Wind Resource Modelling in

Transcription

1 American Journal of Energy and Power Engineering 2016; 3(1): 1-9 Published online March 4, 2016 ( ISSN: Wind Resource Modelling in Ecuador Park Il-Soo 1, Jang Su-Hwan 1, Jang Yu-Woon 1, Ha Sang-sub 1, Chung Kyung-Won 1, Jeffrey S. Owen 1, Lee Seung-Woo 2, Choi Young-Jean 2 Keywords Wind Resource Map, Numerical Simulation, Ecuador Received: December 7, 2015 Revised: December 17, 2015 Accepted: December 19, Korea-Latin America Green Convergence Center, Hankuk University of Foreign Studies, Seoul, Korea 2 Applied Meteorology Research Division, National Institute of Meteorological Research, Seoul, Korea address nierpis@daum.net (P. Il-Soo) Citation Park Il-Soo, Jang Su-Hwan, Jang Yu-Woon, Ha Sang-sub, Chung Kyung-Won, Jeffrey S. Owen, Lee Seung-Woo, Choi Young-Jean. Wind Resource Modelling in Ecuador. American Journal of Energy and Power Engineering. Vol. 3, No. 1, 2016, pp Abstract The aim of this research was to perform a numerical wind simulation for a wind resource map and analyzed the wind resources that will be serviced as the preliminary reference material in assessing potential wind farm locations in Ecuador. The study also provides comprehensive information for policies to assessment the geographical potential of wind energy resources in areas where wind turbines can be installed. The wind resource map at 80m above-sea-level (ASL) was simulated by WRF over the surrounding region, including Ecuador. The wind speed in the Andes Mountains was ranging from 4 to 6 m/s. The prevailing wind directions in southern and northern regions were the easterly and southerly respectively. 1. Introduction The installed wind energy capacity over the world in 2014 amounted to 369,597 MW [1]. Wind power has been known as one of the most potential and techno-economically viable renewable energy sources of this generation [2]. Many countries have been paying growing attention to renewable energy as wind power for decreasing greenhouse gases to meet with global warming phenomena, and for generating environmentally friendly energy [3]. Wind energy turbines in 2014 have been concentrated in the China, USA, Germany, Spain, and India [1]. The EU has planned that wind power turbines will be expanded by 20% of the total electric generation by In 2014, the wind energy installed capacity in Brazil, Mexico, Chile, and Uruguay were 5,939 MW, 2,551 MW, 836 MW, 464 MW respectively [1], [4]. Ecuador began wind development in 2007, with the creation of wind farms in the Galapagos. Nowadays, several projects have been developed in the province of Loja in where a very high quality potential site was identified, with good, stable, almost unidirectional winds, and with the objective of 200 MW in a couple of years [5]. In Ecuador, the total of wind energy installations in 2013 were 19 MW [6]. And, Goldwind Corp. is set to install turbines on two of the highest wind farms in the world at around 2,900 metres above sea level. The two projects are the Ducal-Membrillo and Huayrapamba wind farms, at 50 MW and 54 MW of installed capacity respectively [7]. The available wind resource has mainly depended on the climatology of the concerned region. Therefore, in order to exploit wind energy at any prospective site, it is very

