## 3 Methodological Approach
### Wind Data--Mesoscale Modeling  
Simultaneous information on wind statistics over every potential area for wind farm development is required for this analysis. Even if a large number of wind measurements are available, it is practically difficult to represent simultaneous data series and cover every potential area of interest. Installation of a mast network for this purpose could lead to rather prohibitive technical and economic restrictions. Additionally, existing wind monitoring networks are relatively large and can provide large spatial coverage but not necessarily high resolution [37]. On the other hand, use of wind potential maps is not a solution since they only provide an estimation of the spatial distribution of the mean wind speed without any information on its temporal variation. Application of a Numerical Weather Prediction (NWP) model can effectively provide the information required. In this connection, high resolution analytical wind data timeseries for typical wind year are used. These data have been produced by the systematic application of a numerical weather prediction model. Analytical presentation and description of the approach was given in [38]. In Figure 2, the relative high resolution wind atlas of Greece is presented in terms of power density and parameter c of Weibull distribution, which are the most common ways to present Aeolian maps. The wind atlas of Greece was based on a typical wind year and 12 months of weather model simulations for grid boxes 2 \(\times\) 2 km\({}^{2}\) in size. The numerical weather prediction model used is \"MM5\" which is run operationally at the The selection of 90 representative points in the Greek territory is based on the location of wind farms as it is expressed by investors' interest and it is depicted in the Regulatory Authority for Energy (RAE) geographical information system in the Greek territory (Figure 3). For future scenarios, spatial dispersion of wind farms and existing plans for interconnections of Greek islands with the power system of mainland Greece are considered. Data for the selected 90 points are presented in Table 2. The duration curve of wind power output is dependent on the spatial distribution of wind farms. Historical data of single wind turbine power production typically show extended time periods with zero or rated production. However, as the spatial dispersion is increased and more wind farms are introduced, the time periods with cumulative zero or rated production are reduced [34]. The aggregated hourly wind power output is calculated on the basis of the mesoscale wind data, and the selected points in the whole Greek territory for the current wind energy development (2019) and the scenarios of installed capacity in each point under consideration for 2030 and 2050 (Figure 4). In Figure 2, high resolution wind atlas are presented for the typical wind year [38]. The indexes of Power density (W/m\({}^{2}\)) and Parameter c of Weibull distribution (m/s) are presented. Both are widely used in Aeolian maps for representation of the wind potential [42; 43]  
Figure 3 shows the overview of wind farms applications in the Greek territory and selection of 90 representative points in the whole Greek territory. In Table 2 the details of the 90 representative points are presented (name, location, k and c of the Weibull distribution and wind power density) [38]. Figure 2: High resolution wind atlas for a typical wind year [38]: (a) power density (W/m\({}^{2}\)), (b) parameter c (m/s) of Weibull distribution. Figure 4: Location of wind farms by 2019, 2030 and 2050. Figure 3: Overview of wind farms applications in the Greek territory and selection of representative points in the whole Greek territory. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Point** & **Site** & \begin{tabular}{c} **Lat** \\ **(\(\circ\))** \\ \end{tabular}  & \begin{tabular}{c} **Long** \\ **(\(\circ\))** \\ \end{tabular}  & \begin{tabular}{c} **k (\(\star\))** \\ \end{tabular}  &