2011 International Conference on Alternative Energy in Developing Countries and Emerging Economies
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III. R
ESULTS AND
D
ISCUSSION
Mean bias from the full annual simulation for wind
speed at 100 m agl at the PCD tower was 1.4 ms
-1
, which
is still an overestimation of wind resource but lower than
the mean bias in months chosen for investigation of land
surface inputs.
Hourly data was averaged over the entire year to
obtain annual mean wind speed at 20 m and 100 m agl.
At 20 m agl, annual mean wind speeds over Bangkok
range from 3.5 to 4.8 ms
-1
, and at 100 m agl they range
from 5.0 to 5.8 ms
-1
. As seen in Fig. 5, highest wind
speeds in Bangkok occur near the ocean in the
southernmost tip of the province, indicating land-sea
interactions influencing the wind resource. Additionally,
spatial variation in wind speed is greater closer to the
surface with wind speeds 0.25 to 0.5 ms
-1
lower over the
rough urban center than over the rest of the province,
demonstrating that surface roughness effects do not
extend up to 100 m agl.
Fig. 5. Annual mean wind speed over Bangkok at 20 m and 100 m agl.
Typically, annual mean wind speeds below 5.5 ms
-1
are not considered suitable for installation of
conventional turbines, and large-scale wind farms often
require 7-9 ms
-1
to be economically feasible. It is
therefore unlikely for large-scale installations to be
feasible in Bangkok. However, while not ideal, it may
still be possible for low speed wind turbines that begin
generating electricity at around 3 ms
-1
to be feasible in
the given wind resource, particularly near the coast and
outside urban areas.
IV. C
ONCLUSIONS
The mesoscale meteorological model MM5 was used
to investigate the wind resource over Bangkok, with
modified land surface inputs. The model was found to
better generate near-surface temperature with the
modified inputs than with default inputs, though it had
little impact on wind speed. The modified inputs were
used in simulations over an entire year to determine the
wind resource over the province.
The wind maps generated in this work are not
recommended to be used in place of observations over a
long (at least one year) period on-site and at the height
turbines are to be installed at. They also do not take into
account the height of the vegetation or urban canopies,
below which winds may be more turbulent and more
under the influence of local effects, and are not
recommended areas for wind turbine installation. The
maps do, however, indicate the magnitude of the wind
resource ranging from 3.5-5.8 ms
-1
at installable hub
heights over the region, and they also highlight areas in
the southern part of Bangkok that have a higher-than-
average wind resource when compared with the rest of
the province.
Because the wind resource was generally found to be
poor, one must carefully select a wind turbine to install in
Bangkok. Most commercial wind turbines worldwide are
designed for mean wind speeds larger than what exist in
Bangkok, and therefore wind turbines specially designed
to generate electricity at low wind speeds should be
considered.
A
CKNOWLEDGMENTS
The authors wish to express gratitude to the Air
Quality and Noise Management Bureau, Pollution
Control Department, Thailand, for supplying wind tower
data, and the LDD for supplying land cover data over
Thailand. We would like to thank the PSU/NCAR for the
MM5 model; and the Japan Meteorological Agency,
NCAR, and NCEP for providing reanalysis data. The
MODIS satellite data are distributed by the Land
Processes Distributed Active Archive Center (LP
DAAC), located at the U.S. Geological Survey (USGS)
Earth Resources Observation and Science (EROS)
Center. Global land cover data used is generously
provided by EC-JRC. This work was financially
supported by the Joint Graduate School of Energy and
Environment (JGSEE) and Center for Energy Technology
and Environment, and the Thailand Research Fund (TRF)
under Grant No. RDG5050016. Final thanks for general
assistance and support goes to members of the JGSEE
Computational Laboratory (Bang Khun Tien campus),
especially Phan Thanh Tung and Thayukorn
Prabamroong.
