4.3.2. Technical Impact of Sensitivity Variables

The influence of sensitivity variables on the technical performance behavior of the optimum EV charging station is investigated via the excess electricity and unmet electric load. The overall results show that the excess energy and the unmet load are sensitive to the change in wind speed, solar radiation, battery minimum state of charge, capacity shortage, and EV charge demand. The effect of variation in the EV charge demand on the excess energy and unmet load of the optimal charging station in Sokoto (Figure 14) reveals that the annual unmet load rises from 2823 kWh to 7623 kWh when the daily charging station load increases from 550 kWh to 1600 kWh. The excess electricity, on the other hand, remains constant at certain numbers of EVs before varying around 250,000 kWh/year. Its minimum value (125,123 kWh/year) was obtained at 900 kWh daily load, while its maximum value of 480,473 kWh/year was achieved when the daily EV load peaked at 1600 kWh. We can deduct from this that the greater the number of electric vehicles, the less reliable the charging system becomes.

**Figure 14.** The effect of charge demand or EV number variation on the technical performance of the optimal charging system.

Figure 15 illustrates the effect of wind speed change on the technological performance of the optimal charging scheme. The unmet load decreases from 5588 kWh/year to 4612 kWh/year when the average wind speed rises from 2 m/s to 8.8 m/s. In the beginning, the excess energy maintains a constant minimum value before a slight fluctuation occurs around 200,000 kWh/year. The wind speed range of 2–4 m/s gives the lowest annual excess energy of 68,133 kWh, while the highest annual excess electricity of 483,326 kWh is obtained at 8.8 m/s. The excess energy and the unmet load experience fluctuation due to the impact of the solar radiation variation, as shown in Figure 16. The unmet load fluctuates around 5200 kWh/year, while the excess energy varies around 260,000 kWh/year. The annual value of the excess energy and unmet load decreases from 371,453 kWh to 132,401 kWh and from 5191 kWh to 4848 kWh when the solar irradiation rises from 2.7 to 10.2 kWh/m2/day. This indicates an improvement in the EV charging station utility as the system becomes able to meet more EV demand.

Figure 17 illustrates the impact of changing the battery SOCmin value on the technological performance of the optimum charging system. The figure shows that the yearly excess energy increases from 147,256 to 317,796 kWh, whereas the annual unmet load reduces from 5140 kWh to 4884 kWh when the battery SOCmin increases from 5% to 60%. Finally, the chart interface showing the variation in the EV load and the capacity shortage (Figure 18) reveals that at a certain EV load and when the capacity shortage rises from 0 to 8%, the annual unmet load rises from 286 kWh to 18,151 kWh, while the excess electricity in this condition increases from 59,135 kWh/year to 210,334 kWh/year. It can be deduced from the outcomes that increasing the capacity shortage would lower the utility of the optimal charging scheme.

**Figure 15.** The effect of wind speed variation on the technical performance of the Sokoto PV/WT/battery EV charging model.

**Figure 16.** The impact of changing the solar radiation value on the excess energy and unfulfilled electric load of the optimal EV charging scheme.

**Figure 17.** The effect of varying the battery SOCminimum percent value on the technical performance of the optimal charging system.

**Figure 18.** Impact of the sensitivity parameters on (**a**) the excess energy, and (**b**) the unmet electric load of the optimal charging station.

#### **5. Conclusions**

This paper has investigated the feasibility of EV charging stations based on RE sources in Nigeria using the HOMER optimization software by considering six different locations with diverse geographical characteristics and climatic conditions. The hybrid charging station system is configured by solar and wind resources with storage devices to charge about 20–30 EVs with a daily capacity of 35 kWh each and applied in different locations in Nigeria, namely, Sokoto, Minna, Port-Harcourt, Enugu, Maiduguri, and Ikeja. The annual average solar radiations and wind speeds used to investigate the optimum hybrid system are 6.24, 4.74, 4.93, 4.13, 5.90, and 5.49 kWh/m2/day and 5.44, 3.81, 4.09, 3.15, 5.50 and 3.97 m/s for Sokoto, Ikeja, Enugu, Port-Harcourt, Maiduguri and Minna, respectively. The feasibility of the hybrid charging station system is assessed by using appropriate technical performance indicators, namely, unmet electric load, capacity shortage, excess electricity, monthly electric generation, individual system components electric production, battery energy out, and maximum renewable penetration, as well as pertinent economic performance indicators, namely, NPC, COE, operating cost, initial capital cost, the battery wear cost and Levelized cost of system components.

The optimization results showed that the combination of PV and WT with battery storage is economically the best system architecture for a charging station in all six sites. The PV/WT charging scheme integrated with battery storage had the least energy cost of all the simulated sites. The COE and the NPC are also very competitive even when it is difficult to install WTs in the considered locations, as seen with the PV/battery charging station scenario. However, the unavailability of a PV system in the PV/WT/battery system architecture is not economically feasible, as indicated in the case of the WT/battery charging station, which has the maximum NPC and COE values of USD3,318,763 and 1.28 USD/kWh in Port-Harcourt site. In general, the PV/WT/battery charging station (2 qty. of WT, 174 kW of PV panels, 380 qty. of batteries storage, and a converter of 109 kW) in Sokoto provides the best economic metrics with the lowest NPC, energy cost, and initial capital costs of USD547,717, USD0.211/kWh, and USD449,134, respectively. Moreover, the charging station presented competitive annual operating and maintenance costs of USD14,344 and USD67,195. The PV/WT/battery CS at the Sokoto site was able to reliably satisfy most of the EV charge demand as it presented a small percentage of the unmet load of 1.38% (In fact, the lowest when compared with corresponding values for the other charging stations). Moreover, the optimal charging station schemes in all six locations were able to sufficiently meet the EV demand with maximum uptime as the percentages of the unfulfilled electric load were below 2% with a capacity shortage of only approximately 2%. The surplus energy produced can be sold directly to the utility grid via a CS-to-grid connection. Moreover, since the proposed charging stations are located in cities/urban areas, this will facilitate any future connection of the charging stations to the grid network to enable the buying/selling electricity approach. The sensitivity analysis conducted to check the robustness of the optimal charging scheme reveals that the technical and economic performance indicators of the optimum charging station are sensitive to the changes in the sensitivity variables.

Furthermore, the outcomes ensure that the hybrid system of RE sources and EVs can minimize carbon and other pollutant emissions. As for further research, the feasibility of the hybrid charging station system can be investigated by considering distributed generation and load uncertainties. The major limitation of this study is the high initial investment cost needed to install the proposed charging system in the suggested locations. This is often the major obstacle that hinders the widespread use of a standalone renewable energybased system in most parts of the world, particularly those parts with limited finances, such as most countries in Africa. However, with the recent technological breakthrough in renewable energy technologies as well as the numerous initiated governmental economic programs, this obstacle could be surmounted in the near future.

**Author Contributions:** J.O.O.: Conceptualization, Methodology, Validation, Formal analysis, Software, Investigation, Resources, Data curation, Writing—original draft preparation and editing. A.M.: Investigation, Writing—review, and editing, Validation, Resources. A.A.I.: Investigation, Writing review, and editing, Validation, Resources. A.M.R.: Writing—review and editing, Investigation, Validation, Data curation, Visualization. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
