# Projected Wind Energy Maximum Potential in Lithuania
## 3 Results  
### Wind Speed  
Three regions were chosen for wind speed analysis in the territory of Lithuania: coastal (Figure 2b), central (Figure 2c) and eastern (Figure 2d). Klaipeda HMS was selected for the observed wind speed of the coastal region, the central region was analyzed using the data of the Kaunas HMS and Utena HMS was used for the wind description in the eastern region. Figure 2a shows the observed annual mean speed in these three areas during 1980-2020 and the projected annual mean speed of wind, according to the IPSL model, during 2020-2100. From these data, we can see that the average speed in the coastal region is the strongest, about 1.5 m/s higher than in the central region, while the wind in the eastern area is about 1.5 m/s lower. It can also be observed that both observational data and projected data indicate that the wind speed is slightly decreasing. In order to assess the rate at which the wind speed decreased, Figure 2b-d show the linear trends of wind speed variation. The coefficients of the linear equations show that the observed wind speed decreases more rapidly than the wind speed projected by all the models according to the selected scenario SSP2-4.5. However, the data indicate that wind speed may slightly increase in later decades of the 21 st century. As mentioned in the previous section, the selected wind turbine has a cut-in wind speed of 3 m/s and a cut-off wind speed of 25 m/s. An analysis was carried out on how the number of windless days per decade and per year affects energy production (Table 2 and Figure 3). For the HAD model, the number of windless days per year in different parts of Lithuania varies from 31 (in Laukuva) to 215 (in Utena); for the IPSL model, from 37 (in Laukuva) to 210 (in Utena); for the MPI scenario, from 15 (in Klaipeda) to 100 (in Utena). For the MPI model, it was found that there are about two times fewer days with wind speeds that are not suitable (comparing the maximum number of days) for electricity generation as for the other models. This observation explains why the map in Figure 4 shows the highest energy production compared to the other models. Figure 2: Analysis of wind speed over Lithuanian territory. (**a**): the change in mean annual speed according to the observed and bias-corrected projection wind speed data (IPSL model, SSP2-4.5 scenario). (**b**): wind speed change in the coastal region (“-o”) according to the data of Klaipeda HMS and three projection models. (**c**): wind speed change in the central region (”-c”) according to the data of Kaunas HMS and three projection models. (**d**): wind speed change in the central region (”-e”) according to the data of Utena HMS and the same three projection models. An analysis of the days when the wind is not predicted to be suitable for renewable wind energy production showed no trend over the next four decades. However, these trends emerged from an analysis of the current century, as shown in Figure 3, wherein the number of days when the wind was unsuitable (<3 m/s or >25 m/s) for the operation of wind farms for the SSP2-4.5 scenario was assessed. By the end of the century, the maximum number of ineligible days is predicted to decrease from an average of 91 days to 87 days. However, the MPI model is the most optimistic in windy areas (e.g., Klaipeda), as the number of unsuitable days will not exceed 30 days per year. According to the HAD model data analysis, the maximum number of such days will vary between 31 and 71. The lowest among all models is MPI, where the minimum number will remain stable throughout the century at around 18 days. The analysis of both HAD maximum and IPSL maximum unsuitable days showed very similar results. Windless days could account for 131 to 215 days per year.