Prospects and Challenges of Decentralized Wind Applications in the Himalayan Terrain 
,
Gautham Krishnadas1 
2 Centre for Sustainable Technologies, India Institute of Science, 560012, India
3 Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP),Indian Institute of Science, Bangalore, 560012, India
Author Correspondence author
Journal of Energy Bioscience, 2012, Vol. 3, No. 1 doi: 10.5376/jeb.2012.03.0001
Received: 03 Sep., 2012 Accepted: 12 Sep., 2012 Published: 19 Oct., 2012
Ramachandra and Krishnadas, 2012, Prospects and Challenges of decentralized wind applications in the Himalayan Terrain, Journal of Energy Bioscience, Vol.3, No.1 1-12 (doi: 10.5376/jeb.2012.03.0001)
Wind energy has the potential to meet energy needs in remote areas. Exploitation of wind resource requires prospecting at the regional levels to assess the technical feasibility for small-scale wind applications. In situations of sparse primary data (surface wind), synthesised wind data based on prudent models are helpful. The current study focuses on the prospects of wind energy in the federal state of Himachal Pradesh, India, characterized by undulating terrain. Three synthesised wind data were collected based on physiographical understanding of the region and validated with long term surface wind measurements available for limited locations. The most representative synthesised wind data were re-validated using statistical methods and seasonal wind profiles were mapped through geospatial techniques. Variations of seasonal wind speeds in the region were consistent with surface measurements and highest range of 1~3.25 m/s was observed in the monsoon season. Large spatial influences of the elevation gradient were observed in the seasonal wind profiles. The high elevation zone (including Lahual Spiti, Kinnaur, Kullu and Shimla districts) in Himachal Pradesh have relatively higher wind speeds (> 2 m/s) during all seasons. These districts were identified as suitable candidates for detailed wind exploration. Wind potential in Himachal Pradesh is observed to be suitable for small-scale wind applications like low wind speed turbines, agricultural water pumps, wind-solar hybrids, space/water heaters, battery charging etc. Improvement in small-scale wind technologies will provide impetus to decentralised and cost effective solutions to meet energy demand in remote regions sustainably.
Introduction
Wind energy based electricity earned prominence in 19th Century. This suffered a major setback with the highly subsidized fossil-fuel based centralized electricity generation and distribution. However, oil crisis of 1970’s and elevated oil prices revived the global interest in wind based systems (Wise, 2000). India has installed over 14 gigawatt (GW) of wind power systems since then and stands fifth in the world (~200 GW) today. In the wake of climatic changes and perishing stock of fossil fuels, wind energy is being widely revered as a clean energy option of 21st century that has high potential to offset carbon. It has been predicted that wind energy can produce 680 TWh of clean electricity globally in 2012, hence avoiding 408 million tons of CO2 emissions. This also supports the clean development mechanism (CDM) endorsed by Kyoto Protocol (GWEC, 2012, http://gwec.net/ wp-content/uploads/2012/06/IWEO_ 2011_ lo wres.pdf).
Nevertheless, major expanses of the world are still deemed as low wind potential areas, while energy demands are escalating. The overall wind potential in India is estimated to be 65 GW although there is enormous scope for up-scaling. Such low estimates are attributed to the wind resource assessment exercises that are performed with focus on large-scale wind turbines based on high winds. It has been argued that this trend towards large-scale wind technology 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, http://www.wind-energy-the-facts. 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, http://www.csiro.au/files/files/pis7.pdf). 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 and NASA-SSE along with terrain specific surface features (Khan et al., 2004). Wind energy potential of the Saharan desert in Algeria was assessed based on NASA-SSE data and prospected to power a wind based desalination plant to support agriculture in the arid region (Mahmoudi et al., 2009). The application of NASA-SSE data for wind power prospecting in two islands of Fiji was also demonstrated (Kumar and Prasad, 2010). These studies substantiate the advantage and increasing interest in synthesized data for regional wind resource assessment.
The present study explores wind resource potential in Himachal Pradesh, a federal Indian state in Western Himalayas based on synthesised wind data, validated with surface measurements. Seasonal wind profiles showing spatial variation of wind speeds are developed using geospatial techniques. The discussion includes the scope for deploying small-scale wind applications suitable for meeting the local energy requirements.
2 Study areas
2.1 Landscape and climate
Himachal Pradesh is located between 30.38°~33.21° North latitudes and 75.77°~79.07° East longitudes, covering a geographical area of 55 673 km2 with 12 districts (Government of Himachal Pradesh, http://hp planning.nic.in/statistics&data.htm). It has a complex terrain with elevation ranging from ~300 to 6 700 m as shown by the Digital Elevation Model (DEM) in Figure 1. Topography, climate, soil and vegetation clearly define the agro-climatic zones in the state. Parts of Una, Bilaspur, Hamirpur, Kangra, Solan and Sirmaur districts lower than 1 000 m above mean sea level represent the tropical zone. Certain segments of Solan, Sirmaur, Mandi, Chamba and Shimla districts located between 1 000~3 500 m have climate conditions varying from sub-tropical to wet-temperate. Lahaul Spiti, Kullu, Kinnaur and some parts of Shimla districts ranging between 3 500~6 700 m are part of the high elevation dry temperate, sub-alpine and alpine zones with sparse vegetation and rainfall.