2 2 Park Il-Soo et al.: Wind Resource Modelling in Ecuador important to survey wind resources which are available as national programs [2]. Regarding wind resource research, many investigations have been carried out. For example, the monthly forecasts of the average wind speed in Portugal, the analyses of wind time series in Oaxaca-Mexico, the wind characteristic and energy potential in Cucuta-Colombia, Kutahya-Turkey, in the Pearl River Delta region in China, and Taiwan, all have been studied [8]. NREL (National Renewable Energy Laboratory) supported by the U.S. Department of Energy is helping to develop high-resolution projections of wind resources worldwide [9]. A numerical simulation by WRF (Weather Research and Forecast model) was performed to investigate the wind farm in Korea on complex terrain and Dragash - Kosovo located on higher elevation areas [10], [11]. A wind resource map in Ecuador has not been developed, but measurements conducted at a potential site revealed wind speeds of close to 6 m/s at 30 m above ground [12]. In this paper, we developed a wind resource map by the modelling for wind power exploitation in Ecuador. 2. Methodology For a wind resource map simulation at 80m ASL in Ecuador surrounding regions, WRF (Weather Research and Forecasting) developed by NCAR (National Center for Atmospheric Research) is used to recognize the weather variability. WRF is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. WRF allows researchers the ability to conduct simulations reflecting either real data or idealized configurations. WRF provides operational forecasting a model that is flexible and efficient computationally, while offering the advances in physics, numerics, and data assimilation contributed by the research community [13]. Terrain and land cover data (100 m 100 m) from the United States Geological Survey, and FNL (GFS Final analysis) that is reinterpretation of data (1 1 ) from the National Centers for Environmental Prediction, are applied to numerical wind simulation as initial values. The atmospheric dynamics of WRF have been simulated for the area shown in Fig. 1. Two nested domains have been used with horizontal resolutions of 30 km and 10 km. In order to simulate the continuous wind flow in the boundaries of Ecuador, the surrounding countries adjacent to Ecuador have been included at the inner domain. The boundary layer and physical schemes for numerical simulation are shown in Table 1. Figure 1. The outer domain (South America) in left and inner one (surrounding regions including Ecuador) in right for WRF model simulation.

3 American Journal of Energy and Power Engineering 2016; 3(1): Table 1. Configurations of numerical model. Model WRF ver [14] Initial data NCEP FNL (1 resolution) Micro physics. Double moment 6-class scheme [15] PBL physics. Mellor-Yamada-Janjic [16] Physics Cumulus parameterization scheme Kain-Fritsch [17] Surface physics. Noah Longwave radiation RRTMg Shortwave radiation Goddard 3. Simulation of Wind Resource Map 3.1. Wind Characteristics in South America Regions The simulated annual average wind speed and direction at 80 m ASL on the outer domain including most of South America regions were shown in Fig. 2. The strongest wind speed, around 8 m/s, occurred in South Pacific Ocean and the Andes Mountains in Chile, and South Atlantic Ocean along the northeast coastline