Onshore wind power plants and wind resources

A wind turbine represents the smallest entity of a wind power plant which combines several wind turbines at one geographical site to a wind farm. The generation profile of a wind farm is modeled based on the electricity output of a single wind turbine which is then scaled up by the electricity market model. The electricity output of a reference onshore wind turbine is modeled in a special wind model extended from Schermeyer (2011). The size of the wind farm (= number of wind turbines) is endogenously found by the electricity market model in the expansion planning decision (model variable for installed capacity of wind farm). The generation profile already considers output reductions such as self-consumption, operational losses, weak and turbulence effects which occur by the interaction of different turbines in a wind farm. A medium-size wind turbine from Gamesa with 2 MW (G80) and a hub height of 78 m is used with its typical power curve for standard conditions (air density of 1.225 kg/m³) (Gamesa, 2009). This turbine has already been used in Moroccan, Tunisian and Egyptian wind projects before (Gamesa, 2011). The size and height of this turbine is in a middle range. Also cost and quality of this turbine is in the same range. The maximum available wind power (PWind)

can be calculated by the equation (Eq 3) according to (IEC, 2005): =1

2∗ ∗ ∗ (Eq 3)

The power of wind consequently depends on the air density (ρAir), the rotor area (AR) and the

wind speed (vWind). As one data set of measurement data for each site increase the use of

extreme values and fluctuation in the wind profiles, feed-in profiles of wind turbines are lowered by the pooling effect between several wind turbines and farms. As some wind farms with distance of several kilometers (10 to 50 km) use the same wind profile, a smoothing approach for the generation profile (wind speeds) is used. This approach is recommended to lower the effect of fluctuations on the electricity system (Norgaard and Holttinen, 2004; Klobasa et al., 2009). An average wind speed of five time steps (vmeas.opt) is calculated to

decrease these fluctuations in the time series of wind speeds at each site.

. , = ,

+ _, + _, + _, + _,

5 (Eq 4)

As the weather data are measured at a height of 10m, the used wind speeds have to be adjusted to the hub height of the wind turbine. In the model, the Hellmann altitude formula (Eq 5) is applied to obtain this calibration of the wind data according to the surface characteristics (Kaltschmitt et al., 2010). This method can easily be applied by using the Hellmann exponent which provides a value related to the structure of the ground surface (the barometric formula with roughness length is another method to adjust the measurement data). The Hellmann exponent hc is assumed to 0.34 for areas with hills or villages, to 0.25 for desert areas with flat

ground and to 0.16 for coastal areas.

= ∗ (ℎ

ℎ ) (Eq 5)

Each wind turbine has a specific power curve where the power coefficient (cp(v)) is displayed

for each wind speed. This coefficient reflects turbine layout, turbine efficiency, start-up and shut-down behavior depending on the current wind speed and air density (p) for the specific

60 CHAPTER 4. Development of the electricity market model RESlion

technical layout of a wind turbine. As hourly values for pressure and temperature are included in the weather file of each site, the power coefficient can be related to the air density calculated at each site. This coefficient has a theoretical maximum according to the Betz Limit of 0.593 (=16/27) (Carrillo et al., 2013). The power coefficient for different air densities is calculated based on the power curve of Gamesa G80. Electricity power of a wind turbine (PWT(v)) is the

result of the wind power multiplied by the power coefficient (cp(v)) (IEC, 2005):

( ) = ( ) ∗ ( ) _{(Eq 6)}

Whereas the power curve data of turbine manufacturers includes mechanical and electrical losses and the aerodynamic behavior of blades, the overall electricity output of a turbine in a wind farm (PTWF(v)) has to take into account the electricity self-consumption for turbine

tracking (lossesself), wake effects within a wind farm (losseswake) and technical availability of the

wind farm (lossesavail).

( ) = ( ) ∗ (1 − − − ) _{(Eq 7)}

The power losses by self-consumptions due to tracking the wind turbine to the wind direction are assumed to be 2% of the power generation (Kaltschmitt and Fischedick, 1995). Losses due to wake effects between wind turbines in a wind farm are generally reported to be lower than 10% (Manwell et al., 2002). Availability due to revision and maintenance of the wind turbine is reduced by 1% to 3% according to Manwell et al. (2002) and Abderrazzaq and Hahn (2006). In a case study for Ireland, Conroy et al. (2011) find an output reduction of 3 – 11% if availability is related to time (3%) or energy output (11%). In the model, average overall losses of 12% for the electricity output of a wind park compared to the modeling of a single wind turbine are considered (all main parameters are displayed in Table 8). Furthermore, the use of hourly values for the electricity generation of wind turbines underestimates the effects of wind gusts and strong declines.

Table 8: Technical parameters of reference wind power plant

Technical parameter Input definition

Turbine name Gamesa G80 Turbine capacity 2 MW Rotor diameter 80 m Hub height 78 m Cut-out speed 25 m/s Total system losses (wind farm) 12 %

Source of weather information (Meteotest, 2011) Measurement height (wind speed) 10 m

Hellmann factor 0.16 to 0.34 depending on geographical location

4.2.1.2 Resource assessment and site selection for wind farms

The selection of potential sites is based on a literature review of national wind resource assessments in each country as these assessments are very detailed for each country and

CHAPTER 4. Development of the electricity market model RESlion 61

usually integrate many local decision factors (land use, grid access, visible impact, etc.) to identify suitable sites and areas for the installation of wind power (see Table 9). In all countries, measurement data are reported with average wind speeds between 5 to 6 m/s at 10 m height of measurement. Even higher values could be found in Morocco at the coastal area, in Libya at some areas of the Mediterranean coast and in Egypt at the coast of the Red Sea. To limit the maximum installed capacity in each area (around the specific site) to a reasonable size, a general upper bound for wind power capacity at each site is assumed with 10 GW. This overall capacity can be divided into many smaller wind farms in the area around one identified wind site. This upper bound is set to the value of 10 GW as this value is assumed to be a maximum capacity which can be installed within a distance of up to 50 km to the point of the weather measurement data. As other studies have found large potentials and huge areas of available land for renewable energy sources in general, this assumption is not in contrast to the potentials at most of the sites. Further geographical restrictions and limitations such as use and slope of land or environmental issues (visibility effects, noise, etc.) are not included in this analysis here. A full list of all selected wind sites is shown in Table 34 (appendix).

After the selection of potential sites, the wind model generates profiles of the electricity output at each site by using the reference wind turbine (2 MW) and the meteorological data of Meteonorm.

Table 9: Wind resource assessment as reported in literature

Country Range of average wind

speeds (reported in source)

Measurement height Source Morocco 3.5 – 8.7 m/s 9.0 – 9.5 m/s 10 m –o– (Ouammi et al., 2010), (Oukili et al., 2010), Algeria 1.0 – 6.0 m/s 1.9 – 6.3 m/s 2.6. – 5.9 m/s 10 – 12 m 10 m 10 m (Merzouk, 2000) (Chellali et al., 2011), (Diaf and Notton, 2013), Tunisia 2.4 – 5.4 m/s

5.5 m/s

6 – 12 m 30 m

(Elamouri and Ben Amar, 2008), (Ben Amar et al., 2008)

Libya ~ 6 m/s 4.5 – 4.9 m/s 6.4 – 8.3 m/s 10 m 10 m 50 m (Elmabrouk, 2009),

(Mohamed and Elmabrouk, 2009) (Ekhlat et al., 2007) Egypt 6.9 – 10.4 m/s 5.5 – 6.5 m/s 25 m 25 m (El-Sayed, 2002), (Ahmed, 2012),

4.2.2 PV power plants and solar resources