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

Yanzheng Liu, Jicong Tan, Zhao Wei, Ying Zhu (), Shiyu Chang, Yexin Li, Shaoyi Li and Yong Guo
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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
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

Sustainability, 2024, vol. 16, issue 2, 1-22

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.

Keywords: extreme random uncertainty; energy and environment systems; coal-dependent; Copula; cost–benefit; planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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