Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Cai, Jianguo; Xu, Qingshan; Cao, Minjian; Yang, Yong‐Biao

doi:10.3390/app9010199

Cited by 5 publications

(10 citation statements)

References 44 publications

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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%

“…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%

“…The CE-IS method used in refs. [14,20] sets the IS PDF as a multivariate Gaussian PDF, where the correlation coefficient matrix (CCM) of all random variables is the unit matrix. That means the parametric function form chosen for the IS PDF of ξ by this method is:…”

Section: Limitations Of Traditional Cross Entropy Based Importance Sa...mentioning

confidence: 99%

“…Firstly, the expressions of the two OPF models are given below. The minimum load shedding model is [14]

\begin{matrix} left \min_{P_{normalC}, P_{LD, sh}, P_{W}} \sum_{b = 1}^{n_{LD}} P_{normalLD, normalsh, b} \\ left s . normalt . \sum_{i = 1}^{n_{C}} P_{C, i} + \sum_{w = 1}^{n_{W}} P_{W, w} = \sum_{b = 1}^{n_{LD}} false(x_{normalLD, b} - P_{LD, sh, b} false) \\ left x_{C} \circ P_{normalC, normalmin} \leq P_{C} \leq x_{C} \circ P_{normalC, normalmax} \\ left 0 \leq P_{W} \leq x_{W} \\ left 0 \leq P_{normalLD, normalsh} \leq x_{LD} \\ left x_{L} \circ P_{normalL, normalmin} \leq P_{L} \leq x_{L} \circ P_{normalL, normalmax} \\ left P_{L} = S F_{normalL, W} P_{W} + S F_{normalL, normalC} P_{C} - S F_{normalL, normalLD} (x_{normalLD} - P_{LD, sh}), \end{matrix}

where P LD,sh is the vector of the amount of load cut off at all buse...…”

Section: Using Truncated Gaussian Mixture Model Based Cross Entrophy ...mentioning

confidence: 99%

“…In fact, the second problem can be well solved by the secant method, which is seen in many relevant works [6,14]. Hence, the efficiency of all CC evaluation methods mainly relies on the fast and accurate reliability assessment of the power system.…”

Section: Introductionmentioning

confidence: 99%

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Capacity value evaluation of wind farms considering the correlation between wind power output and load

Cai

2021

IET Generation Trans & Dist

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A method of capacity value evaluation for wind farms considering the correlation between wind power and load is presented. The paper starts with defining the metric of capacity value called capacity credit, and its basic evaluation process. Then the core part of capacity credit evaluation, which is the reliability assessment of power systems, is focused on. In this core part, two limitations of the frequently used cross entropy based importance sampling method are analysed. To solve the problems, an improved method is proposed by using truncated Gaussian mixture model as the proposal distribution of the cross entropy based importance sampling methods. This improved method is adopted to speed up the reliability assessment of composite power systems in the capacity credit evaluation. Finally, several numerical tests are designed and performed on the IEEE-RTS 79 and IEEE-RTS 96 test systems. The results show that the improved method is faster than traditional cross entropy based importance sampling methods when assessing the reliability of power system. Besides, the efficiency of the improved method is almost impervious to the correlation of load and wind power output, which ensures its applicability in different scenarios. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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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%

Section: Limitations Of Traditional Cross Entropy Based Importance Sa...mentioning

confidence: 99%

“…Firstly, the expressions of the two OPF models are given below. The minimum load shedding model is [14]

\begin{matrix} left \min_{P_{normalC}, P_{LD, sh}, P_{W}} \sum_{b = 1}^{n_{LD}} P_{normalLD, normalsh, b} \\ left s . normalt . \sum_{i = 1}^{n_{C}} P_{C, i} + \sum_{w = 1}^{n_{W}} P_{W, w} = \sum_{b = 1}^{n_{LD}} false(x_{normalLD, b} - P_{LD, sh, b} false) \\ left x_{C} \circ P_{normalC, normalmin} \leq P_{C} \leq x_{C} \circ P_{normalC, normalmax} \\ left 0 \leq P_{W} \leq x_{W} \\ left 0 \leq P_{normalLD, normalsh} \leq x_{LD} \\ left x_{L} \circ P_{normalL, normalmin} \leq P_{L} \leq x_{L} \circ P_{normalL, normalmax} \\ left P_{L} = S F_{normalL, W} P_{W} + S F_{normalL, normalC} P_{C} - S F_{normalL, normalLD} (x_{normalLD} - P_{LD, sh}), \end{matrix}

where P LD,sh is the vector of the amount of load cut off at all buse...…”

Section: Using Truncated Gaussian Mixture Model Based Cross Entrophy ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Capacity value evaluation of wind farms considering the correlation between wind power output and load

Cai

2021

IET Generation Trans & Dist

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Capacity credit evaluation of wind energy using a robust secant method incorporating improved importance sampling

Cai

2021

Sustainable Energy Technologies and Assessments

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Reliability assessment of renewable energy integrated power systems with an extendable Latin hypercube importance sampling method

Cai

Hao

et al. 2022

Sustainable Energy Technologies and Assessments

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Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling

Cited by 5 publications

References 44 publications

Capacity value evaluation of wind farms considering the correlation between wind power output and load

Capacity value evaluation of wind farms considering the correlation between wind power output and load

Capacity credit evaluation of wind energy using a robust secant method incorporating improved importance sampling

Reliability assessment of renewable energy integrated power systems with an extendable Latin hypercube importance sampling method

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