The boosting of said sources has been advanced especially due to the global warming phenomenon and climate change, reason why, at the world level, the renewable power generation capacity increased to 167 gigawatts (GW) and reached 2.17 GW around the world. This represents an annual growth of close to 8.3% 1.
Since 2005, Colombia has a constant wind-power installed capacity of 19.5 MW (Irena, 2018); however, by being located in the intertropical convergence zone, Colombia has periods of the year in which the solar radiation and wind intensity could easily fill in or complement the power grid system. This is presented especially during the drought season due to the El Niño phenomenon, which generally causes important rain reduction, inclusively in areas not part of the power system. Its importance lies in that these clean energies could help supply the power basket of the country without emitting GEG.
Studies and measurements carried out by the Hydrology, Meteorology and Environmental Studies Institute (IDEAM, for its Spanish acronym) and the Mining Energy Planning Unit (UPME, for its Spanish acronym) have shown that there are three regions of the country that wind intensities are useful for power purposes: the high province of Guajira area, the Caribbean sea and the high mountainous areas of the Andean region. There, the wind intensities are persistent and greatly over 5 m/s during the whole year, making them exploitable for power production; however, in this research project, they did not make an accurate quantification of this resource, so therefore the Universidad Nacional de Colombia (UNal) 2 carried out meteorological simulation in the high Andean region for the purpose of calculating the variability and availability of the winds.
To achieve this goal, they used numeric models to simulate the complexity of the dynamic and physical processes that occur in the atmosphere and that also allows obtaining results in different space-time scales and resolutions.
To show the wind field in high mountain areas in Colombia they used the regional Weather Research and Forecasting (WRF) model, developing Navier-Stokes fluid differential equations applied to the atmosphere. Despite all physical processes as the water change phase and others cannot be solved by this formula, the WRF has different options to parameterize other physical factors that occur in the atmosphere, such as long and short wave radiation, ground-atmosphere interaction, the planetary boundary layer and the formation of clouds explained through microphysics and cumulus parameterizations.
The research project analyzes the planetary boundary layer parameterization (PBL) as it is the area of the atmosphere closest to the earth surface, where interaction is perceived through heat exchange, moment and humidity, and where PBL schemes are essential to obtain the low-level atmosphere wind profile to estimate the power potential produced by the wind.
Therefore, the WRF model was run in grid resolutions of 3km x 3km (dominion 2) and 9km x 9km (dominion 1) combining 7 PBL and 3 surface parameterizations for a total of 11 simulations according to their capability to reproduce turbulence kinetic energy (TKE), an important indicator in describing PBL turbulence. This indicator allows having an energy boundary that boosts the production and destruction of wind twisters.
The 11 simulations were used to extract information of the wind direction and speed data on an hour to hour basis for 2013, through it zonal and meridian components (u, v), information which is statistically validated by meteorological station observations, using wind speed as the main decision variable to choose the final parameterization scheme.
The dominion that better simulated the field wind is the 9km x 9km, using Bougeault–Lacarrère (BouLac) scheme parameterizations–which has the advantage of forecasting events with induced orography turbulence– and the MM5 for surface, which is more efficient to simulate turbulence flows during the day when the turbulence kinetic energy is higher.
As this is the expected result taking into account the complex topographical characteristics of the study areas, the simulations in other parts of the Colombian territory should obey other types of parameterizations, deserving a particular analysis to calculate the correct wind potential for use as a power alternative.
Once the wind field for the high mountain area was obtained for the 9km x 9km dominion, they calculated the Weibul distribution form and scale using 5 numeric and statistical methods; distribution successfully used to describe the frequency curves in the speed measurements in intervals of at least a year 3. The results of the methods were averaged, obtaining the wind speed and wind energy density. From this, they used the information from different variables and different heights in the study areas to reclassify them as a function of the wind characteristics 4.
The wind speeds discovered in the first 10m were classified as poor (wind speeds under 5m/s), as ground friction and other local phenomena can impact in values of low usefulness to produce wind power, and its offer depends more on the seasonality and the time of day, while wind energy density above 50m has greater potentiality relevance values, and they discovered “exceptional”, the WRF model, wind speeds over or equal to 8.2m/s with wind energy equal or above 1,000 W/m2, especially in the mountainous areas of the Provinces of Nariño, Cauca, Valle del Cauca, Tolima, and Quindío, among others.
Lastly, knowing the PBL parameterizations that better adjust to the observations in the high mountain areas would allow users to perform operational simulations with the parameterizations produced in this study, besides producing information of optimal places that provide wind resource and its variability during an average year.
Furthermore, knowledge of the wind resource through numeric modeling would help diminish the uncertainness of its variability and look for its integration into the Colombian power grid, as a great part of the power offer in the country depends on water resources and considering the risks linked to drought phenomena in the country, the contribution of wind power to the service portfolio would be very important in determining the prices of the Colombian power basket.
Link to the research project:
1. Irena. (2018). International Renewable Energy Agency. All Rights Reserved. http://www.irena.org/wind
2. Simulación meteorológica en zona de alta montaña para estimar el potencial eólico.
3. Odo, F. C., Offiah, S. U., and Ugwuoke, P. E. (2012). Weibull distribution-based model for prediction of wind potential in Enugu, Nigeria. Advances in Applied Science Research, 3(2), 1202-1208.
4. Figueredo, C. M. (2011). Fuentes renovables de energía Cuba. http://www.cubasolar.cu/biblioteca/energia/Energia55/HTML/articulo03.htm
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