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Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access

Author

Listed:
  • Lin, Xiaojie
  • Mao, Yihui
  • Chen, Jiaying
  • Zhong, Wei

Abstract

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty.

Suggested Citation

  • Lin, Xiaojie & Mao, Yihui & Chen, Jiaying & Zhong, Wei, 2023. "Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access," Applied Energy, Elsevier, vol. 349(C).
  • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009935
    DOI: 10.1016/j.apenergy.2023.121629
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    References listed on IDEAS

    as
    1. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    2. Wang, Jinda & Zhou, Zhigang & Zhao, Jianing & Zheng, Jinfu & Guan, Zhiqiang, 2019. "Optimizing for clean-heating improvements in a district energy system with high penetration of wind power," Energy, Elsevier, vol. 175(C), pages 1085-1099.
    3. Steinegger, Josef & Wallner, Stefan & Greiml, Matthias & Kienberger, Thomas, 2023. "A new quasi-dynamic load flow calculation for district heating networks," Energy, Elsevier, vol. 266(C).
    4. Chen, Xi & Zhao, Tian & Chen, Qun, 2022. "An online parameter identification and real-time optimization platform for thermal systems and its application," Applied Energy, Elsevier, vol. 307(C).
    5. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    6. Zhang, Bo & Qiu, Rui & Liao, Qi & Liang, Yongtu & Ji, Haoran & Jing, Rui, 2022. "Design and operation optimization of city-level off-grid hydro–photovoltaic complementary system," Applied Energy, Elsevier, vol. 306(PB).
    7. Sun, Qie & Fu, Yu & Lin, Haiyang & Wennersten, Ronald, 2022. "A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties," Applied Energy, Elsevier, vol. 314(C).
    8. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    9. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    10. González-Ordiano, Jorge Ángel & Mühlpfordt, Tillmann & Braun, Eric & Liu, Jianlei & Çakmak, Hüseyin & Kühnapfel, Uwe & Düpmeier, Clemens & Waczowicz, Simon & Faulwasser, Timm & Mikut, Ralf & Hagenmeye, 2021. "Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow," Applied Energy, Elsevier, vol. 302(C).
    11. Hofmeister, Markus & Mosbach, Sebastian & Hammacher, Jörg & Blum, Martin & Röhrig, Gerd & Dörr, Christoph & Flegel, Volker & Bhave, Amit & Kraft, Markus, 2022. "Resource-optimised generation dispatch strategy for district heating systems using dynamic hierarchical optimisation," Applied Energy, Elsevier, vol. 305(C).
    12. Chen, Qun & Fu, Rong-Huan & Xu, Yun-Chao, 2015. "Electrical circuit analogy for heat transfer analysis and optimization in heat exchanger networks," Applied Energy, Elsevier, vol. 139(C), pages 81-92.
    13. Zhong, Wei & Chen, Jiaying & Zhou, Yi & Li, Zhongbo & Lin, Xiaojie, 2019. "Network flexibility study of urban centralized heating system: Concept, modeling and evaluation," Energy, Elsevier, vol. 177(C), pages 334-346.
    14. Pickering, B. & Choudhary, R., 2019. "District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles," Applied Energy, Elsevier, vol. 236(C), pages 1138-1157.
    15. Fan, Wei & Ju, Liwei & Tan, Zhongfu & Li, Xiangguang & Zhang, Amin & Li, Xudong & Wang, Yueping, 2023. "Two-stage distributionally robust optimization model of integrated energy system group considering energy sharing and carbon transfer," Applied Energy, Elsevier, vol. 331(C).
    16. 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).
    17. 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).
    18. Liu, Wenxia & Huang, Yuchen & Li, Zhengzhou & Yang, Yue & Yi, Fang, 2020. "Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response," Energy, Elsevier, vol. 198(C).
    Full references (including those not matched with items on IDEAS)

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