4.3.1. Economic Impact of Sensitivity Variables

The influence of the sensitivity parameters on the cost of the charging station in Sokoto is discussed in this section. The examined economic metrics of the optimal EV charging scheme are the NPC and COE. The overall outcome of the investigations shows that the economic parameters change with the variation in the value of the sensitivity variables. For instance, in Figure 9, the NPC of the charging scheme rises from USD288,592 to USD864,954 when the EV charge demand rises from 550 kWh/day to 1600 kWh/day. During this process, the cost of electricity remains unstable as it alternates around USD0.212/kWh. Nonetheless, the minimum COE value was realized at USD0.208/kWh when the EV load reached 1500 kWh/day, whereas the maximum COE value was obtained at USD0.218/kWh as the load rose further to 1600 kWh/day. It can be observed here that the charging station becomes more economically unattractive as the number of EVs increases. However, the system at some certain load demand would become economically feasible.

The effect of wind speed change on the system costs of the charging scheme in Sokoto (Figure 10) reveals that both the NPC and the COE experience a cost drop as the wind speed at the selected location increases. The COE, for example, reduces from USD0.273/kWh to USD0.138/kWh, while the NPC reduces from USD708,751 to USD360,653 as the wind speed rises from 2 m/s to 8.8 m/s. This means that the NPC and the COE decreased by about 47.1% and 47.2%. Similarly, it is clear from the influence of solar radiation change on the economic viability of the charging station depicted in Figure 11 that both the NPC and COE decrease due to a rise in the values of solar irradiation. It is clear from the results that with more renewable energy resources penetration, the charging station will become more economically competitive and will provide more cost benefits to both the developers and the users as the economic feasibility status has improved.

**Figure 9.** The effect of variation in the number of EVs or charge demand on the economic metrics NPC and COE of the PV/WT/battery charging system in Sokoto.

**Figure 10.** The effect of wind speed variation on the system costs of the Sokoto PV/WT/battery charging scheme.

**Figure 11.** Impact of change in solar radiation value on the PV/WT/battery charging station costs in Sokoto.

Furthermore, the effect of varying the battery SOCminimum on the NPC and the electricity cost of the charging station in Sokoto has illustrated in Figure 12. The increase in the value of this sensitivity variable resulted in a rise in the values of the NPC and the COE. The NPC increases from USD541,550 to USD612,854, and the COE rises from USD0.208/kWh to USD0.236/kWh as the battery minimum state of charge rises from 5% to 60%. Increasing the battery's minimum state of charge, therefore, makes the charging station more expensive, which could create difficulties during the development and installation phase as the initial capital cost and the NPC increase due to this impact.

**Figure 12.** Effect of varying the battery SOCminimum on the NPC and the COE of the optimal charging system.

Finally, the change in the maximum annual capacity shortage and EV charge demand on the total NPC and the cost of electricity of the optimum EV charging scheme is depicted in Figure 13. At a particular value of the load demand, the rise in the percent value of the capacity shortage causes a reduction in the NPC and the COE values of the charging system. It is clear from the chart interface that the NPC reduces from USD730,640 to USD442,148, while the electricity cost, on the other hand, reduces from USD0.292/kWh to USD0.185/kWh as the capacity shortage increases from 0% to 8%. This has indicated that the increase in the capacity shortage can enhance the economic feasibility of the EV charging station. However, this can also create some reliability issues for the system as the charging system might not be able to adequately meet some charge demand of some EVs.

**Figure 13.** Effect of the sensitivity parameters on the (**a**) total NPC, and (**b**) cost of electricity of the optimal charging station.