of Brazil. The moderate wind speed, around 4 m/s, occurred in the neighboring coastline to South Pacific Ocean in Peru, Chile and Ecuador, and in the range of inland regions about 700 km further away from the coastline in Brazil. The weak wind speed, around 1m/s, occurred in the northwest regions, the inland regions and coastline located at southernmost in Brazil. The prevailing wind directions in South Pacific Ocean were from the southeast to the southwest. The prevailing wind directions in the Andes Mountains were southeast. The prevailing wind directions in Argentina adjacent to the Andes Mountains were northwest. The prevailing wind directions in the northern regions in Argentina, and the western and eastern coastline regions in Brazil were southeast. The prevailing wind directions in most regions except the western and eastern coastline regions in Brazil were northeast. Figure 2. The simulated annual average wind speed in left and annual average wind direction in right on the outer domain including most of South America regions at 80 m ASL Wind Resource Map in Ecuador In coastal areas, predicting wind behavior is complicated by changes in roughness and atmospheric stability at the coastline. Previous studies have shown that physical models can predict changes in wind speed reasonably well, although differences in stability conditions on land and offshore are important [18]. Strong, frequent winds are ideal for generating electricity. For a specific location, the annual average wind speed is used to calculate the amount of energy in the wind blowing, which is expressed as watts per square meter [2]. The annual average wind speed at 80 m ASL is shown in Fig. 3. The strongest wind speed, around 6 m/s, appeared at about 50 km offshore from the coastline, but there was little difference in the wind speed over the sea farther from the coast. On the other hand, the wind speed

4 4 Park Il-Soo et al.: Wind Resource Modelling in Ecuador steadily decreased to below 2 m/s farther inland from the coast. The wind speed in the Andes Mountains was in the ranging from 4 to 6m/s. The prevailing wind directions on the offshore and land locations were from the southwest and from the southeast in the Andes. At 9 locations (Fig. 4, Table 2), the monthly frequency of wind in four directions (northerly: greater than 315 or less than 45, easterly: greater than 45 and less than 135, southerly: greater than 135 and less than 225, and westerly: greater than 225 and less than 315) and monthly average wind speed for the wind simulated by WRF are analyzed as summarized in Table 3. To understand some details of the wind characteristics, the monthly wind speed and direction at 80 m ASL on surrounding region in Ecuador was analyzed as shown in Figure 5 and 6. In January, the strongest wind speed was 4.0 m/s at a westerly prevailing wind at Canar in the southern region at Riobamba. The prevailing wind directions in northern regions at Riobamba were southerly with the wind speed in the range m/s, but the prevailing wind directions in the southern regions were easterly with wind speeds ranging from 2.5 to 3.5 m/s. In February, The strongest wind speed was 4.3 m/s at an easterly prevailing wind at Canar. The prevailing wind directions in northern regions were southerly with the wind speed in the ranging from 2.1 to 2.6 m/s, and the prevailing wind directions in the southern regions were easterly with the wind speeds ranging from 2.5 to 4.3 m/s. In March, The strongest wind speed was 4.2 m/s with an easterly prevailing wind at Canar. The prevailing wind Figure 3. The simulated annual average wind speed in left and annual average wind direction in right on surrounding regions including Ecuador at 80 m ASL. Figure 4. Location of the 9 observations in Ecuador.

