*4.1. Load Data Estimation*

In this study, the electric load analyzed is implemented under hypothetical states. The load profile of small-scale charging stations for the six locations is illustrated in Figure 6. The demand profile of the EV charging schemes is forecasted due to the small number (below 10 charging points) of EVCSs presently installed in Nigeria at the moment. According to the hypothetical daily, seasonal and annual load description of the selected six locations, about 20–30 EVs can be charged in a station. In the morning till the afternoon, between 06:00 and 16:00, up to 20 EVs can be recharged at an average load of 80 kW, whereas the hybrid charging scheme can provide energy to charge about 10 EVs averaged at 40 kW in the latter hours of the day from 16:00 until 22:00. The daily capacity of each EV was assumed to be 35 kWh of battery energy; therefore, the total average and peak load demand of 30 EVs is 1050 kWh/day and 104.99 kW at a load factor of 0.42. To establish an accurate estimation of the highest demand and depict a realistic load requirement of the proposed charging system, a time-step and day-to-day random variability of 10% and 5% were used in the EV load data analysis.

**Figure 6.** EV charging station system load profile.

## *4.2. Performance Assessment of the Proposed Charging Station Schemes*

The economic and technical outcomes, including the optimum component sizes of different feasible charging station models in the six considered sites, are illustrated in Tables 13 and 14. The combination of PV and WT with battery storage is economically the best system architecture for the charging station in all six sites. It is clear from Table 13 that the PV/WT/battery charging station had the least energy cost in all the simulated sites. The COE and NPC are also very competitive, even if it is difficult to install WT 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 design architecture is not economically viable, as indicated in the case of the WT/battery changing station, which has the maximum NPC and COE values of USD3,318,763 and USD1.28/kWh in the 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 provide the best economic metrics with the lowest NPC, electricity cost, and initial 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 wind energy-based charging stations in all the case study sites had the highest operating and maintenance costs because of the large number of WTs needed to fulfill the EV charging requirement. Most of the WTs need maintenance once every two years at the minimum. Therefore, the maintenance of the key parts of the WTs by carrying out tasks such as turbine inspecting, lubricating, repairing, and cleaning also contributed to high maintenance costs. The optimal charging station (PV/WT/battery in Sokoto) model had the lowest PV Levelized cost (USD0.118/kWh) with a competitive WT Levelized cost (USD0.0594/kWh). The battery wear cost is constant at USD0.1/kWh throughout the simulated year in all the sites considered.

Furthermore, according to Table 14, the highest and lowest values of the maximum penetration of renewables were reported in Minna and Sokoto. Therefore, the maximum total annual electricity is produced at 2,204,533 kWh by the WT/battery-based charging station in Minna, whereas the minimum is generated at 495,306 kWh in Sokoto by the PV/battery charging station at a capacity shortage of only about 2%. 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%, which is the lowest when compared with the corresponding values of the other charging stations. Moreover, the optimal charging station schemes in all six locations were able to sufficiently meet the EV demand with a maximum uptime as the percentages of the unfulfilled electric load were below 2% with a capacity shortage of only approximately 2%.





The monthly electric generation by the PV/WT/battery-based charging station in the six case study sites is illustrated in Figure 7. Generally, the solar PV panel generated most of the electricity needed to meet the EV charging requirement in Ikeja, Port-Harcourt, and Minna as compared to the WT production. However, in Ikeja and Port-Harcourt, the WT electric production was only competitive between June and September. The overall energy production (from both PV panel and WT) in Sokoto and Maiduguri were low from April until October. The total electric production started to increase in November and maintained a continuous maximum value until March. The gross monthly electricity generated in Enugu maintained a constant value for the whole of the simulated year, with the highest production reported in July and August. Furthermore, the annual electricity production of the optimal charging station schemes (PV/WT/battery) in the case study sites is illustrated in Figure 8, where the highest excess electricity and gross electric energy is produced in Sokoto due to the enormous presence of RE resources. The surplus electricity 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 buy/sell electricity approach.

**Figure 7.** Monthly electricity generated by the PV/WT/battery-based charging station in the six selected sites.

**Figure 8.** Yearly electric generation of the optimal charging station schemes (PV/WT/battery) in the considered locations.

In addition, Figure 8 reveals that the maximum PV production (506,181 kWh/year) is encountered in Minna, while the Maiduguri site reported the minimum PV generation at 319,137 kWh/year. The Maiduguri site recorded the highest annual electricity production from WT at 348,860 kWh, whereas a small yearly minimum value of about 73,484 kWh from the WT was reported in the Minna site. The environmental benefit of the proposed EV charging stations is that there is no carbon footprint and there is zero greenhouse gas while other harmful emissions are mitigated. This kind of system can be used to facilitate the global adoption of electric vehicles, which are often used to support economic decarbonization. Moreover, the provision of EV charge demand via the utilization of freely and readily available renewable energy resources will help to effectively promote the use of EVs as a mechanism to bring about a green solution in the transportation sector.

The optimal EVCS system designed in this study is further compared with the results of the existing EVCS designs for diverse application places using various simulation tools and approaches in the literature. The different combinations of energy resources and storage equipment and the economic and environmental results are presented in Table 15. Observation of the outcomes of the various studies revealed that the net present cost ranges approximately between USD21,000 and USD3,580,000, while the energy cost varies between about USD0.06/kWh and USD0.90/kWh. For comparison, the NPC and COE of the optimal EVCS system obtained in this study are USD547,717 and USD0.211/kWh, respectively. This provides evidence that the proposed standalone EVCS system presented herein is acceptable and possesses competitive economic metrics when compared with the previously published EVCS systems shown in Table 15. As regards the environmental benefits of the proposed standalone EV charging station, the majority of the existing works presented in Table 15 reported some non-negligible figures for greenhouse gas emissions. This highlights the drawbacks of some of the previously designed EVCS systems in terms of environmental preservation from carbon emissions. This study is claimed to be environmentally friendly indeed, as it presents no greenhouse gas emissions (i.e., no carbon footprint) whatsoever. This could facilitate the decarburization of the economy via the adoption of electric vehicles by providing fully renewable energy charging points for EVs in different parts of the country.


#### *4.3. Sensitivity Evaluation*

The sensitivity assessment was conducted in this analysis to examine the effect of some important variables on the technical and economic performance of the PV/WT/battery charging system in Sokoto. The sensitivity analysis was investigated and discussed via the variation of key system variables. Sensitivity evaluation is capable of identifying the most important variables of an investment due to the possibility of knowing in advance the effect of input parameters with uncertainty on the system cost variables and can be utilized in different contexts as well as in the assessment of investment projects [61]. The wind speed, solar radiation, battery energy storage minimum state of charge (BES SOCmin), maximum yearly capacity shortage, and EV charge demand varied at different minimum and maximum levels with respect to the base value, as sensitivity variables are shown in Table 16. The techno-economic impact of the sensitivity variables on the PV/WT/batterybased charging scheme in the Sokoto site is further elucidated below.

**Table 16.** Ranges of the sensitivity analysis parameters considered for the optimal charging station.

