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A new combined clustering method to Analyse the potential of district heating networks at large-scale

Author

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  • Marquant, Julien F.
  • Bollinger, L. Andrew
  • Evins, Ralph
  • Carmeliet, Jan

Abstract

For effective integration of large amounts of renewables and high-efficiency energy technologies, their benefits have to be quantified. Network-level energy optimisation approaches can determine the optimal location of generation technologies within a region and the optimal layout of energy distribution networks to link them. Mixed-integer linear programming (MILP) formulations are generally employed and this is often a burden for large scale models as the computational time drastically increases with the problem size.

Suggested Citation

  • Marquant, Julien F. & Bollinger, L. Andrew & Evins, Ralph & Carmeliet, Jan, 2018. "A new combined clustering method to Analyse the potential of district heating networks at large-scale," Energy, Elsevier, vol. 156(C), pages 73-83.
    • Handle: RePEc:eee:energy:v:156:y:2018:i:c:p:73-83
    DOI: 10.1016/j.energy.2018.05.027
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    Citations

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    Cited by:

    1. Gjoka, Kristian & Rismanchi, Behzad & Crawford, Robert H., 2023. "Fifth-generation district heating and cooling systems: A review of recent advancements and implementation barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    2. Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
    3. Maria Jebamalai, Joseph & Marlein, Kurt & Laverge, Jelle & Vandevelde, Lieven & van den Broek, Martijn, 2019. "An automated GIS-based planning and design tool for district heating: Scenarios for a Dutch city," Energy, Elsevier, vol. 183(C), pages 487-496.
    4. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    5. Wang, Wei & Jing, Rui & Zhao, Yingru & Zhang, Chuan & Wang, Xiaonan, 2020. "A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization," Applied Energy, Elsevier, vol. 270(C).
    6. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2022. "A review on the integration and optimization of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    7. Domínguez, R. & Vitali, S., 2021. "Multi-chronological hierarchical clustering to solve capacity expansion problems with renewable sources," Energy, Elsevier, vol. 227(C).
    8. Kiani-Moghaddam, Mohammad & Soltani, Mohsen N. & Kalogirou, Soteris A. & Mahian, Omid & Arabkoohsar, Ahmad, 2023. "A review of neighborhood level multi-carrier energy hubs—uncertainty and problem-solving process," Energy, Elsevier, vol. 281(C).

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