Wind Resource Modelling in Ecuador - AASCITarticle.aascit.org/file/pdf/9250772.pdfLee Seung-Woo,...

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


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.


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


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


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).


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


Figure 5. The simulated monthly average wind speed on surrounding regions including Ecuador at 80 m ASL.


Figure 6. The simulated monthly average wind directions on surrounding regions including Ecuador at 80 m ASL.


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.

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