operational AEP.

In the EIA-923 database, availability and curtailment data are not available. Therefore, in our analysis we omit steps 4 and 9 of the list, and only perform calculations on net energy data.

![image](f03){width="16cm"}

Monte Carlo Analysis
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To quantify the impact of the single uncertainty components on the long-term operational AEP estimate obtained using the methodology described in the previous section, we implement a Monte Carlo approach. In general, a Monte Carlo method involves the randomized sampling of inputs to, or calculations within, a method which, when repeated many times, results in a distribution of possible outcomes from which uncertainty can be deduced. This is usually calculated as the standard deviation or the coefficient of variation (i.e. standard deviation normalized by mean) of the resulting distribution [@iso1995guide; @dimitrov2018wind]. Monte Carlo methods have been used in different applications for uncertainty quantification within the wind energy industry, ranging from the prediction of extreme wind speed events [@ishihara2015prediction], to offshore fatigue design [@muller2018application], to economic analysis of the benefits of wind energy projects [@williams2008estimating]. Here, we apply this approach to derive a distribution of long-term operational AEP values from which its uncertainty can be calculated. Using a Monte Carlo approach provides 