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Coordinated dispatch of multi-energy system with district heating network: Modeling and solution strategy

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  • Lu, Shuai
  • Gu, Wei
  • Zhou, Jinhui
  • Zhang, Xuesong
  • Wu, Chenyu

Abstract

This paper proposes a dispatch strategy for the multi-energy system (MES) that utilizes the district heating network (DHN) to realize the heat power interaction of multi cogeneration system, so as to improve the operational flexibility. A novel model for the DHN that couples multi heat sources and is under quantity regulation is proposed, based on which an optimal coordinated dispatch model (OCDM) for the MES is presented comprehensively. The DHN model comprises dispatch constraints (DCs) and linear system of temperature correction equations (TCEs). The DCs form part of the OCDM constraints, where the mass flow temperatures are treated as constants and the mass flow rates and heat power are decision variables. The TCEs are employed to update the mass flow temperatures in DCs. Subsequently, an iterative solving strategy for the OCDM is proposed. The convergence of the solving strategy is analyzed and an effective method is introduced to ensure the convergence. A modified system based on an actual MES in Changchun, China is researched in the case study. Three cases are given to verify the effectiveness of the dispatch strategy and the solution method as well as study the influence of wind power penetration and heating level to the results.

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  • Lu, Shuai & Gu, Wei & Zhou, Jinhui & Zhang, Xuesong & Wu, Chenyu, 2018. "Coordinated dispatch of multi-energy system with district heating network: Modeling and solution strategy," Energy, Elsevier, vol. 152(C), pages 358-370.
    • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:358-370
    DOI: 10.1016/j.energy.2018.03.088
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    15. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
    16. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    17. Chen, Dongwen & Li, Yong & Abbas, Zulkarnain & Li, Dehong & Wang, Ruzhu, 2022. "Network flow calculation based on the directional nodal potential method for meshed heating networks," Energy, Elsevier, vol. 243(C).
    18. Li, Hangxin & Wang, Shengwei, 2022. "Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings," Energy, Elsevier, vol. 244(PA).
    19. Hosseini, Seyed Hamid Reza & Allahham, Adib & Walker, Sara Louise & Taylor, Phil, 2020. "Optimal planning and operation of multi-vector energy networks: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).

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