh, Peter B., 2003. "An alternative framework to Lagrangian relaxation approach for job shop scheduling," European Journal of Operational Research, Elsevier, vol. 149(3), pages 499-512, September.
    14. Anderson, Osten & Bragin, Mikhail A. & Yu, Nanpeng, 2025. "Optimizing deep decarbonization pathways in California with power system planning using surrogate level-based Lagrangian relaxation," Applied Energy, Elsevier, vol. 377(PA).
    15. Anser, Muhammad Khalid & Yousaf, Zahid & Zaman, Khalid & Nassani, Abdelmohsen A. & Alotaibi, Saad M. & Jambari, Hanifah & Khan, Aqeel & Kabbani, Ahmad, 2020. "Determination of resource curse hypothesis in mediation of financial development and clean energy sources: Go-for-green resource policies," Resources Policy, Elsevier, vol. 66(C).
    16. Liang, Ziwen & Mu, Longhua, 2024. "Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory," Energy, Elsevier, vol. 295(C).
    17. Kalantari, Hosein & Ali Ghoreishi-Madiseh, Seyed, 2023. "Study of mine exhaust heat recovery with fully-coupled direct capture and indirect delivery systems," Applied Energy, Elsevier, vol. 334(C).
    18. Fengyuan Yan & Xiaolong Han & Qianwei Cheng & Yamin Yan & Qi Liao & Yongtu Liang, 2022. "Scenario-Based Comparative Analysis for Coupling Electricity and Hydrogen Storage in Clean Oilfield Energy Supply System," Energies, MDPI, vol. 15(6), pages 1-28, March.
    19. Shi, Zhengkun & Yang, Yongbiao & Xu, Qingshan & Wu, Chenyu & Hua, Kui, 2023. "A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance," Applied Energy, Elsevier, vol. 351(C).
    20. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:16:y:2024:i:5:p:1908-:d:1346120. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.