5 American Journal of Energy and Power Engineering 2016; 3(1): Table 2. Main features at 9 observations. Observations WMO ID Latitude (degree) Longitude (degree) ASL (m) Ibarra Inguincho Tomalon Latacunga Rumipamba Riobamba Canar Catamayo Loja La Argelia directions in northern regions were southerly with the wind speed in the ranging from 2.2 to 2.5 m/s, and the prevailing wind directions in the southern regions were easterly with wind speeds ranging from 2.2 to 4.2 m/s. In April, The strongest wind speed was 3.9 m/s with a westerly prevailing wind at Canar. The prevailing wind directions in northern regions were southerly with wind speeds ranging from 2.1 to 2.6 m/s. In southern regions, the prevailing wind directions were easterly with wind speeds in the range of 2.4 to 3.1 m/s. In May, The strongest wind speed was 4.0 m/s with a westerly prevailing wind at Canar. The prevailing wind directions in northern regions were northerly with the wind speed in the range of 2.4 to 3.0 m/s. The prevailing wind in southern regions was easterly with the wind speed in the range of 2.7 to 3.0 m/s except the prevailing wind was northerly at Riobamba and the prevailing wind was westerly at Canar. In June, The strongest wind speed was 4.0 m/s with a westerly prevailing wind at Canar. The prevailing wind directions in northern regions were northerly with the wind speed in the range of 2.2 to 2.9 m/s and, the prevailing wind in the southern regions was easterly with the wind speeds ranging from 2.0 to 3.1 m/s except the prevailing wind was westerly at Canar. In July, The strongest wind speed was 4.4 m/s with a westerly prevailing wind at Canar. The prevailing wind directions in northern regions were northerly with the wind speed in the range of 2.1 to 2.7 m/s and, the prevailing wind directions in the southern regions were easterly with wind speeds ranging from 2.3 to 3.3 m/s except the prevailing wind was westerly at Canar. In August, The strongest wind speed was 3.8 m/s with a westerly prevailing wind at Canar. The prevailing wind directions in northern regions were easterly with the wind speed in the range of 1.6 to 1.7 m/s and, the prevailing wind directions in the southern regions were easterly with the wind speeds ranging from 2.5 to 3.3 m/s except the prevailing wind was westerly at Canar. In September, The strongest wind speed was 4.4 m/s with a westerly prevailing wind at Canar. The prevailing wind directions in northern regions were north-easterly with the wind speed in the range of 1.7 to 2.5 m/s and, the prevailing wind directions in the southern regions were easterly with the wind speed in the range of 2.7 to 3.8 m/s except the prevailing wind was southerly at Catamayo and the prevailing wind was the westerly at Canar. In October, The strongest strong wind speed was 5.6 m/s with an easterly prevailing wind at Canar. The