# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
## 4 Results  
### Wind Speed Data Averaging  
Three case scenarios for wind speed averaging were used to typify possible variations of wind power stability by using one, two, and three minutes. For each case, during the averaging process, no time interval includes observations from two data segments with missing wind speed values. The monthly percentage of missing data was calculated for M4, while no missing data were found from M2. The missing data at M4 were not scattered over time but were found to be sustained for long periods--sometimes days--and it was not practical to perform data imputations to fill the data gaps. For this reason, the power analysis in the following subsections was performed on a daily basis. Figure 4 shows the boxplots of the entire one-year data of M2 and M4 using the three averaging scenarios. Obviously, the M2 data have a lower annual wind speed average with fewer variations compared to that of M4. The three averaging intervals also show almost identical statistics, with a mean \(\pm\) standard deviation of 3.7002 \(\pm\) 2.9095, 3.7002 \(\pm\) 2.8803, and 3.7002 \(\pm\) 2.8616 for one, two, and three minutes, respectively. Similarly, for the M4 data, the figure shows higher wind averages of 4.4040 \(\pm\) 3.5194, 4.4031 \(\pm\) 3.4893, and 4.4023 \(\pm\) 3.4702, respectively, for the same three averaging intervals. The stationary analysis of wind averaging was also investigated using the obtained wind angular directions and Weibull distribution parameters, as shown in Figure 5. The wind directions of M2, as in Figure 5a-c, show new patterns with different averaging intervals, which could be explained as extreme variations in the wind directions within small time intervals of one to three minutes. On the \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Description or Value** \\ \hline Battery pack capacity (\(B_{c}\)) & 50 kW \\ Driving range & Between 225–465 km \\ Fast charging range & From 10–80\% \\ Charging point & Supercharger v3 (250 kW DC) \\ Charging point max power & 170 kW \\ Charging point avg power (\(P_{charge}\)) & 100 kW \\ DC charging time (\(t_{charge}\)) & 21 min \\ Real energy consumption & 10.2–21.1 kWh/100 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relevant Tesla Model 3 Standard Range Plus specifications. contrary, the wind directions of M4, as illustrated in Figure 5g-i, are stable over the three averaging intervals. The wind histograms and Weibull distribution parameters show slight changes when varying the averaging intervals, as explained in the caption of Figure 5. These results do not provide a shred of evidence on which averaging interval is more suitable for wind energy harvesting when considering the stability of the power output. Since wind time series data are highly non-stationary, the dynamic analysis of statistical parameters over shorter periods may reveal more underlying behaviors of wind changes. This will be provided in the following subsections, where the moving window power analysis is presented over the three averaging scenarios. Figure 4: Boxplot of the M2 and M4 tower wind speed (without outliers) grouped via averaging. Figure 5: Windrose plots and wind speed distributions for M2 (**a–f**) and M4 (**g–l**) when averaged over one (**a,d,j,i**), two (**b,e,h,k**), and three (**c,f,i,l**) minutes. (**d**) Wind speed distribution for M2 averaged over one minute and fitted on Weibull distribution of scale = 4.0849 and shape = 1.3948. (**e**) Two minutes averaged wind speed distribution of the M2 tower data with Weibull scale = 4.0930 and shape = 1.410. (**f**) The M2 tower wind speed data averaged over three minutes interval with Weibull scale = 4.0986 and shape = 1.421. (**j**) The M4 tower wind speed of one minute average with Weibull distribution scale = 4.8212 and shape = 1.3444. (**k**) The M4 wind speed data when averaging over two minutes with Weibull scale = 4.8299 and shape = 1.3576.