## 2 Data and Methods
### Methods
#### 2.3.3 Assessment of Future Wind Energy Resource  
Eight indices were considered to assess the future wind energy resource in the northwest Iberian Peninsula coast, following the methodology applied by [38; 49; 50] to assess the richness of the wind resource, the variability, the environmental risk, and cost parameters. These indices are: Annual average wind speed (\(W_{\rm ann}\)), rich level occurrence (RLO), downtime (DWNT), coefficient of variation index (\(C_{v}\)), monthly variability index (\(M_{v}\)), risk of extreme wind speeds (Risk), water depth (WD), and distance to coast (DC). The richness of the wind resource parameter accounts for the potential wind energy resource available, which is independent of the converter device. The \(W_{\rm ann}\) index is divided into seven partitions following the NREL classification scheme [60]. The rich level occurrence measured by means of the RLO index is defined as the frequency of WPD higher than 200 \(\rm Wm^{-2}\). The effectiveness in the exploitation of the wind energy resource is also a key factor because wind turbines only operate in a particular range that allows the production of energy. The upper (cut-off speed) and lower (cut-in speed) thresholds of this range are usually around 4 and 25 \(\rm ms^{-1}\), respectively [49; 50]. In this sense, the DWNT index provides a measure of the total time with useful wind speed occurrence (EWSO) in which a wind turbine is producing electricity. The temporal variability of the wind energy resource is another important factor, since a stable energy supply throughout the year would optimize the energy harvest efficiency. In this context, the coefficient of variation (\(C_{v}\)) index is defined as:  
\[C_{v}=\frac{S}{\overline{x}}\, \tag{3}\]  
where \(S\) and \(\overline{x}\) are the standard deviation and the WPD mean, respectively. Additionally, the monthly variability (\(M_{v}\)) index was also considered. This index is defined as:  
\[M_{v}=\frac{P_{M1}-P_{M12}}{P_{year}}, \tag{4}\]  
where \(P_{year}\) is the annual mean of the WPD, and \(P_{M1}\) and \(P_{M12}\) represent the WPD for the most and least energetic months, respectively. The risk index was also considered, to take into account extreme wind speeds (EWS) that can highly affect the safety of ocean engineering. This index was calculated by means of the EWS over a 50-year period calculated based on the Gumbel curve method. Finally, water depth and distance to coast indices were also considered for every grid pixel. These indices represent the cost factors related to grid connection and marine engineering. For this purpose, the ETOPO bathymetry and the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) coastline databases were used to calculate WD and DC indices, respectively. The assessment of the future wind resource based on the indices described above was carried out following a Delphi classification method similar to the one described in [37; 49; 61]. First, each index was normalized to encompass all of them in a single parameter (the \(\rm C_{wind}\) index) since, as mentioned above, each index has its magnitude and units. The normalization process followed is similar to the one applied by [49; 50], in such a way that both positive and negative indices were turned into positive factors with the optimal value equal to 1 and the worst equal to 0. The partitions of the \([0,1]\) interval were calculated considering a \(1/(n-1)\) step, where n is the number of categories (10 or 5, depending on the index). Most of the indices were normalized using 10 partitions (Table 2),with the exception of the \(\rm W_{ann}\) index normalized using 7 (as mentioned before), and WD and DC which were normalized using 5 partitions (Table 3).