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Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties

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  • Dianzheng Fu

    (Key Laboratory of Networked Control Systems, Digital Factory Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

  • Tianji Yang

    (Key Laboratory of Networked Control Systems, Digital Factory Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

  • Yize Huang

    (Key Laboratory of Networked Control Systems, Digital Factory Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

  • Yiming Tong

    (Key Laboratory of Networked Control Systems, Digital Factory Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

Abstract

The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply–demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply–demand, policy requirement constraints, and the financial minimization objective. The results indicate that biofuel allocation and expansion schemes are sensitive to the multiple and compound uncertainty inputs, and the corresponding biofuel-deficit change trends of three heat sources are obviously distinct with the system’s condition, varying due to the complicated interactions of the system’s components. Beyond that, a potential trade-off relationship between the heating cost and the constraint-violation risk can be obtained by observing system responses with thermalization coefficient varying.

Suggested Citation

  • Dianzheng Fu & Tianji Yang & Yize Huang & Yiming Tong, 2022. "Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties," Energies, MDPI, vol. 15(15), pages 1-21, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5406-:d:872425
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    References listed on IDEAS

    as
    1. Wang, S. & Xie, Y.L. & Huang, G.H. & Yao, Y. & Wang, S.Y. & Li, Y.F., 2021. "A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China," Energy Policy, Elsevier, vol. 149(C).
    2. 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.
    3. Huang, G. H. & Baetz, B. W. & Patry, G. G., 1995. "Grey fuzzy integer programming: An application to regional waste management planning under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 17-38, March.
    4. Lin, Q.G. & Huang, G.H., 2009. "Planning of energy system management and GHG-emission control in the Municipality of Beijing--An inexact-dynamic stochastic programming model," Energy Policy, Elsevier, vol. 37(11), pages 4463-4473, November.
    5. Cooper, William W. & Deng, H. & Huang, Zhimin & Li, Susan X., 2004. "Chance constrained programming approaches to congestion in stochastic data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 155(2), pages 487-501, June.
    6. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2021. "Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and," Energy, Elsevier, vol. 232(C).
    7. Yamchi, Hamid Bakhshi & Safari, Amin & Guerrero, Josep M., 2021. "A multi-objective mixed integer linear programming model for integrated electricity-gas network expansion planning considering the impact of photovoltaic generation," Energy, Elsevier, vol. 222(C).
    8. Ji, Ling & Huang, Guo-He & Huang, Lu-Cheng & Xie, Yu-Lei & Niu, Dong-Xiao, 2016. "Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty," Energy, Elsevier, vol. 109(C), pages 920-932.
    9. A. Charnes & W. W. Cooper, 1983. "Response to "Decision Problems Under Risk and Chance Constrained Programming: Dilemmas in the Transition"," Management Science, INFORMS, vol. 29(6), pages 750-753, June.
    10. Huang, Guo H. & Baetz, Brian W. & Patry, Gilles G., 1995. "Grey integer programming: An application to waste management planning under uncertainty," European Journal of Operational Research, Elsevier, vol. 83(3), pages 594-620, June.
    11. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    12. Quddus, Md Abdul & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Yu, Fei & Bian, Linkan, 2018. "A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network," International Journal of Production Economics, Elsevier, vol. 195(C), pages 27-44.
    13. Li, Y.P. & Huang, G.H. & Chen, X., 2011. "Planning regional energy system in association with greenhouse gas mitigation under uncertainty," Applied Energy, Elsevier, vol. 88(3), pages 599-611, March.
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