IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v327y2022ics0306261922013381.html
   My bibliography  Save this article

Fully analytical model of heating networks for integrated energy systems

Author

Listed:
  • Zhang, Suhan
  • Gu, Wei
  • Zhang, Xiao-ping
  • Lu, Hai
  • Lu, Shuai
  • Yu, Ruizhi
  • Qiu, Haifeng

Abstract

The wide promotion of electric heating has necessitated the combined analysis of the electric power system (EPS) and the district heating network (DHN) for economic and secure improvement. However, the flexibility of DHN brought by thermal dynamics is governed by partial differential equations (PDE), which is difficult to be accurately quantified. The traditional numerical methods usually solve the problem based on discretization, but the extra complexity and numerical oscillation inevitably occur. To address the problem, this paper proposes a fully analytical method (FAM) to describe the thermal dynamics based on a bilateral characteristic line method (CLM), avoiding inaccurate state estimation and operational strategy design. A FAM-based equivalent model is then developed to quantify the relationship between thermal sources and demands and reduce model complexity. Finally, the expression of average temperature is derived to reformulate the optimal energy flow (OEF) problem in the heat and electricity integrated energy system (HE-IES), which accurately reflects the influence of thermal dynamics in a continuous-time domain. Case studies indicate that the proposed FAM significantly improves the analysis accuracy, stability, and efficiency over the traditional numerical methods in simulation and optimization.

Suggested Citation

  • Zhang, Suhan & Gu, Wei & Zhang, Xiao-ping & Lu, Hai & Lu, Shuai & Yu, Ruizhi & Qiu, Haifeng, 2022. "Fully analytical model of heating networks for integrated energy systems," Applied Energy, Elsevier, vol. 327(C).
  • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013381
    DOI: 10.1016/j.apenergy.2022.120081
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922013381
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.120081?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, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
    2. Chen, Yuwei & Guo, Qinglai & Sun, Hongbin & Li, Zhengshuo & Pan, Zhaoguang & Wu, Wenchuan, 2019. "A water mass method and its application to integrated heat and electricity dispatch considering thermal inertias," Energy, Elsevier, vol. 181(C), pages 840-852.
    3. Dancker, Jonte & Wolter, Martin, 2021. "Improved quasi-steady-state power flow calculation for district heating systems: A coupled Newton-Raphson approach," Applied Energy, Elsevier, vol. 295(C).
    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. 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).

    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. Steinegger, Josef & Hammer, Andreas & Wallner, Stefan & Kienberger, Thomas, 2024. "Revolutionizing heat distribution: A method for harnessing industrial waste heat with supra-regional district heating networks," Applied Energy, Elsevier, vol. 372(C).
    2. Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
    3. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    4. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    5. Yang, Weijia & Huang, Yuping & Zhao, Daiqing, 2023. "A coupled hydraulic–thermal dynamic model for the steam network in a heat–electricity integrated energy system," Energy, Elsevier, vol. 263(PC).
    6. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    7. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    8. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan & Zhang, Xian & Wang, Guibin, 2020. "Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems," Applied Energy, Elsevier, vol. 280(C).
    9. Dénarié, A. & Aprile, M. & Motta, M., 2023. "Dynamical modelling and experimental validation of a fast and accurate district heating thermo-hydraulic modular simulation tool," Energy, Elsevier, vol. 282(C).
    10. Wang, Xiaojing & Han, Li & Wang, Chong & Yu, Hongbo & Yu, Xiaojiao, 2023. "A time-scale adaptive dispatching strategy considering the matching of time characteristics and dispatching periods of the integrated energy system," Energy, Elsevier, vol. 267(C).
    11. Wang, Jiangjiang & Huo, Shuojie & Yan, Rujing & Cui, Zhiheng, 2022. "Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties," Energy, Elsevier, vol. 252(C).
    12. Yang, Hongming & Liang, Rui & Yuan, Yuan & Chen, Bowen & Xiang, Sheng & Liu, Junpeng & Zhao, Huan & Ackom, Emmanuel, 2022. "Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data," Applied Energy, Elsevier, vol. 313(C).
    13. Qin, Yuxiao & Liu, Pei & Li, Zheng, 2022. "Multi-timescale hierarchical scheduling of an integrated energy system considering system inertia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    14. Zhai, Junyi & Wang, Sheng & Guo, Lei & Jiang, Yuning & Kang, Zhongjian & Jones, Colin N., 2022. "Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid," Applied Energy, Elsevier, vol. 326(C).
    15. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Tonello, Andrea M. & Qadrdan, Meysam & Wu, Jianzhong, 2022. "Data-driven prosumer-centric energy scheduling using convolutional neural networks," Applied Energy, Elsevier, vol. 308(C).
    16. Zhou, Dezhi & Wu, Chuantao & Sui, Quan & Lin, Xiangning & Li, Zhengtian, 2022. "A novel all-electric-ship-integrated energy cooperation coalition for multi-island microgrids," Applied Energy, Elsevier, vol. 320(C).
    17. Kong, Xiangyu & Liu, Dehong & Wang, Chengshan & Sun, Fangyuan & Li, Shupeng, 2020. "Optimal operation strategy for interconnected microgrids in market environment considering uncertainty," Applied Energy, Elsevier, vol. 275(C).
    18. Boghetti, Roberto & Kämpf, Jérôme H., 2024. "Verification of an open-source Python library for the simulation of district heating networks with complex topologies," Energy, Elsevier, vol. 290(C).
    19. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    20. Tian, Xingtao & Lin, Xiaojie & Zhong, Wei & Zhou, Yi & Cong, Feiyun, 2024. "Optimal dispatch of integrated electricity and heating systems considering the quality-quantity regulation of heating systems to promote renewable energy consumption," Energy, Elsevier, vol. 300(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:appene:v:327:y:2022:i:c:s0306261922013381. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.