prevailing wind in northern regions was south-easterly with the wind speed in the range of 1.4 to 2.2 m/s, and the prevailing wind in the southern regions was easterly with the wind speed ranging from 2.2 to 6.0 m/s. In November, The strongest strong wind speed was 3.9 m/s with an easterly prevailing wind at Canar. The prevailing in northern regions was southerly with the wind speed in the range of 2.2 to 2.7 m/s, and the prevailing wind in southern regions was easterly with wind speeds ranging from 2.1 to 4.0 m/s. In December, The strongest wind speed was 4.3 m/s with a westerly prevailing wind at Canar. The prevailing wind in northern regions was southerly with the wind speed in the range of 2.3 to 2.8 m/s, and the prevailing wind in the southern regions was easterly with the wind speeds ranging from 2.1 to 3.7 m/s. Generally, the strongest wind speed at Canar was 6.0 m/s with an easterly prevailing wind in October. At other times, the highest wind speed was between 3.8 and 4.4 m/s with a westerly prevailing wind. The prevailing wind directions in southern regions were the easterly except for a westerly prevailing wind at Canar. In northern regions, the prevailing wind was southerly except for a northerly prevailing wind from May to July and an easterly prevailing wind from August to September. The WRF wind speed at any of the sites may have been underestimated or overestimated. When considering that the RMSE for high resolution of 1 km by 1 km was 1.5 ~2.0 m/s [19], we can understand that the WRF prediction (10 km by 10 km) in Ecuador still shows a reasonable correlation with the monitoring data (Table 4).

6 6 Park Il-Soo et al.: Wind Resource Modelling in Ecuador Table 3. The monthly frequency of wind in four directions (N: greater than 315 or less than 45, E: greater than 45 and less than 135, S: greater than 135 and less than 225, and W: greater than 225 and less than 315) and monthly average wind speed at 80 ASL for the wind simulated by WRF. Month N 3.8 1) (1.2) 2) 5.4(1.3) 6.7(1.2) 4.2(1.3) 4.2(1.3) 5.8(1.8) 9.2(1.8) 0.8(1.1) 1.3(1.0) E 12.9(1.5) 13.3(1.4) 12.9(1.5) 26.7(1.4) 36.7(1.5) 46.7(2.5) 45(3.2) 49.2(3.2) 70.4(3.5) S 60.0(3.1) 62.5(2.9) 58.8(2.9) 53.3(2.6) 42.5(2.7) 29.2(2.8) 3.8(1.3) 35.4(2.8) 23.3(2.9) W 23.3(1.8) 18.8(1.7) 21.7(1.9) 15.8(1.9) 16.7(2.3) 18.3(2.5) 42.1(4) 14.6(1.8) 5.0(1.4) 2 N 14.8(1.2) 13.9(1.0) 15.3(1.1) 11.7(1.2) 9.0(1.0) 6.7(1.4) 7.2(2.5) 8.1(1.2) 10.3(1.4) E 14.4(1.4) 13.0(1.3) 18.4(1.6) 26.0(1.5) 33.2(1.6) 39.9(2.5) 40.4(4.3) 46.6(2.8) 61.4(3.4) S 47.5(2.6) 48.9(2.5) 45.3(2.4) 35.0(2.1) 34.1(2.3) 23.8(2.2) 6.7(1.6) 26.9(3.0) 17.0(3.1) W 23.3(1.6) 24.2(1.4) 21.1(1.6) 27.4(1.9) 23.8(1.9) 29.6(2.4) 45.7(3.8) 18.4(1.5) 11.2(1.6) 3 N 14.6(2.0) 13.8(2.2) 11.7(2.0) 14.2(2.2) 14.2(1.8) 8.8(2.6) 4.2(1.7) 12.9(1.4) 13.3(1.2) E 17.1(1.8) 15.4(1.7) 21.7(2.0) 19.2(1.5) 22.9(1.7) 44.6(2.2) 47.5(4.2) 35.4(2.9) 54.6(3.4) S 46.3(2.5) 50.4(2.4) 43.8(2.3) 47.1(2.2) 42.9(2.2) 25.0(2.4) 4.2(1.0) 30.4(3.2) 20.0(2.7) W 22.1(1.4) 20.4(1.5) 22.9(1.6) 19.6(1.9) 20.0(2.0) 21.7(2.2) 44.2(3.9) 21.3(1.3) 12.1(1.3) 4 N 28.5(2.1) 29.7(2.1) 28.9(2.1) 26.8(2.3) 25.1(2.4) 20.1(2.8) 10.0(3.0) 7.5(1.1) 7.5(1.1) E 20.5(2.1) 21.3(2.0) 23.0(1.9) 28.9(1.8) 32.6(1.9) 48.5(2.4) 34.7(3.1) 43.1(2.6) 61.1(3.0) S 30.5(2.6) 28.9(2.4) 30.1(2.5) 31.8(2.1) 29.3(2.2) 16.3(2.6) 