# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
## 4 Results
### Charging Station Capacity
Similarly, the three-minute intervals compared to the one-minute averaging intervals show superior improvement in the system, producing almost 81% and 91% increases in the system's monthly coverage of EVs. Furthermore, Figure 6 illustrates the comparison results of using two different wind speed datasets (M2-2018 and M4-2014). According to the results, it can be seen that M4 data (Figure 6d,c,f) provide a greater wind turbine power output, and therefore a greater number of EVs can be charged. However, the performance is not stable throughout the months of the year. The M2 dataset shows the best performance during January, February, March, April, November, and December, with lesser performance during the other months. The M4 data lead to the same conclusion as M2, except for December, where almost 12 days of the data were missing and were excluded from the calculations. Figures 7 and 8 show examples of a direct wind energy EV charging conversion procedure over one month for both M2 (January 2018) and M4 (January 2014) data, respectively. The wind speed data were averaged using three minute intervals, as concluded from Figure 6, while Figures 7a and 8a show the heat maps of interval-based wind speed progression (m/s) rearranged in daily vectors to observe the correlation between wind speed observations and wind turbine output power. For each predefined charging time slot (\(t_{charge}=21\) min), the algorithm converts the wind speeds to turbine power outputs using an overlapped sliding window procedure (Figures 7b and 8b). It can be concluded that the regions of the wind speed heat maps that show no changes in the color (wind speed) are reflected as stable power outputs in the corresponding power heat maps. The filtered wind power time slots (\(t_{charge}=21\) min) are shown in Figures 7c and 8c. We can see that the power time slots that are constrained for EV charging are scattered throughout the day, with the M4 dataset showing a greater availability of EV charging time slots. For power time slots available at night and for better charging system efficiency, prior alerts must be sent to the customers when the power is expected to be available and when parking lots are available in the charging station. The conversion of wind power to energy can be seen in Figure 7d,e and Figure 8d,e. The M2 and M4 datasets show different patterns of daily  
Figure 6: Selected turbines and total number of EVs calculated over different averaging intervals for M2 (**a**–**c**) and M4 (**d**–**f**) data. (**a**,**d**) The monthly estimated total EVs when using one-minute wind speed averaging. (**b**,**d**) The number of EVs using two-minute averaging intervals. (**c**,**f**) The three-minute averaging results of total EVs. energy availability, with minimal EV charging energy on some days, especially for the M2 data. It will be beneficial to add another renewable energy source, connect to the grid, or combine these scenarios to tackle this energy uncertainty. The excess power harvested from the wind can be injected into the utility grid during the windy days, which will compensate for the system energy cost provided to the customers. As shown in Figures 7f and 8f, only a few days had zero wind energy, which was harvested from an individual wind turbine, where wind energy aggregation from the multi-turbine grid could enhance the overall system efficiency. Furthermore, for certain days, the wind energy availability for EV charging could be beyond the charging station's capacity, which in turn could be sold to the utility grid and be re-used when wind energy is not available, thus reducing the reliance on battery storage systems. Figure 8: Wind energy EV charging conversion results for the M4 tower data during January 2014 for selected turbine no16. (**a**) Wind speed (average \(\pm\) standard deviation is \(8.3917\pm 6.2554\) m/s) heat map rearranged to show daily 21-min intervals. (**b**) The corresponding wind turbine power output for each 21-min interval. (**c**) The usable interval for EV charging.