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American Journal of Energy and Power Engineering 2016; 3(1): 1-9
Published online March 4, 2016 (http://www.aascit.org/journal/ajepe)
ISSN: 2375-3897
Keywords Wind Resource Map,
Numerical Simulation,
Ecuador
Received: December 7, 2015
Revised: December 17, 2015
Accepted: December 19, 2015
Wind Resource Modelling in Ecuador
Park Il-Soo1, Jang Su-Hwan
1, Jang Yu-Woon
1, Ha Sang-sub
1,
Chung Kyung-Won1, Jeffrey S. Owen
1, Lee Seung-Woo
2,
Choi Young-Jean2
1Korea-Latin America Green Convergence Center, Hankuk University of Foreign Studies, Seoul,
Korea 2Applied Meteorology Research Division, National Institute of Meteorological Research, Seoul,
Korea
Email address [email protected] (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. 1-9.
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 2020. 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 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.
American Journal of Energy and Power Engineering 2016; 3(1): 1-9 3
Table 1. Configurations of numerical model.
Model WRF ver. 3.3.1 [14]
Initial data NCEP FNL (1° resolution)
Physics
Micro physics. Double moment 6-class scheme [15]
PBL physics. Mellor-Yamada-Janjic [16]
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.
3.2. 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 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 2.6-3.1 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.
American Journal of Energy and Power Engineering 2016; 3(1): 1-9 5
Table 2. Main features at 9 observations.
Observations WMO ID Latitude (degree) Longitude (degree) ASL (m)
Ibarra 84043 0.3389 -78.1364 2218
Inguincho 84045 0.2583 -78.7342 3185
Tomalon 84056 0.0333 -78.2333 2797
Latacunga 84123 -0.9069 -78.6156 2787
Rumipamba 84143 -1.0181 -78.5922 2628
Riobamba 84176 -1.6536 -78.6569 2800
Canar 84226 -2.5514 -78.9375 3083
Catamayo 84265 -3.9958 -79.3719 1238
Loja La Argelia 84270 -4.0364 -79.2011 2130
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 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
84043 84045 84056 84123 84143 84176 84226 84265 84270
1 N 3.81)(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 84043 84045 84056 84123 84143 84176 84226 84265 84270
RMSE (m/s) 2.16 2.73 2.55 2.97 2.69 3.32 3.07 2.58 4.31
American Journal of Energy and Power Engineering 2016; 3(1): 1-9 7
Figure 5. The simulated monthly average wind speed on surrounding regions including Ecuador at 80 m ASL.
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.
American Journal of Energy and Power Engineering 2016; 3(1): 1-9 9
4. 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-2009-413-B00004) funded by the Korean Ministry of
Education.
References
[1] http://www.gwec.net/global-figures/graphs.
[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: 1-11.
[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: 121-144.
[5] http:// http://www.barlovento-recursos.com/noticias.php/News.
[6] https://en.wikipedia.org/wiki/Wind_power_by_country.
[7] http://www.windpowermonthly.com/article/1294525/goldwind-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: 2705-2713.
[9] http://www.nrel.gov/wind/international_wind_resources.html.
[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-V11. 2009; 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] http:// www.repower.de/Central and South America Ecuador, Panama and Peru.
[13] http://www.wrf-model.org/index.php.
[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 double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rew. 2010; 138: 1587-1612.
[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: 170-181.
[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: 121-134.
[19] http://www.greenmap.go.kr/Meteorological Resource Map.