## 7 Numerical Results
### Simulation Results

Table 8: Output values for the day-ahead (DA) operation. or discharged by a level 2 charger), then the amount of energy level exchanged with the MG will be decreased because charging or discharging duration will be increased, so the fewer of EVs will be used in a 24 h operation period. On the other hand, if the uncertainties of SoC and battery capacity are not considered, the load curve will change more. In this case, the energy interaction of the station with the MG will change completely because according to (3) and (4), these parameters have direct effect on the station load curve. The station's load curve in the case of ignoring the EVs price sensitivity has a different shape. In this case, the EVs do not discharge their stored energy despite the price increase. Table 8 is presented in order to understand how these curves effect on the optimal DA solutions. As previously mentioned, the total cost of the operation is $717. The total energy produced by the micro-turbines and the upstream grid is 1040 kWh and 5131.8 kWh in a period of 24 h, respectively. These values are calculated by applying the proposed method for the charging station load. This state is considered as the base case and the results of sensitivity analysis are compared with this case. Table 10 shows the results of sensitivity analysis on the EVs uncertainty parameters. According to this table, ignoring any of these parameters in the calculation of the EVs charging station load leads to higher operating costs. For example, if the uncertainty of the type of chargers is not taken into account, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multirow{2}{*}{ \begin{tabular}{c} **Proposed** \\ **Model** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Remove** \\ **Uncertainty** \\ **of Type of** \\ **EVs** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Remove** \\ **Uncertainty** \\ **of Type of** \\ **Chargers** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Remove** \\ **Uncertainty** \\ **of SoC** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Remove** \\ **Uncertainty** \\ **of Battery** \\ **Capacity** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Remove** \\ **Price** \\ **Sensitivity** \\ \end{tabular}  } \\ \hline
1 & 48.45 & 48.402 & 26.349 & 150.672 & 150.504 & 49.76 \\ \hline
2 & 50.085 & 48.264 & 44.76 & 138.042 & 141.735 & 50.01 \\ \hline
3 & 52.545 & 54.744 & 53.427 & 130.332 & 128.253 & 56.16 \\ \hline
4 & 50.52 & 49.923 & 46.494 & 106.83 & 134.043 & 51.95 \\ \hline
5 & 48.945 & 47.757 & 41.022 & 55.686 & 116.157 & 54.15 \\ \hline
6 & 38.61 & 39.588 & 40.548 & 37.824 & 69.042 & 87.48 \\ \hline
7 & 5.46 & 4.629 & 50.442 & 18.738 & 30.756 & 118.37 \\ \hline
8 & \(-\)11.31 & \(-\)9.966 & 51.999 & 11.388 & 31.743 & 128.29 \\ \hline
9 & \(-\)31.905 & \(-\)28.707 & 32.109 & 3.756 & 17.214 & 137.31 \\ \hline
10 & \(-\)48.99 & \(-\)48.303 & 5.625 & 0.33 & 16.041 & 141.63 \\ \hline
11 & \(-\total amount of micro-turbine generation will decrease by 2.7% (1012 kWh) and the purchasing power will increase by 0.1% (5182 kWh). Furthermore, the total operating cost will increase by 0.55% (SS721). The type of EVs uncertainty has the least impact on the operation cost. It increases cost only 0.08%. In contrast, ignoring the uncertainty of the EVs price sensitivity has the largest impact on the optimal solutions. It increases the operating cost by 21.2%. This result shows the importance of uncertainty in EVs price sensitivity. Uncertainties in SoC and uncertainty in battery capacity have also increased the cost by 4.8% and 7.5%, respectively, which uncertainty of EVs battery capacity has a fairly large impact on cost and optimal solutions. Overall, uncertainties in SoC, battery capacity and price sensitivity have a higher impact on the load profile and operating cost compared to uncertainties in the type of EVs and type of chargers.