## 5 Simulation Results
### Case 6: Scheduling of the _EV_ Charging
#### 5.7.3 Evse-4  
This charging strategy considers an adaptive charger and EV gets charged rigorously according to the continuous values of power given by the IHEMS. Intuitively, the algorithm tries to follow the RES power availability trend to charge the EV as shown in Figure 11. The RES power is used at its maximum level, and the utility loading decreases further to 0.31 kW at 05:00 as shown in Figure 12. This is a decrease of 90% compared to EVSE-1. The reason for this large reduction in utility loading is the adaptive nature of EVSE-4 that resulted in the EV charging time expansion to 14 h in search for the surplus power from the RES. The cost of 24-hours operation of the SH decreases to $3.24 which is 9.5% less than the cost for EVSE-1. Figure 13 presents the electrical powers of each device installed in the SH, and Figure 14 shows the thermal powers from the FC and boiler for this case.  
The cost comparison between EVSE-1 and EVSE-2 shows a cost reduction of 8% although EVSEs for the two cases have the same constant charging powers. This difference is due to considering the EV as a schedulable load. The impact of different chargers on cost is minimal as the difference between EVSE-4 and EVSE-2 is only 1.5%. However, in comparison to EVSE-1, the peak load on the utility decreases by 17.6%, 62% and 90% by using EVSE-2, EVSE-3, and EVSE-4, respectively. Therefore, the scheduling of the EV is beneficial for the power system operator as it reduces the urgent need for generation capacity enhancement and expansion of transmission and distribution networks. This is the case in many places around the world. Toronto, Canada [55] and Perth, Western Australia [56], are the examples of two places where this is a big concern and researchers advised using the controlled charging schemes for reducing the loading impacts of the EVs.  
A summary of the costs of the boiler, the FC and the utility is presented in Table 5 for each case. Savings in the net cost relative to the previous and the base case is also presented at the end of this table. A negative utility cost shows a net profit to the SH for 24 h. A negative saving value relative to the previous case represents an increase in the SH operating cost as shown by \(-\)55 % for case 5.  
Figure 15 shows the convergence of RCGA for Case 6. It is evident that the algorithm moves rapidly towards the optimal solution in the initial generations, and takes around 600 generations to converge at the optimal cost.  
Table 6 provides the numerical values of the power demands and generations, and the costs for the 24-hours operation of the SH. The costs for the FC, boiler, and utility make a major part of the total cost of the SH. The total cost for the operation and maintenance of the BESS is very small (i.e., $0.0062 per day), and is not shown in the cost table. This table can serve as a look up table for the comparison purpose by the future researchers if they work on similar simulation conditions.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Devices** & **Base Case** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** \\ \hline Boiler Cost (\$/day) & 2.19 & 2.19 & 1.90 & 2.04 & 2.02 & 1.92 & 1.79 \\ FC Cost (\$/day) & 0 & 0 & 0.98 & 0.54 & 0.60 & 0.93 & 1.41 \\ Utility Cost (\$/day) & 4.65 & 0.44 & \(-\)0.49 & \(-\)0.24 & \(-\)0.32 & 0.72 & 0.04 \\ \hline
**Net Cost (\$/day)** & 6.84 & 2.63 & 2.4 & 2.34 & 2.30 & 3.56 & 3.24 \\
**Saving relative to the previous case (\%)** & & 62 & 9 & 3 & 2 & \(-\)55 & 9 \\
**Saving relative to Base Case (\%)** & & 61.55 & 64.91 & 65.79 & 66.37 & 47.95 & 52.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of costs and savings for each case.  
Figure 15: Characteristics of RCGA convergence