2019
DOI: 10.3390/app9010199
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Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Abstract: This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed. The method is based on the skew-normal mixture model (SNMM) and D-vine copulas, which is used to model the marginal distribution and the correlation structure, respectively. Then a cross entropy based importance sampling (CE-IS) is improved to enhance the efficiency of the power system reliability assessment, which is a crucial part of the… Show more

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Cited by 5 publications
(10 citation statements)
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“…For example, the CE‐IS methods in refs. [14,20,21] choose Gaussian or Weibull distribution functions as the IS PDF of load or wind power output. This choice relies more on experience and may be somewhat arbitrary, so the efficiency of CE‐IS may be limited by an improper choice. The correlation between continuous variables is either omitted or not addressed mathematically rigorously in the IS PDF, which causes the efficiency of CE‐IS to decline in some cases.…”
Section: Methodology Of Traditional Cross Entropy Based Importance Samentioning
confidence: 99%
See 4 more Smart Citations
“…For example, the CE‐IS methods in refs. [14,20,21] choose Gaussian or Weibull distribution functions as the IS PDF of load or wind power output. This choice relies more on experience and may be somewhat arbitrary, so the efficiency of CE‐IS may be limited by an improper choice. The correlation between continuous variables is either omitted or not addressed mathematically rigorously in the IS PDF, which causes the efficiency of CE‐IS to decline in some cases.…”
Section: Methodology Of Traditional Cross Entropy Based Importance Samentioning
confidence: 99%
“…Firstly, the expressions of the two OPF models are given below. The minimum load shedding model is [14] min…”
Section: Definition Of Indicator Function I(x)mentioning
confidence: 99%
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