IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225013209.html
   My bibliography  Save this article

Downscaling deconstruction, hybrid semi-mechanism state estimation and cascaded dynamic equivalent modelling of complex district heating networks

Author

Listed:
  • Chen, Xingyuan
  • Hu, Yang
  • Zhao, Jingwei
  • Wang, Yini

Abstract

District heating system is important for modern urban integrated energy system. To reduce carbon emissions, its intelligent operation via cyber-physical fusion is concerned, where dynamic modelling with low complexity, high precision, and fast computability becomes critical. To solve this problem, a systematic approach including downscaling deconstruction, hybrid semi-mechanism (HSM) state estimation and cascaded dynamic equivalent modelling of complex district heating network (DHN) is offered. Firstly, to describe a DHN under an organized mathematical framework, it is expressed as a direct graph whose adjacency matrix is extracted for node clustering via customized Tarjan algorithm. It yields several segmented clusters, seen as the downscaling deconstruction results. Secondly, for cases where physical sensors are missing or damaged, leading to data missing for cascade modelling, a HSM state estimation method is proposed to provide reliable information support. Thirdly, through integrated modelling for cascaded dynamics of segmented clusters, complex thermodynamic properties of a DHN can be achieved. Finally, a DHN in northern China is adopted for validation using measured data. The results indicate that the state estimation method proposed in this paper can effectively achieve high-precision integrated modelling of a DHN, huge potential for optimal operation to improve operational efficiency and reduce operating costs.

