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A Nonlinear Analytical Algorithm for Predicting the Probabilistic Mass Flow of a Radial District Heating Network

Author

Listed:
  • Guoqiang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Wenxue Wang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Yi Wu

    (State Grid Jiangsu Power Company, Nanjing 210024, Jiangsu Province, China)

  • Wei Hu

    (State Grid Jiangsu Power Company, Nanjing 210024, Jiangsu Province, China)

  • Zijun Yang

    (State Grid Jiangsu Power Company, Nanjing 210024, Jiangsu Province, China)

  • Zhinong Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Haixiang Zang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Sheng Chen

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

This paper develops a nonlinear analytical algorithm for predicting the probabilistic mass flow of radial district heating networks based on the principle of heat transfer and basic pipe network theory. The use of a nonlinear mass flow model provides more accurate probabilistic operation information for district heating networks with stochastic heat demands than existing probabilistic power flow analytical algorithms based on a linear mass flow model. Moreover, the computation is efficient because our approach does not require repeated nonlinear mass flow calculations. Test results on a 23-node district heating network case indicate that the proposed approach provides an accurate and efficient estimation of probabilistic operation conditions.

Suggested Citation

  • Guoqiang Sun & Wenxue Wang & Yi Wu & Wei Hu & Zijun Yang & Zhinong Wei & Haixiang Zang & Sheng Chen, 2019. "A Nonlinear Analytical Algorithm for Predicting the Probabilistic Mass Flow of a Radial District Heating Network," Energies, MDPI, vol. 12(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1215-:d:218072
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    References listed on IDEAS

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    Cited by:

    1. Bott, Andreas & Janke, Tim & Steinke, Florian, 2023. "Deep learning-enabled MCMC for probabilistic state estimation in district heating grids," Applied Energy, Elsevier, vol. 336(C).
    2. Maurer, Jona & Tschuch, Nicolai & Krebs, Stefan & Bhattacharya, Kankar & Cañizares, Claudio & Hohmann, Sören, 2023. "Toward transactive control of coupled electric power and district heating networks," Applied Energy, Elsevier, vol. 332(C).
    3. Tomasz Janusz Teleszewski & Dorota Anna Krawczyk & Antonio Rodero, 2019. "Reduction of Heat Losses Using Quadruple Heating Pre-Insulated Networks: A Case Study," Energies, MDPI, vol. 12(24), pages 1-12, December.
    4. Dorota Anna Krawczyk & Tomasz Janusz Teleszewski, 2019. "Reduction of Heat Losses in a Pre-Insulated Network Located in Central Poland by Lowering the Operating Temperature of the Water and the Use of Egg-shaped Thermal Insulation: A Case Study," Energies, MDPI, vol. 12(11), pages 1-12, June.

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