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Figure 1 Digital elevation model (DEM) of Himachal Pradesh
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2.2 Energy and environment
The hill state of Himachal Pradesh represents one of the rich biodiversity zones adversely impacted by unplanned development. Field investigations reveal substantially high energy requirement due to the colder climatic conditions, particularly in high elevation zone (>3 500 m). People are largely dependent on forests (fuelwood) for meeting their heating (room and water) and cooking demands, although vegetation cover is sparse in high elevation zone (Ramachandra et al., 2012). This has resulted in decline of vegetation cover, fragmentation of forests and associated ecological imbalance in an ecologically fragile region such as the Himalayas. In recent times, there has been increase in fossil-fuel based energy consumption, with resultant pollution and glacial melting (Aggarwal and Chandel, 2010). This necessitates exploration of clean renewable energy as decentralised sources. Even so, marginality and negligence of these mountain regions in the past have led to scarcity of reliable data which hinders efficient resource assessment and planning (Bhagat et al., 2006).
3 Data, models and methods
3.1 Surface wind measurements
Long term wind characteristics in Himachal Pradesh were recorded at 13 meteorological stations (Figure 2) of the India Meteorological Department (IMD). Table 1 lists the IMD stations and periods of wind measurement exercises. Wind speed measurement heights varied from 1.7 m (Dalhousie), 5 m (Manali), 7 m (Dharmshala), 9.7 m (Bilaspur) to 26 m (Kyelong) and 26.5 m (Shimla).
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Out of 13, IMD provided surface wind data for 10 stations (Bilaspur, Sundernagar, Nahan, Chamba, Bhuntar, Dharmshala, Dalhousie, Manali, Simla CPRI and Shimla) recorded for different durations (Table 1). Wind speed at Mandi was obtained from a literature on wind climatology in India (Mani and Mooley, 1983). The measured data included: 1) synoptic hour values (local time 8:30 and 17:30); 2) daily averages for durations between synoptic hours and; 3) monthly averages (not available for Mandi) of wind speeds. Daily averages of wind speeds were obtained by averaging the mean for two 12 hour periods starting from 17:30 hrs, capturing the diurnal variations of the wind. Wind measurements were standardized to 10 m using power law equation (1) as per World Meteorological Organization (WMO) norm (Ramachandra et al., 1997).
V/V0 = (H/H0)α (1)
where V0 is the measured wind speed, V is the standardized wind speed, H0is the measured height, H is the desired height (10 m) and α is the power law index. Here α is a measure of roughness due to frictional and impact forces on the ground surface which varies according to terrain, time and seasons. The value of α calculated for most of the regions representing the Himalayan terrain are well above 0.40 based on long term observations and calculations (Mani and Mooley, 1983). In order to minimize extrapolation errors we considered the least value of 0.40 for Himachal Pradesh. The wind measurement heights in Himachal Pradesh were standardized using power law equation with α as 0.4.
Topography of Himachal Pradesh renders enormous variation to the micro-climate, wind speeds and direction, adding to complexity of wind resource assessment in the region. The available IMD surface wind data were characterized by large gaps and non-standard measurement heights. In addition, these stations were not representative of the diverse agroclimatic zones and particularly unavailable for the high elevation zone (> 3 500 m) of Himachal Pradesh (Figure 2). Capturing the wind regime of its complex terrain using these data cannot be a desirable option. Recently, IMD has deployed Automatic Weather Stations (AWS) at 22 locations in Himachal Pradesh (Figure 2) at 2 m heights above the ground (Automatic Weather Station, 2012, http://www.imdaws.com/). However, according to the communication from IMD, AWS based wind data were available merely for 3 stations (Bilaspur, Una and Udaipur) for the year 2011. Hence, we explored long term global wind datasets synthesised based on prudent models appropriate for the study area.
3.2.1. NASA SSE
3.2.2. NOAA-CIRES
NOAA-CIRES 20th Century Reanalysis Version 2 dataset provides estimates of global tropospheric and stratospheric variability since 1871 at six-hourly temporal resolution. These were derived based on surface and sea level pressure measurements. Monthly sea-surface temperatures and sea-ice distributions were considered as boundary conditions in an Ensemble Kalman Filter data assimilation with support of certain parameterizations and global numerical weather prediction (NWP) model (NOAA, 2012, http://www.esrl.noaa.gov/psd/data/20thC_Rean/). The NWP model generates numerical simulations of the global atmospheric state, which are reanalysed and stored. However, NWP data are generally used as input of models which generate low resolution grids so as to infer the near-surface wind field. This process, called downscaling, is performed using statistical and dynamical considerations (Aguera-Perez et al., 2012). Global wind speeds of 138 years (1871~2008) at 2°×2° spatial resolution were accessed at http://www.esrl.noaa.gov/psd/data/20thC_Rean/.