6.7(1.5) 33.1(2.6) 25.9(2.4) W 20.5(1.4) 20.1(1.3) 18.0(1.4) 12.6(1.4) 13.0(1.6) 15.1(2.4) 48.5(3.9) 16.3(1.2) 5.4(1.4) 5 N 52.9(2.4) 53.3(2.5) 52.9(2.6) 47.9(2.8) 45.8(2.9) 42.1(3.2) 12.5(2.4) 5.0(1.0) 3.3(1.1) E 28.3(1.7) 26.7(1.6) 28.3(1.9) 34.2(1.8) 37.5(1.9) 50.0(2.4) 22.9(2.6) 36.7(2.7) 60.4(3.0) S 9.2(1.5) 7.5(1.2) 7.9(1.3) 7.5(1.2) 7.1(1.3) 2.5(1.8) 5.0(1.8) 33.8(2.6) 22.5(2.7) W 9.6(1.4) 12.5(1.3) 10.8(1.3) 10.4(1.5) 9.6(1.5) 5.4(2.0) 59.6(4.0) 24.6(1.1) 13.8(1.1) 6 N 43.5(2.2) 46.9(2.3) 43.9(2.3) 42.3(2.7) 40.6(2.9) 31.0(3.5) 9.2(2.6) 4.6(0.8) 3.8(1.2) E 21.3(1.7) 20.1(1.5) 25.5(1.6) 31.4(1.5) 39.3(1.8) 49.0(2.5) 9.2(2.0) 41.8(2.7) 61.1(3.1) S 15.9(1.1) 15.1(1.0) 15.1(1.0) 10.9(1.1) 8.4(1.1) 5.0(1.2) 2.9(1.0) 36.4(2.4) 27.6(2.3) W 19.3(1.3) 18.0(1.4) 15.5(1.5) 15.5(1.4) 11.7(1.3) 15.1(1.7) 78.7(4.0) 17.2(1.3) 7.5(1.1) 7 N 50.4(2.1) 50.4(2.1) 53.8(2.0) 47.9(2.6) 41.7(2.7) 34.6(3.2) 12.1(1.9) 4.6(1.4) 6.3(1.3) E 25.8(1.6) 26.3(1.7) 26.3(1.7) 33.3(1.7) 43.3(1.8) 52.9(2.3) 15.0(2.3) 37.1(2.9) 60.4(3.3) S 7.9(1.9) 8.3(1.3) 7.9(1.8) 10.4(1.6) 8.8(1.7) 3.3(2.1) 3.3(1.1) 37.5(2.6) 25.8(2.6) W 15.8(1.3) 15.0(1.4) 12.1(1.3) 8.3(1.5) 6.3(1.8) 9.2(2.1) 69.6(4.4) 20.8(1.3) 7.5(1.1) 8 N 27.5(2.3) 30.0(2.4) 32.1(2.2) 33.8(2.6) 35.8(2.6) 31.3(3.1) 7.9(1.9) 1.7(0.6) 1.7(0.4) E 35.4(1.6) 32.9(1.6) 31.7(1.6) 36.7(1.6) 39.6(1.7) 50.8(2.5) 11.7(2.5) 40.8(2.8) 65.8(3.3) S 15.0(1.6) 15.4(1.3) 16.7(1.5) 15.8(1.5) 12.9(1.6) 4.2(1.8) 3.8(1.3) 37.9(2.9) 23.8(2.8) W 22.1(1.4) 21.7(1.5) 19.6(1.4) 13.8(1.4) 11.7(1.4) 13.8(2) 76.7(3.8) 19.6(1.4) 8.8(1.1) 9 N 26.8(1.9) 28.0(2.1) 27.6(2.2) 32.6(2.5) 33.1(2.7) 28.5(3.4) 12.6(3.2) 1.7(0.4) 2.1(1.9) E 26.4(2.2) 26.8(2) 29.3(2.0) 37.2(1.7) 42.3(1.7) 54.0(2.7) 7.5(1.5) 38.1(3.5) 63.2(3.8) S 27.6(1.6) 26.4(1.6) 23.9(1.6) 16.7(1.5) 12.1(1.6) 5.0(1.4) 3.8(0.6) 51.9(2.7) 32.2(2.4) W 19.3(1.4) 18.8(1.3) 19.3(1.5) 13.4(1.5) 12.6(1.5) 12.6(2.3) 76.2(4.4) 8.4(1.0) 2.5(0.8) 10 N 14.2(1.6) 12.5(1.8) 15.4(1.7) 12.9(2.3) 13.3(2.2) 10.4(3.1) 9.6(2.6) 1.3(1.5) N/A E 20.0(1.8) 22.9(1.6) 22.1(1.4) 24.6(1.5) 33.8(1.6) 44.2(2.2) 42.9(5.6) 49.2(3.1) 70.8(3.7) S 40.8(2.2) 35.8(2.2) 37.9(2.2) 41.3(1.8) 35.0(1.9) 23.8(1.9) 5.8(1.2) 38.8(2.9) 25.4(2.5) W 25.0(1.4) 28.8(1.4) 24.6(1.4) 21.3(1.8) 17.9(1.7) 21.7(2.3) 41.7(3.4) 10.8(1.5) 3.8(0.9) 11 N 11.7(1.6) 10.5(1.5) 11.3(1.6) 11.7(1.5) 8.8(1.8) 13.0(2.4) 5.4(2.6) 0.8(0.5) 0.4(0.6) E 23.4(1.7) 23(1.8) 24.3(1.6) 29.3(1.5) 32.2(1.7) 41.4(2.1) 54.4(3.9) 52.7(3.1) 76.6(3.6) S 42.7(2.7) 41.8(2.6) 42.7(2.5) 36.8(2.3) 37.2(2.2) 22.6(2.4) 5.0(1.5) 32.6(3.2) 18.8(2.7) W 22.2(1.5) 24.7(1.5) 21.8(1.5) 22.2(1.7) 21.8(1.6) 23.0(2.2) 35.2(3.6) 13.8(1.7) 4.2(1.2) 12 N 13.3(1.1) 12.1(1.1) 13.8(1.1) 8.3(1.2) 10.0(1.2) 7.5(1.7) 8.8(1.9) 1.3(1.0) 0.8(0.7) E 11.3(1.6) 12.9(1.6) 15.8(1.4) 23.8(1.4) 29.2(1.7) 43.3(2.1) 42.5(3.5) 46.7(3.4) 67.9(3.7) S 51.3(2.8) 55.4(2.5) 48.3(2.7) 46.7(2.3) 37.5(2.4) 24.2(2.7) 4.2(2.1) 40.4(2.8) 27.9(2.4) W 24.2(1.6) 19.6(1.6) 22.1(1.9) 21.3(1.8) 23.3(1.7) 25(2.1) 44.6(4.3) 11.7(1.3) 3.3(0.8) 1) Frequency (%), 2) Average wind speed (m/s) Table 4. The statistic performance for wind speed between WRF prediction and monitoring data. ID RMSE (m/s)

7 American Journal of Energy and Power Engineering 2016; 3(1): Figure 5. The simulated monthly average wind speed on surrounding regions including Ecuador at 80 m ASL.

