# Assessment of the Impacts of Climate Change on Power Systems: The Italian Case Study
## 1 Introduction
### Generation and Demand Models
#### 2.2.2 Wind Power  
Among the RES power capacities worldwide, wind power capacity was the second in quantity [7]. Therefore, it is important to understand the ways in which climate change could impact wind energy generation.  
Wind speed is the most important influencing factor of wind energy. Specifically, the expression of input wind turbine power, under the assumption of dry air at pressure of 1 atmosphere is [21]:  
\[P_{w}=\frac{1}{2}\rho(T_{a},p)\cdot A\cdot\mathrm{v_{H}}^{3}=\frac{1}{2}\frac{35 3.05}{273.15+T_{a}}\cdot A\cdot\mathrm{v_{H}}^{3} \tag{5}\]  
where:  
\(P_{w}\) is the power in the wind (W);  
\(\rho\) is the air density (kg/m\({}^{3}\)) function of temperature \(T_{a}\) and pressure \(p\);  
\(A\) is the cross-sectional area through which the wind passes (m\({}^{2}\));  
and v\({}_{\mathrm{H}}\) is the windspeed at the hub turbine height and orthogonal to A and at the hub turbine height (m/s).  
The cubic relation in (5) suggests that even little variations in mean wind speed and wind patterns can drastically change the wind energetic potential. Climate change may modify atmospheric dynamics with impacts on both wind speed and wind patterns [22]. In light of this, many studies have addressed the forecast of long term wind patterns and average wind speed, for different regions of the world.  
Finally, in addition to mean wind speed and wind patterns, other factors affect wind energy production such as air density (which in turn depends on the air temperature), but less research has been conducted on this aspect.  
Focusing on the two main papers of this work, [13; 14], the procedure followed in [13] took the wind input data, that is, wind speed at 10 m (v\({}_{10}\)), then extrapolated it to the turbine hub height (H), for example using the power law found by [PERSON] [23]:  
\[\mathrm{v_{H}}=\mathrm{v_{10}}^{\frac{1}{7}} \tag{6}\]  
Once the wind speed at hub height was obtained, this value was entered in a standard turbine power curve to obtain the electric power generated by the turbine.  
Specifically, the wind turbine characteristic showed further no linearities when the wind speed was either lower than the cut-in wind speed or when it exceeded the cut-out wind speed, where in both situations the output power was zero [21].  
The wind farm distribution existing in the year of paper was considered to evaluate future relative changes in wind power potential.  
In [14], the wind power capacity factor was calculated at each reanalysis grid box, with the extrapolation of near-surface winds to a constant hub-height of 100 m. Taking these values by means of a turbine standard curve, these speeds gave the value of power generated. The capacity factor was aggregated to country level, and then it was multiplied by the national installed capacity (scaled) to obtain the country level wind generation. Only onshore wind farms were considered. The grid resolution used was 0.5\({}^{\circ}\), being not so fine a resolution that makes it difficult to state where wind power farms will be installed between grid points, inside a country. This motivates the assumption of a homogeneous distribution of wind farms for future scenarios assessment.