2020
DOI: 10.1016/j.jhydrol.2020.124832
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Coupling the two-level programming and copula for optimizing energy-water nexus system management – A case study of Henan Province

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Cited by 28 publications
(11 citation statements)
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“…Therefore, the regulation of the human-water relationship in Henan Province was mainly focused on the above-mentioned aspects. After determining the above content, we adopt a step-by-step optimization method [53] to set different control scenarios (change the index value with a fixed step) within a reasonable range of indicators to achieve a dynamic evaluation of human-water harmony. After simulating through constant replacement of the control scenarios, and a control scenario that meets the target setting is finally found.…”
Section: Regulation Of Human-water Harmony Degree In Henan Provincementioning
confidence: 99%
“…Therefore, the regulation of the human-water relationship in Henan Province was mainly focused on the above-mentioned aspects. After determining the above content, we adopt a step-by-step optimization method [53] to set different control scenarios (change the index value with a fixed step) within a reasonable range of indicators to achieve a dynamic evaluation of human-water harmony. After simulating through constant replacement of the control scenarios, and a control scenario that meets the target setting is finally found.…”
Section: Regulation Of Human-water Harmony Degree In Henan Provincementioning
confidence: 99%
“…The P values of probability distribution functions of energy loads were all >.05 (eg, the P values of electricity loads under S3 in L, M, and H would be .154, .362, and .326, respectively), indicating Normal distribution could fit well for the probability distributions of them by using Anderson-Darling test. 40,41 Figure 2 shows the probability distribution of the average energy load under different demand levels. The probability distribution of the energy loads was assumed to be a normal distribution.…”
Section: Assumption and Datamentioning
confidence: 99%
“…CVaR is conditional value at risk, it is a better risk measurement technology than value at risk (VaR) proposed by rockafeuar and uryasev in 1997, which means the average loss value of a portfolio when the loss of the portfolio exceeds a given var [25]- [29]. When using copula function to calculate CVaR of portfolio, the analytical formula of CVaR is not easy to solve.…”
Section: ) Risk Measurement Model Of Portfolio Based On Cvarmentioning
confidence: 99%