IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8041-d1299306.html
   My bibliography  Save this article

Optimal Energy Management Strategy of Clustered Industry Factories Considering Carbon Trading and Supply Chain Coupling

Author

Listed:
  • Jiaying Wang

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310030, China)

  • Chunguang Lu

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310030, China)

  • Shuai Zhang

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Huajiang Yan

    (State Grid Zhejiang Marketing Service Center, Hangzhou 310030, China)

  • Changsen Feng

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

Industrial parks, characterized by the clustering of multiple factories and interconnected energy sources, require optimized operational strategies for their Integrated Energy Systems (IES). These strategies not only aim to conserve energy for industrial users but also relieve the burden on the power supply, reducing carbon emissions. In this context, this paper introduces an optimization strategy tailored to clustered factories, considering the incorporation of carbon trading and supply chain integration throughout the entire production process of each factory. First, a workshop model is established for each factory, accompanied by an energy consumption model that accounts for the strict sequencing of the production process and supply chain integration. Furthermore, energy unit models are devised for the IES and then a low-carbon and economically optimized scheduling model is outlined for the IES within the industrial park, aiming to minimize the total operational cost, including the cost of carbon trading. Finally, case studies are conducted within a paper-making industrial park located in the Zhejiang Province. Various scenarios are compared and analyzed. The numerical results underscore the model’s economic and low-carbon merits, and it offers technical support for energy conservation and emission reduction in paper-making fields.

Suggested Citation

  • Jiaying Wang & Chunguang Lu & Shuai Zhang & Huajiang Yan & Changsen Feng, 2023. "Optimal Energy Management Strategy of Clustered Industry Factories Considering Carbon Trading and Supply Chain Coupling," Energies, MDPI, vol. 16(24), pages 1, December.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8041-:d:1299306
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8041/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/24/8041/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Effrosyni Giama & Elli Kyriaki & Athanasios Papaevaggelou & Agis Papadopoulos, 2023. "Energy and Environmental Analysis of Renewable Energy Systems Focused on Biomass Technologies for Residential Applications: The Life Cycle Energy Analysis Approach," Energies, MDPI, vol. 16(11), pages 1-22, May.
    2. George Ekonomou & George Halkos, 2023. "Exploring the Impact of Economic Growth on the Environment: An Overview of Trends and Developments," Energies, MDPI, vol. 16(11), pages 1-19, June.
    3. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    4. Wei, Xintong & Qiu, Rui & Liang, Yongtu & Liao, Qi & Klemeš, Jiří Jaromír & Xue, Jinjun & Zhang, Haoran, 2022. "Roadmap to carbon emissions neutral industrial parks: Energy, economic and environmental analysis," Energy, Elsevier, vol. 238(PA).
    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. Dawei Feng & Wenchao Xu & Xinyu Gao & Yun Yang & Shirui Feng & Xiaohu Yang & Hailong Li, 2023. "Carbon Emission Prediction and the Reduction Pathway in Industrial Parks: A Scenario Analysis Based on the Integration of the LEAP Model with LMDI Decomposition," Energies, MDPI, vol. 16(21), pages 1-15, October.
    2. Zhang, Zhonglian & Yang, Xiaohui & Li, Moxuan & Deng, Fuwei & Xiao, Riying & Mei, Linghao & Hu, Zecheng, 2023. "Optimal configuration of improved dynamic carbon neutral energy systems based on hybrid energy storage and market incentives," Energy, Elsevier, vol. 284(C).
    3. Halkos, George & Aslanidis, Panagiotis-Stavros, 2024. "Reviewing environmental aspects under the scope of ESG," MPRA Paper 120298, University Library of Munich, Germany.
    4. Xiong, Kang & Hu, Weihao & Cao, Di & Li, Sichen & Zhang, Guozhou & Liu, Wen & Huang, Qi & Chen, Zhe, 2023. "Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach," Renewable Energy, Elsevier, vol. 214(C), pages 216-232.
    5. Anastasovski, Aleksandar, 2023. "What is needed for transformation of industrial parks into potential positive energy industrial parks? A review," Energy Policy, Elsevier, vol. 173(C).
    6. Du, Yanxiang & Liang, Jin & Yang, Shiliang & Hu, Jianhang & Bao, Guirong & Wang, Hua, 2022. "Numerical investigation of the Ni-based catalytic methanation process in a bubbling fluidized bed reactor," Energy, Elsevier, vol. 257(C).
    7. 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.
    8. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
    9. Guo, Xiaopeng & Dong, Yining & Ren, Dongfang, 2023. "CO2 emission reduction effect of photovoltaic industry through 2060 in China," Energy, Elsevier, vol. 269(C).
    10. Olatunji A. Shobande & Simplice A. Asongu, 2021. "The rise and fall of the energy-carbon Kuznets curve: Evidence from Africa," Working Papers 21/069, European Xtramile Centre of African Studies (EXCAS).
    11. Shang, Gang & Xu, Liyun & Tian, Jinzhu & Cai, Dongwei & Xu, Zhun & Zhou, Zhuo, 2023. "A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger," Energy, Elsevier, vol. 274(C).
    12. Jurgis Zagorskas & Zenonas Turskis, 2024. "Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach," Sustainability, MDPI, vol. 16(7), pages 1-21, April.
    13. Wang, Lili & Zhao, Jun & Teng, Junfeng & Dong, Shilong & Wang, Yinglong & Xiang, Shuguang & Sun, Xiaoyan, 2022. "Study on an energy-saving process for separation ethylene elycol mixture through heat-pump, heat-integration and ORC driven by waste-heat," Energy, Elsevier, vol. 243(C).
    14. Gu, Donglin & Guo, Jiahang & Fan, Yurui & Zuo, Qiting & Yu, Lei, 2022. "Evaluating water-energy-food system of Yellow River basin based on type-2 fuzzy sets and Pressure-State-Response model," Agricultural Water Management, Elsevier, vol. 267(C).
    15. Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).
    16. Qiu, Dawei & Xue, Juxing & Zhang, Tingqi & Wang, Jianhong & Sun, Mingyang, 2023. "Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading," Applied Energy, Elsevier, vol. 333(C).
    17. Xinyu Gao & Ze Li & Jiabang Yu & Jiayi Gao & Xiaohu Yang & Bengt Sundén, 2023. "Thermo-Economic Performance Analysis of Modified Latent Heat Storage System for Residential Heating," Energies, MDPI, vol. 16(19), pages 1-19, September.
    18. Wang, Guotao & Liao, Qi & Li, Zhengbing & Zhang, Haoran & Liang, Yongtu & Wei, Xuemei, 2022. "How does soaring natural gas prices impact renewable energy: A case study in China," Energy, Elsevier, vol. 252(C).
    19. Yan, Xinping & He, Yapeng & Fan, Ailong, 2023. "Carbon footprint prediction considering the evolution of alternative fuels and cargo: A case study of Yangtze river ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    20. Woon, Kok Sin & Phuang, Zhen Xin & Taler, Jan & Varbanov, Petar Sabev & Chong, Cheng Tung & Klemeš, Jiří Jaromír & Lee, Chew Tin, 2023. "Recent advances in urban green energy development towards carbon emissions neutrality," Energy, Elsevier, vol. 267(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:gam:jeners:v:16:y:2023:i:24:p:8041-:d:1299306. 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.