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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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    1. Lun Yang & Xia Zhao & Xinyi Li & Wei Yan, 2018. "Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems," Energies, MDPI, vol. 11(4), pages 1-21, April.
    2. Liu, Xuezhi & Wu, Jianzhong & Jenkins, Nick & Bagdanavicius, Audrius, 2016. "Combined analysis of electricity and heat networks," Applied Energy, Elsevier, vol. 162(C), pages 1238-1250.
    3. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
    4. Chaudry, Modassar & Wu, Jianzhong & Jenkins, Nick, 2013. "A sequential Monte Carlo model of the combined GB gas and electricity network," Energy Policy, Elsevier, vol. 62(C), pages 473-483.
    5. Yining Zhang & Yubin He & Mingyu Yan & Chuangxin Guo & Yi Ding, 2018. "Linearized Stochastic Scheduling of Interconnected Energy Hubs Considering Integrated Demand Response and Wind Uncertainty," Energies, MDPI, vol. 11(9), pages 1-23, September.
    6. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    7. Liu, Xuezhi & Mancarella, Pierluigi, 2016. "Modelling, assessment and Sankey diagrams of integrated electricity-heat-gas networks in multi-vector district energy systems," Applied Energy, Elsevier, vol. 167(C), pages 336-352.
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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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