8 8 Park Il-Soo et al.: Wind Resource Modelling in Ecuador Figure 6. The simulated monthly average wind directions on surrounding regions including Ecuador at 80 m ASL.

9 American Journal of Energy and Power Engineering 2016; 3(1): Conclusion The wind resource map at 80m ASL was simulated by WRF over the south America region including Ecuador. The strongest wind speed, around 6 m/s, appeared at about 50 km offshore from the coastline, but there was little difference in the wind speed over the sea farther from the coast. On the other hand, the wind speed steadily decreased to below 2 m/s farther inland from the coast. The wind speed in the Andes Mountains was in the range from 4 to 6 m/s. The prevailing wind directions in southern regions at Riobamba were the easterly except for a westerly prevailing wind at Canar. In northern regions at Riobamba, the prevailing wind was southerly except for a northerly prevailing wind from May to July and an easterly prevailing wind from August to September. Generally, the strongest wind speed at Canar was 6.0 m/s with an easterly prevailing wind in October. At other times, the highest wind speed was between 3.8 and 4.4 m/s with a westerly prevailing wind. The results of this research were expected to be serviced as the preliminary reference material in assessing potential wind farm locations in Ecuador. Acknowledgements Supported by National Research Foundation of Korea (NRF B00004) funded by the Korean Ministry of Education. References [1] [2] Shikha Singh, T. S. Bhatti, D. P. Kothari. A review of Wind- Resource-Assessment Technology. Journal of Energy Engineering 2006; 1: 32: 1. [3] David Watts, Danilo Jara. Statistical analysis of wind energy in Chile. Renewable Energy 2010; 10: [4] Jang, Yu-Woon, Park, Il-Soo, Lee, Gang Woong. Wind resource assessment with meteorological data in Chile. Institute of Latin American Studies, Hankuk University of Foreign Studies 2012; 31: [5] [6] [7] -install-high-altitude-projects-ecuador. [8] Laerte de Araujo Lima, Celso Rosendo Bezerra Filho. Wind energy assessment and wind farm simulation in Triunfo- Pernambuco, Brazil. Renewable Energy 2010; 35: [9] [10] Myungsung Lee, Seung Ho Lee, Nahmkeon Hur, Chang-koon Choi. A NUMERICAL SIMULATION OF FLOW FIELD IN A WIND FARM ON COMPLEX TERRAIN. APCWE-V ; 8pp. [11] Bereket, L. H, Tesfamichael, B. G, Asmerom M. G. Wind Resource Assessment in Dragash Kosovo. UNDP Conservation of Biodiversity and Sustainable Land Use Management in Dragash, 2013; 39pp. [12] and South America Ecuador, Panama and Peru. [13] [14] Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers. A description of the advanced research WRF version 3. NCAR Tech. Note TN-475+STR 2008; 113pp. [15] Lim, K.-S. and S.-Y. Hong. Development of effective doublemoment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rew. 2010; 138: [16] Janjic, Z. I. Nonsingular implementation of the Mellor- Yamada level 2.5 scheme in the NCEP Meso model, NCEP Office Note, 2002; 4: 7: 61. [17] Kain, J. S. The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 2004; 43: [18] Barbara J., Francesco D., Bernhard L., Torsten K. and Jens T. Offshore Wind Resource Assessment with WAsP and MM5: Comparative Study for the German Bight. Wind Energy 2007; 10: [19] Resource Map.