| Abstract: |
| The impact of wind power forecast errors (WPFEs) on power system reliability can be quantified by a sensitivity
model, which helps to determine the importance of different wind farms. However, the unknown distribution and
correlation of WPFEs make it difficult to calculate the reliability sensitivity. The existing univariate non-standard
third-order polynomial normal transformation (NSTPNT) expresses the reliability sensitivity of WPFEs by a normal
random variable with explicit distribution, and is not suitable for multiple wind farms with correlated forecast errors.
In this paper, the univariate NSTPNT method is extended to the multivariate by deriving the analytical expression of
the correlation coefficients before and after the transformation, to establish the transformation between the WPFEs
and a normal random vector (RV) with the specific correlation. A reliability sensitivity model to the WPFEs expressed
to the normal RV is then proposed. The numerical results validate the accuracy of the proposed multivariate NSTP
NT and the sensitivity model. The maximum relative error for using the sensitivity to approximate the change of
reliability with distribution parameters of the WPFEs is less than 2.42%. The necessity of considering the correlation
of WPFEs is analyzed. The maximum relative error of the sensitivity reaches 83% when the correlation is ignored. |
| Key words: Multivariate NSTPNT, Wind farms, WPFEs, Correlation, Reliability, Sensitivity |
| DOI：10.1186/s41601-021-00192-0 |
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| Fund: |
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