Suggested Citation

  • Chen, Xingyuan & Hu, Yang & Zhao, Jingwei & Wang, Yini, 2025. "Downscaling deconstruction, hybrid semi-mechanism state estimation and cascaded dynamic equivalent modelling of complex district heating networks," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013209
    DOI: 10.1016/j.energy.2025.135678
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225013209
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135678?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Longyan & Chen, Meng & Luo, Zhaohui & Zhang, Bowen & Xu, Jian & Wang, Zilu & Tan, Andy C.C., 2024. "Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data," Energy, Elsevier, vol. 291(C).
    2. Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
    3. Sukharev, Mikhail G. & Kulalaeva, Maria A., 2021. "Identification of model flow parameters and model coefficients with the help of integrated measurements of pipeline system operation parameters," Energy, Elsevier, vol. 232(C).
    4. Liu, Zhikai & Zhang, Huang & Wang, Yaran & Fan, Xianwang & You, Shijun & Li, Ang, 2023. "Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system," Energy, Elsevier, vol. 280(C).
    5. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
    6. Gu, Wei & Wang, Jun & Lu, Shuai & Luo, Zhao & Wu, Chenyu, 2017. "Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings," Applied Energy, Elsevier, vol. 199(C), pages 234-246.
    7. Zhu, Dongping & Huang, Xiaogang & Ding, Zhixia & Zhang, Wei, 2024. "Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Li, Pengchao & Guo, Fang & Li, Yongfei & Yang, Xuejing & Yang, Xudong, 2025. "Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems," Energy, Elsevier, vol. 315(C).
    9. Yue, Bao & Wei, Ziqing & Zheng, Chunyuan & Ding, Yunxiao & Li, Bin & Li, Dongdong & Liang, Xingang & Zhai, Xiaoqiang, 2023. "Power consumption prediction of variable refrigerant flow system through data-physics hybrid approach: An online prediction test in office building," Energy, Elsevier, vol. 278(PA).
    10. Boussaid, Taha & Rousset, François & Scuturici, Vasile-Marian & Clausse, Marc, 2024. "Enabling fast prediction of district heating networks transients via a physics-guided graph neural network," Applied Energy, Elsevier, vol. 370(C).
    11. Wang, Jinda & Kong, Fansi & Pan, Baoqiang & Zheng, Jinfu & Xue, Puning & Sun, Chunhua & Qi, Chengying, 2024. "Low-order gray-box modeling of heating buildings and the progressive dimension reduction identification of uncertain model parameters," Energy, Elsevier, vol. 294(C).
    12. Nord, Natasa & Shakerin, Mohammad & Tereshchenko, Tymofii & Verda, Vittorio & Borchiellini, Romano, 2021. "Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects," Energy, Elsevier, vol. 222(C).
    13. Favaro, Pietro & Dolányi, Mihály & Vallée, François & Toubeau, Jean-François, 2024. "Neural network informed day-ahead scheduling of pumped hydro energy storage," Energy, Elsevier, vol. 289(C).
    14. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    15. Wang, Tong & Wu, Yan & Zhu, Keming & Cen, Jianmeng & Wang, Shaohong & Huang, Yuqi, 2025. "Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data," Energy, Elsevier, vol. 317(C).
    16. Zhang, Wencan & Xie, Yi & He, Hancheng & Long, Zhuoru & Zhuang, Liyang & Zhou, Jianjie, 2025. "Multi-physics coupling model parameter identification of lithium-ion battery based on data driven method and genetic algorithm," Energy, Elsevier, vol. 314(C).
    17. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," 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. Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).
    2. Olamilekan E. Tijani & Sylvain Serra & Patrick Lanusse & Rachid Malti & Hugo Viot & Jean-Michel Reneaume, 2025. "Grey-Box Modelling of District Heating Networks Using Modified LPV Models," Energies, MDPI, vol. 18(7), pages 1-32, March.
    3. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    4. Zhu, Yunyi & Xie, Bin & Wang, Anqi & Qian, Zheng, 2025. "Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    5. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Pan, Bo & Qi, Shiqiang, 2020. "Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind power," Applied Energy, Elsevier, vol. 275(C).
    6. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    7. Vandermeulen, Annelies & Van Oevelen, Tijs & van der Heijde, Bram & Helsen, Lieve, 2020. "A simulation-based evaluation of substation models for network flexibility characterisation in district heating networks," Energy, Elsevier, vol. 201(C).
    8. 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.
    9. Zhang, Suhan & Gu, Wei & Lu, Hai & Qiu, Haifeng & Lu, Shuai & Wang, Dada & Liang, Junyu & Li, Wenyun, 2021. "Superposition-principle based decoupling method for energy flow calculation in district heating networks," Applied Energy, Elsevier, vol. 295(C).
    10. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    11. Zheng, Jinfu & Zhou, Zhigang & Zhao, Jianing & Wang, Jinda, 2018. "Integrated heat and power dispatch truly utilizing thermal inertia of district heating network for wind power integration," Applied Energy, Elsevier, vol. 211(C), pages 865-874.
    12. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    13. Chen, Bowen & Lin, Yonggang & Gu, Yajing & Feng, Xiangheng & Cao, Zhongpeng & Sun, Yong, 2025. "A novel active wake control strategy based on LiDAR for wind farms," Energy, Elsevier, vol. 317(C).
    14. Tian, Xingtao & Lin, Xiaojie & Zhong, Wei & Zhou, Yi, 2023. "Analytical sensitivity analysis of radial natural gas networks," Energy, Elsevier, vol. 263(PC).
    15. Wang, Jinda & Zhou, Zhigang & Zhao, Jianing & Zheng, Jinfu, 2018. "Improving wind power integration by a novel short-term dispatch model based on free heat storage and exhaust heat recycling," Energy, Elsevier, vol. 160(C), pages 940-953.
    16. Zeynali, Saeed & Nasiri, Nima & Ravadanegh, Sajad Najafi & Marzband, Mousa, 2022. "A three-level framework for strategic participation of aggregated electric vehicle-owning households in local electricity and thermal energy markets," Applied Energy, Elsevier, vol. 324(C).
    17. Zhang, Suhan & Gu, Wei & Qiu, Haifeng & Yao, Shuai & Pan, Guangsheng & Chen, Xiaogang, 2021. "State estimation models of district heating networks for integrated energy system considering incomplete measurements," Applied Energy, Elsevier, vol. 282(PA).
    18. Wu, Chenyu & Gu, Wei & Xu, Yinliang & Jiang, Ping & Lu, Shuai & Zhao, Bo, 2018. "Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers," Applied Energy, Elsevier, vol. 232(C), pages 607-616.
    19. Zheng, Jinfu & Zhou, Zhigang & Zhao, Jianing & Hu, Songtao & Wang, Jinda, 2021. "Effects of intermittent heating on an integrated heat and power dispatch system for wind power integration and corresponding operation regulation," Applied Energy, Elsevier, vol. 287(C).
    20. Wang, Dan & Zhi, Yun-qiang & Jia, Hong-jie & Hou, Kai & Zhang, Shen-xi & Du, Wei & Wang, Xu-dong & Fan, Meng-hua, 2019. "Optimal scheduling strategy of district integrated heat and power system with wind power and multiple energy stations considering thermal inertia of buildings under different heating regulation modes," Applied Energy, Elsevier, vol. 240(C), pages 341-358.

    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:eee:energy:v:322:y:2025:i:c:s0360544225013209. 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.