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A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin

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  • Deng, Na
  • Cai, Rongchang
  • Gao, Yuan
  • Zhou, Zhihua
  • He, Guansong
  • Liu, Dongyi
  • Zhang, Awen

Abstract

Rational scheduling strategy is the key to play the economy of district heating and cooling (DHC) system. However, very limited studies have been conducted on it using a suitable model or based on actual operational data, which made the obtained results not realistic or feasible. This paper proposes an actual operational-based optimal scheduling strategy to minimize the daily operation cost of an energy station in Tianjin integrated with electric chiller (EC) system, ground source heat pump (GSHP) system, water thermal energy storage (WTES) system and combined cooling, heating and power (CCHP) system under background of actual cooling load demand. Considering both nonlinear input-output characteristics and discrete working ranges of energy equipments, the mixed-integer nonlinear programming model is used to solve this problem. Results illustrate that the proposed optimal scheduling strategy can achieve zero waste of cooling energy and the cost saving ratio can reach to 24.3%, 34.2%, 47.3% and 63.9% under the load ratio of 75%, 60%, 45% and 30% respectively, compared with the existing scheduling strategy, which shows the cost saving effect is significant especially for the new-built energy station during the initial operation stage.

Suggested Citation

  • Deng, Na & Cai, Rongchang & Gao, Yuan & Zhou, Zhihua & He, Guansong & Liu, Dongyi & Zhang, Awen, 2017. "A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin," Energy, Elsevier, vol. 141(C), pages 1750-1763.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:1750-1763
    DOI: 10.1016/j.energy.2017.10.130
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    8. Blommaert, Maarten & Wack, Y. & Baelmans, M., 2020. "An adjoint optimization approach for the topological design of large-scale district heating networks based on nonlinear models," Applied Energy, Elsevier, vol. 280(C).
    9. Kumar, Devesh & Pindoriya, Naran M., 2024. "A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant," Energy, Elsevier, vol. 290(C).
    10. Wang, Lu & Guo, Leihong & Ren, Jianlin & Kong, Xiangfei, 2022. "Using of heat thermal storage of PCM and solar energy for distributed clean building heating: A multi-level scale-up research," Applied Energy, Elsevier, vol. 321(C).
    11. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    12. Xiao Gong & Fan Li & Bo Sun & Dong Liu, 2020. "Collaborative Optimization of Multi-Energy Complementary Combined Cooling, Heating, and Power Systems Considering Schedulable Loads," Energies, MDPI, vol. 13(4), pages 1-17, February.
    13. Wang, Lu & Yuan, JianJuan & Qiao, Xu & Kong, Xiangfei, 2023. "Optimal rule based double predictive control for the management of thermal energy in a distributed clean heating system," Renewable Energy, Elsevier, vol. 215(C).
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    16. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).

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