IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v296y2024ics0360544224008284.html
   My bibliography  Save this article

Study on time-of-use pricing method for steam heating system considering user response characteristics and thermal storage capacity

Author

Listed:
  • Zhong, Wei
  • Dai, Zhe
  • Lin, Xiaojie
  • Pan, Guanchang

Abstract

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply.

Suggested Citation

  • Zhong, Wei & Dai, Zhe & Lin, Xiaojie & Pan, Guanchang, 2024. "Study on time-of-use pricing method for steam heating system considering user response characteristics and thermal storage capacity," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224008284
    DOI: 10.1016/j.energy.2024.131056
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224008284
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131056?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. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    2. Bagge, Hans & Johansson, Dennis, 2011. "Measurements of household electricity and domestic hot water use in dwellings and the effect of different monitoring time resolution," Energy, Elsevier, vol. 36(5), pages 2943-2951.
    3. Zhuang, Wennan & Zhou, Suyang & Chen, Jinyi & Gu, Wei, 2024. "Operation optimization of electricity-steam coupled industrial energy system considering steam accumulator," Energy, Elsevier, vol. 289(C).
    4. Yan, Aibin & Zhao, Jun & An, Qingsong & Zhao, Yulong & Li, Hailong & Huang, Yrjö Jun, 2013. "Hydraulic performance of a new district heating systems with distributed variable speed pumps," Applied Energy, Elsevier, vol. 112(C), pages 876-885.
    5. Guelpa, Elisa & Verda, Vittorio, 2019. "Thermal energy storage in district heating and cooling systems: A review," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    7. Merlet, Yannis & Baviere, Roland & Vasset, Nicolas, 2022. "Formulation and assessment of multi-objective optimal sizing of district heating network," Energy, Elsevier, vol. 252(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. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    2. Ioan Sarbu & Matei Mirza & Daniel Muntean, 2022. "Integration of Renewable Energy Sources into Low-Temperature District Heating Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-28, September.
    3. Capone, Martina & Guelpa, Elisa & Mancò, Giulia & Verda, Vittorio, 2021. "Integration of storage and thermal demand response to unlock flexibility in district multi-energy systems," Energy, Elsevier, vol. 237(C).
    4. Riccardo Toffanin & Paola Caputo & Marco Belliardi & Vinicio Curti, 2022. "Low and Ultra-Low Temperature District Heating Equipped by Heat Pumps—An Analysis of the Best Operative Conditions for a Swiss Case Study," Energies, MDPI, vol. 15(9), pages 1-19, May.
    5. A. J. Jin & C. Li & J. Su & J. Tan, 2022. "Fundamental Studies of Smart Distributed Energy Resources along with Energy Blockchain," Energies, MDPI, vol. 15(21), pages 1-12, October.
    6. Muniak, Damian Piotr, 2014. "A new methodology to determine the pre-setting of the control valve in a heating installation. A general model," Applied Energy, Elsevier, vol. 135(C), pages 35-42.
    7. Zheng, Jinfu & Zhou, Zhigang & Zhao, Jianing & Wang, Jinda, 2018. "Effects of the operation regulation modes of district heating system on an integrated heat and power dispatch system for wind power integration," Applied Energy, Elsevier, vol. 230(C), pages 1126-1139.
    8. Cosgrove, Paul & Roulstone, Tony & Zachary, Stan, 2023. "Intermittency and periodicity in net-zero renewable energy systems with storage," Renewable Energy, Elsevier, vol. 212(C), pages 299-307.
    9. Egging-Bratseth, Ruud & Kauko, Hanne & Knudsen, Brage Rugstad & Bakke, Sara Angell & Ettayebi, Amina & Haufe, Ina Renate, 2021. "Seasonal storage and demand side management in district heating systems with demand uncertainty," Applied Energy, Elsevier, vol. 285(C).
    10. Zhang, Lipeng & Gudmundsson, Oddgeir & Thorsen, Jan Eric & Li, Hongwei & Li, Xiaopeng & Svendsen, Svend, 2016. "Method for reducing excess heat supply experienced in typical Chinese district heating systems by achieving hydraulic balance and improving indoor air temperature control at the building level," Energy, Elsevier, vol. 107(C), pages 431-442.
    11. Woltmann, Stefan & Kittel, Julia, 2022. "Development and implementation of multi-agent systems for demand response aggregators in an industrial context," Applied Energy, Elsevier, vol. 314(C).
    12. Yan, Jingjing & Zhang, Huan & Wang, Yaran & Zhu, Zhaozhe & Bai, He & Li, Qicheng & You, Shijun, 2024. "Pump-stopping-induced hydraulic oscillations in long-distance district heating system: Modelling and a comprehensive analysis of critical factors," Energy, Elsevier, vol. 294(C).
    13. Hirvijoki, Eero & Hirvonen, Janne, 2022. "The potential of intermediate-to-deep geothermal boreholes for seasonal storage of district heat," Renewable Energy, Elsevier, vol. 198(C), pages 825-832.
    14. Xu, Ruoyu & Liu, Xiaochen & Liu, Xiaohua & Zhang, Tao, 2024. "Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports," Energy, Elsevier, vol. 298(C).
    15. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).
    16. Schumacher, Felix & Nussbaumer, Thomas, 2023. "Four approaches for the year-round operation of wood-fired heating plants with low pollutant emissions," Energy, Elsevier, vol. 278(C).
    17. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    18. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    19. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    20. Annala, Salla & Ruggiero, Salvatore & Kangas, Hanna-Liisa & Honkapuro, Samuli & Ohrling, Tiina, 2022. "Impact of home market on business development and internationalization of demand response firms," Energy, Elsevier, vol. 242(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:energy:v:296:y:2024:i:c:s0360544224008284. 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/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.