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Analysis of Extreme Random Uncertainty in Energy and Environment Systems for Coal-Dependent City by a Copula-Based Interval Cost–Benefit Stochastic Approach

Author

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  • Yanzheng Liu

    (School of Future Technology, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Jicong Tan

    (School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Zhao Wei

    (School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Ying Zhu

    (Shaanxi Key Laboratory of Environmental Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
    Department of Civil and Environmental Engineering, School of Engineering, Tohoku University, Sendai 980-8577, Japan)

  • Shiyu Chang

    (School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Yexin Li

    (Ankang Environmental Engineering Design Limited Company, Ankang 725000, China)

  • Shaoyi Li

    (Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China)

  • Yong Guo

    (Ankang Environmental Engineering Design Limited Company, Ankang 725000, China)

Abstract

Extreme random events will interfere with the inversion analysis of energy and environment systems (EES) and make the planning schemes unreliable. A Copula-based interval cost–benefit stochastic programming (CICS) is proposed to deal with extreme random uncertainties. Taking Yulin city as an example, there are nine constraint-violation scenarios and six coal-reduction scenarios are designed. The results disclose that (i) both system cost and pollutant emission would decrease as the industrial energy supply constraint-violation level increases; (ii) when the primary and secondary energy output increases by 9% and 13%, respectively, and industrial coal supply decreases by 40%, the coal-dependent index of the system would be the lowest, and the corresponding system profitability could reach [29.3, 53.0] %; (iii) compared with the traditional chance-constrained programming, Copula-based stochastic programming can reflect more uncertain information and achieve a higher marginal net present value rate. Overall, the CICS-EES model offers a novel approach to gain insight into the tradeoff between system reliability and profitability.

Suggested Citation

  • Yanzheng Liu & Jicong Tan & Zhao Wei & Ying Zhu & Shiyu Chang & Yexin Li & Shaoyi Li & Yong Guo, 2024. "Analysis of Extreme Random Uncertainty in Energy and Environment Systems for Coal-Dependent City by a Copula-Based Interval Cost–Benefit Stochastic Approach," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:745-:d:1319443
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    References listed on IDEAS

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    1. Cabrales, Sergio & Valencia, Carlos & Ramírez, Carlos & Ramírez, Andrés & Herrera, Juan & Cadena, Angela, 2022. "Stochastic cost-benefit analysis to assess new infrastructure to improve the reliability of the natural gas supply," Energy, Elsevier, vol. 246(C).
    2. 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.
    3. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Yin, S., 2018. "Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China," Applied Energy, Elsevier, vol. 212(C), pages 834-849.
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