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

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  • 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.

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  • 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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    1. Guo, Rui & Shamsi, Mohammad Haris & Sharifi, Mohsen & Saelens, Dirk, 2025. "Exploring uncertainty in district heat demand through a probabilistic building characterization approach," Applied Energy, Elsevier, vol. 377(PA).

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