## 4 Model Validation
### Analysis of the Wind Generation Model  
The next study was conducted to analyze the impact of increasing wind power penetration on the CSR indices by varying the number of wind power states for the reliability assessment. Previous studies have shown that the impact of wind power generation on the reliability indices increases for higher penetration levels [2]. In order to analyze the impact of increasing wind penetration, the study considers two different cases of 50 MW and 150 MW wind power, integrated at Bus 3, which are respectively 18% and 43% of the total generation in the system. The peak load model was selected for this study since it is conducted to study the impact of varying the number of wind power states for different wind penetration levels. Table 4 shows the results of the CSR indices obtained when the number of states is varied in the multi-state wind models. The state-of-the-art computation method implemented in commercial tools like the PSS/E consider wind generation as a two-state model. The results obtained from this model are shown in the first row of Table 4 for comparison with the results obtained from other models. It can be concluded from an analysis of the percentage differences of the indices that the number of wind power states \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Load States** & **No. of Scenarios** & **EENS (MWh/yr)** & **EDLC (hr/yr)** \\ \hline
1 & 1 & 1199.0 & 76.0 \\ \hline
2 & 32 & 137.2 & 10.9 \\ \hline
3 & 243 & 135.4 & 10.9 \\ \hline
4 & 1024 & 134.1 & 10.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: HL II results for multi-state loads. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Model Using** & **EDLC (hr/yr)** & **Error (\%)** \\ \hline Three-states & 1.04 & 4.5 \\ \hline Hourly annual dataset & 1.09 & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: HL I results for selected three state load model. should be increased as the level of wind penetration increases, in order to achieve the same level of accuracy. However, it should be noted that an increasing number of states escalates the number of evaluation scenarios. Therefore, it is always a trade-off between the accuracy in representing the wind power variability and computational efficiency. Another study was conducted to determine the CSR indices of the RBTS, implementing the developed three-state load model while varying the number of wind power states for two different wind penetration levels. In this study, wind power generation is not modeled as a negative load but is considered as a multi-state generating unit. The number of evaluation scenarios, NoS corresponding the to the different number of wind generation states are also calculated and shown in Table 5. As shown in Table 5, increasing the number of states enhances accuracy but significantly increases the number of scenarios. For instance, by increasing the number of wind states from four to five for 150 MW of wind penetration level, there will be an improvement of 4% accuracy in EDLC and 2% in EENS. However, the number of scenarios will also increase from 972 to 1215, requiring the increased number of power flows to a multiple of added number of scenarios. It should be noted that the negative load modeling approach reduces the effective net wind power penetration by converting a significant portion of wind generation into the load. For a wind penetration level of 43% considered in this paper, the negative load modeling approach reduced the effective net wind penetration to 35%. This signifies that the multi-state model to represent 43% of wind power after the negative load modeling technique will enhance the accuracy in its representation.