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A multi-fuel management model for a community-level district heating system under multiple uncertainties

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  • Fu, D.Z.
  • Zheng, Z.Y.
  • Shi, H.B.
  • Xiao, Rui
  • Huang, G.H.
  • Li, Y.P.

Abstract

In this study, an interval two-stage double-stochastic single-sided fuzzy chance-constrained programming model is developed for supporting fuel management of a community-level district heating system (DHS) fed with both traditional fossil fuels and renewable biofuels under multiple uncertainties. The proposed model is based on the integration of interval parameter programming and single-sided fuzzy chance-constrained programming within an improved stochastic programming framework to tackle the uncertainties expressed as crisp intervals, fuzzy relationship, and probability distributions. Through transforming and solving the model, the related fuzzy and stochastic information can be effectively reflected in the generated solutions. A real fuel management case of a DHS located in Junpu New District of Dalian is utilized to demonstrate the model applicability. The obtained solutions provides an effective linkage in terms of both ‘‘quality’’ and ‘‘quantity’’ aspects for fuel management under various scenarios associated with multiple factors, and thus can help the decision makers to identify desired fuel allotment patterns. Moreover, this study is also useful for decision makers to address the other challenges (e.g. the imbalance between fuel supply and demand, the contradiction between air-pollution emission and environmental protection, as well as the tradeoff between the total heating cost and system satisfaction degree) generated in the fuel management processes.

Suggested Citation

  • Fu, D.Z. & Zheng, Z.Y. & Shi, H.B. & Xiao, Rui & Huang, G.H. & Li, Y.P., 2017. "A multi-fuel management model for a community-level district heating system under multiple uncertainties," Energy, Elsevier, vol. 128(C), pages 337-356.
    • Handle: RePEc:eee:energy:v:128:y:2017:i:c:p:337-356
    DOI: 10.1016/j.energy.2017.04.031
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    Cited by:

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    2. Ji, Ling & Zhang, Beibei & Huang, Guohe & Wang, Peng, 2020. "A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China," Energy, Elsevier, vol. 190(C).
    3. Krzysztof Pikoń & Piotr Krawczyk & Krzysztof Badyda & Magdalena Bogacka, 2019. "Predictive Analysis of Waste Co-Combustion with Fossil Fuels Using the Life Cycle Assessment (LCA) Methodology," Energies, MDPI, vol. 12(19), pages 1-11, September.
    4. Khishtandar, Soheila, 2019. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design," Applied Energy, Elsevier, vol. 236(C), pages 183-195.

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