# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
## 4 Results
### Charging Station Capacity  
The wind-powered EV charging station is strongly dependent on the availability of constant power supply from wind turbines, which limits the station in terms of providing smart charging compared with an immediate charging scenario. Grid power compensation could partially solve this issue and provide enough power to charge the EVs when turbines fail to satisfy the EVs' power demands. In this study, the direct wind-to-EV charging using DC fast-charging technology was preferable due to the significant reduction in power conversion stages. Moreover, the study aimed to reduce the reliance on grid and storage systems. Excess wind power that is not used for fast charging can be injected into the utility grid. The (re-)scheduling of the charging events is triggered whenever the charging system predicts stable wind energy that falls within user-defined EV charging specifications (energy volume and charging duration). All time intervals (the holding time of EV for charging) were statistically analyzed, and the intervals that did not show stable or enough power within the EV charging standards were filtered. Finally, the cumulative daily wind energy and the estimated number of EVs that the station could handle were obtained from the remaining charging intervals. Figure 6 summarizes the average monthly number of EVs calculated from each individual wind turbine power output over one-, two-, and three-minute wind speed averaging for the M2 and M4 datasets. For all scenarios, turbine no124 shows the best performance from the selected nine turbines, followed by turbine no44. Even though the wind speed data variations and power curve sampling are the same for all turbines, instantaneously mapping the wind speed to wind power using the power curve gives more stability in the output wind power, which strongly depends on the shape of the power curve itself. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID \({}^{*}\)**} & **Height** & **Cut-In** & **Rated** & **Cut-Out** & **Power** & **M4 Tower Data (\%)** & & **M2 Tower Data (\%)** \\  & **(m/s)** & **(m/s)** & **(m/s)** & **(kW)** & **1 Min** & **2 Min** & **3 Min** & **1 Min** & **2 Min** & **3 Min** \\ \hline no16 & 134 & 3 & 10 & 20 & 3300 & 9.41 & 14.34 & 18.35 & 5.22 & 8.28 & 9.78 \\ no17 & 114 & 3 & 10 & 20 & 3000 & 9.22 & 14.44 & 17.74 & 5.22 & 7.74 & 9.64 \\ no44 & 119 & 3 & 10 & 22 & 3000 & 12.61 & 17.69 & 21.26 & 6.82 & 9.12 & 10.49 \\ no67 & 129 & 3 & 10 & 23 & 3150 & 13.41 & 17.94 & 22.20 & 7.20 & 9.41 & 10.87 \\ no73 & 99.5 & 3 & 10 & 25 & 2300 & 14.96 & 18.73 & 22.20 & 6.92 & 8.97 & 10.21 \\ no94 & 139 & 3 & 10 & 22 & 3200 & 12.42 & 17.05 & 21.45 & 7.10 & 9.41 & 11.01 \\ no95 & 136 & 3 & 10 & 22 & 3000 & 12.33 & 17.00 & 21.31 & 7.10 & 9.46 & 10.91 \\ no124 & 137 & 3 & 10 & 25 & 3500 & 16.04 & 20.21 & 23.76 & 7.86 & 9.96 & 11.34 \\ no128 & 99 & 3 & 10 & 25 & 2200 & 14.96 & 18.73 & 22.20 & 6.86 & 8.97 & 10.21 \\ \hline \hline \multicolumn{10}{l}{\({}^{*}\)The ID column contains the turbine names generated by sequential numbering (e.g., no16 indicates turbine number} \\ \multicolumn{10}{l}{16 in the turbines’ data library).} \\ \end{tabular}
\end{table} 
Table 2: Selected wind turbines output power stability (%) using M2 and M4 wind speed data with different averaging intervals. The comparison results using different wind speed averaging intervals revealed that as the averaging interval (in minutes) increases, the stability of wind power output increases, which verifies the blades' momentum effects. Comparing the results of one-minute averaging (Figure 6a,c) with the two-minute averaging (Figure 6b,d), it is evident that there is a significant increase in power stability, which leads to a higher number of EVs that the station can serve on a monthly basis. Taking turbine no16 (e.g., January) as an example, the two-minute averaging intervals show 47% and 45% increases in total EVs for the M2 and M4 datasets, respectively.