Journal of Energy Bioscience 2012, Vol.3, No.1, 1-12
2
and non-supportive policy intervention has curbed the
development of small-scale wind technologies in
certain parts of the world (Ross et al., 2012; Barry and
Chapman, 2009). Proficient understanding of local wind
dynamics with advancement in small-wind wind
technologies gives stimulus to decentralize clean energy,
particularly in remote areas with appreciable wind
regimes (Nouni et al., 2007). This reiterates the need for
detailed regional wind resource assessment exercises.
1
Regional wind resource assessment
Wind resource assessment is the primary step towards
understanding the local wind dynamics of a region
(
Ramachandra et al., 1997). Wind flow developed due
to the differential heating of earth is modified by its
rotation and further influenced by local topography.
This results in annual (year to year), seasonal,
synoptic (passing weather), diurnal (day and night)
and turbulent (second to second) changes in wind
pattern (Hester and Harrison, 2003). Increased heat
energy generated due to industries and escalating
population in urban areas result in heat islands which
affects the wind flow as well.
1.1
Surface wind measurements
Wind characteristics like speed and direction
measured at meteorological stations (surface) aid in
assessing the local wind resource. Wind patterns are
observed to be tantamount for regions in proximity.
However, local winds have high topographical and
land cover influence, and assuming the wind data
from a measured site applicable for a nearby site of
interest calls for error. Monthly wind speed variation
for regions within a radius of 30 km shows similar
patterns but with difference in magnitude, and the
study suggests using 6 years of long term wind data
for satisfactory representation of monthly variations
(
Mani and Mooley, 1983). A one year wind speed data
maintains an error within ± 10% which reduces to ± 3%
for 3 years data but still burden the economics of a
wind energy based project (European Wind Energy
Association, 2012,
org/). The surface wind datasets sometime fail to
capture the diurnal variations especially during the
night hours, giving an elevated estimate of the daily
average as wind speeds are generally higher in the
daylight (Bekele and Palm, 2009). Despite these
complexities, wind resource assessments based on the
available surface measurements at different sites using
statistical tools have provided satisfactory results
(
Ramachandra and Shruthi, 2003; Elamouri and Ben,
2008;
Ullah et al., 2010, Dahmouni et al., 2011; Tiang
and Ishak, 2012).
1.2
Models for prospecting wind
Surface wind measurements being reliable sources of
information on the wind regime are available for only
few locations. Acquiring surface wind data is
expensive and time consuming. These gaps limit the
wider spatial and temporal understanding of regional
wind characteristics. In this regard, models like Wind
Atlas Analysis and Application Program (WAsP) and
Computational Fluid Dynamics (CFD) based on local
topography and climate help in micro-scale (1~10 km)
studies of wind resources. These models are validated
with dense surface measurements and are not
applicable for regions with thermally forced flows like
sea breeze and mountain winds for which meso-scale
(10
~100 km) models are preferred. A combination of
meso-scale and micro-scale models viz. the Karlsruhe
Atmospheric Meso-scale Model (KAMM/WAsP),
Meso Map and Windscape System along with
geoinformatics provide reliable wind prospecting and
have been tried for different regions (Coppin et al.,
2012,
). However,
these tools are expensive considering the scale of projects
in small wind areas.
1.3
Synthesised wind data
Synthesised wind data available from various sources
provide preliminary understanding of the wind regime
of a region. Depending on the physiographical
features and climatic conditions, these data help assess
wind potential in the region of interest validated by
long term surface wind measurements. Wind resource
atlas derived with the help of National Oceanic and
Atmospheric Administration (NOAA) and National
Aeronautical and Space Agency (NASA) Surface
Meteorology and Solar Energy (SSE) wind data,
validated with available surface measurements,
provided a range of mean wind speeds on a
meso-scale wind atlas for Newfoundland (Khan and
Iqbal, 2004). Similarly, a wind map for Bangladesh
was prepared from synthesised global data of NOAA
