## 5 Case Studies Analysis
### Multi Scenario Sample Data Generation  
In the model, the fluctuation degree in the wind turbine and photovoltaic power stations is reflected in the value of robustness indexes \(\Gamma_{\rm wt}\) and \(\Gamma_{\rm pv}\). To accurately assess the impact of the volatility of new energy outputs on grid planning, the initial group sample data are first generated when \(\Gamma_{\rm wt}\) = \(\Gamma_{\rm pv}\) = 0.03. Subsequently, while the robustness index of the wind turbine \(\Gamma_{\rm wt}\) = 0.03 remains unchanged, the robustness index of the photovoltaic power station is gradually increased from 0.03 to 0.08, and a series of operating cost data affected by photovoltaic output fluctuations is obtained. Finally, while the robustness index of the photovoltaic power station \(\Gamma_{\rm pv}\) = 0.03 remains unchanged, the robustness index of the wind turbine is gradually increased from 0.03 to 0.08, and a series of operating cost data affected by the output fluctuation of the wind turbine are obtained. The final generated sample data are shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pollutant Gasses** & **Emission Coefficient (g/kWh)** & **Goverance Cost (JPY/kg)** \\ \hline CO\({}_{2}\) & 889 & 0.210 \\ SO\({}_{2}\) & 1.8 & 1.842 \\ NO\({}_{\rm x}\) & 4.6 & 62.964 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Environmental parameters of miniature gas turbine set. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\Gamma_{\rm wt}\)} & \multirow{2}{*}{\(\Gamma_{\rm pv}\)} & **Initial** & **Equipment** & **Environmental** & **Total Cost of** & **Time** \\  & & **Investment** & **Maintenance** & **Goverance** & **Microgrid/** & **/ms** \\  & & **Cost/JPY** & **Cost/JPY** & **Costs/JPY** & **JPY** & \\ \hline \multirow{8}{*}{0.03} & 0.03 & 181,472.08 & 12,967.28 & 4648.24 & 203,980.58 & 32.91 \\  & 0.04 & 181,307.14 & 19,031.09 & 4702.38 & 20  
From Table 3, it can be seen that as Gamma wt and Gamma PV represent the deviation between predicted and actual costs, as Gamma wt and Gamma PV decrease, i.e., as the number of iterations increases, the solving time of the column and constraint generation algorithms gradually increases, and the time increases faster when the values of Gamma wt and Gamma PV are larger. Due to the excessive number of scenes, only the optimization results of \(\Gamma_{\text{wt}}=\Gamma_{\text{pv}}=0.05\) (Figure 3), \(\Gamma_{\text{wt}}=0.05\), \(\Gamma_{\text{pv}}=0.08\) (Figure 4), and \(\Gamma_{\text{wt}}=0.08\), \(\Gamma_{\text{pv}}=0.05\) (Figure 5) are presented here. From the comparison between Figures 3 and 4, it can be seen that the increase in the fluctuation of photovoltaic power station output has a significant impact on the scheduling of typical spring days from 11:00 to 15:00. The output of micro combustion units increases significantly, photovoltaic consumption decreases significantly, and there are no significant fluctuations in electricity sales. From the comparison between Figures 3 and 5, it can be seen that the increase in fluctuation of wind turbine output has a greater impact on typical spring days from 11 a.m. to 15 p.m. and from 20 to 23 p.m. The output of micro combustion units significantly increases during lunchtime, while the wind power output of the system is lower at night. Therefore, power balance is maintained by purchasing electricity.