3.2.3 CRU
3.3. Wind profiling
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Figure 3 Synthesized as well as surface (IMD) wind speed locations for Himachal Pradesh
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4 Results and Discussions
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Figure 4 Annual average wind speed in Himachal Pradesh based on 1°×1°spatial resolution NASA-SSE data
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Figure 5 Annual average wind speed in Himachal Pradesh based on 2°×2° spatial resolution NOAA-CIRES reanalysis data
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Figure 6 Annual average wind speed in Himachal Pradesh based on 10’×10’ spatial resolution CRU data
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4.1.1 Validation
It is known that the density of vegetation and size of canopy cover reduces with increasing elevation (towards alpine zone) and facilitates higher wind flow. The surface measurements from IMD (though not representative of the entire state), validate the consistent increase in wind speeds with elevation as illustrated by the three synthesised data, viz. NASA-SSE, NOAA-CIRES and CRU (Figure 4~6).
Synthesised wind data were cross-compared using box-plots (Figure 7) to observe the pattern of monthly wind speed variations over the region. On comparison with NOAA-CIRES and CRU wind data, NASA-SSE values were observed to be exaggerated. Although, NOAA-CIRES data were more distributed due to their coarseness in spatial resolution, they showed similar monthly variations as CRU values, with a unimodal rise in wind speeds during monsoon season (June to September). Further, these data were validated with surface measurements of IMD. The RMSE (Root Mean Square Error) for NASA-SSE and NOAA-CIRES were 2.57 m/s and 1.92 m/s respectively. CRU data showed the least RMSE of 1.32 m/s on validation and hence scored over the rest as the most representative data for the region.
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Figure 7 Cross-comparison of synthesised monthly wind speed data
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4.1.2 Re-validation
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Figure 8 Correlation analysis for re-validation of CRU data within 10 km radius of IMD sites with surface data availability
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Table 2 Correlation coefficients and interpretation (SAMHSA, 2011, http://pathwayscourses.samhsa.gov/ eval201/eval201_ 4_ pg9. htm)
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The above findings reiterate that CRU wind speeds are the most representative among the three synthesised datasets collected. CRU model considers geographical influences and high resolution makes it prudent for meso-scale (10~100 km) seasonal wind profiling in the tough Himalayan terrain of Himachal Pradesh.
4.2 Seasonal wind profiling
Seasonal wind profiles for Himachal Pradesh based on CRU data were generated by Kriging interpolation method in a geospatial application. Figure 9 shows the seasonal as well as spatial variation of average wind speeds. Frequency of occurrences of regional wind speeds are represented by histograms. As observed in the annual CRU wind regime (Figure 6), the spatial variations of wind speeds in seasonal wind profiles (Figure 9) were explicitly influenced by elevation and resultant agro-climatic zones. Highest wind speeds in the range of 1~3.25 m/s were seen during Monsoon season (June to August) with high elevation zones of Lahual Spiti, Kinnaur, Kullu and Shimla districts showing above 2.5 m/s. Wind speeds declined during post-monsoon and winter seasons (October to February) and ranged as 0.75~2.25 m/s over the region, although high elevations zones showed above 2 m/s. During summer and pre-monsoon (March to May), wind speeds showed an increasing trend in the range of 1.25~3 m/s, high elevation zones having wind speeds appreciably above 2.5 m/s. Hence, the districts of Lahual Spiti, Kinnaur, Kullu and Shimla with annually consistent wind speeds above 2 m/s are suitable candidates for detailed wind exploration.
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Figure 9 Seasonal wind profiles with spatial variations and frequency of occurrence of wind speeds for Himachal Pradesh
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4.2.1 Validation of seasonal and regional variations in wind speed
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Figure 10 Monthly average wind speeds at IMD stations with data availability
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The IMD sites were mostly located in low and middle elevation zones ranging from tropical to wet-temperate (< 3 500 m) which experience wind speeds below 2 m/s according to wind profiles based on CRU data. None of the available IMD sites represent high elevation zone where higher wind speeds (> 2 m/s) were investigated. Most of the IMD sites (except Shimla) measured wind speeds below 10 m, and hence the assumption of standardized measurements for data (mast height) sparse stations could be an underestimate according to power law in Equation 1. Hence, the actual wind speeds in many of the data sparse sites may be higher at 10 m. AWS wind speed measurements installed at 22 stations spread across Himachal Pradesh (shown in Figure 2) with better spatial coverage would aid in better validation of these results apart from removal of uncertainties such as data gaps, etc. Availability of more number of reliable wind measurement stations and detailed analysis of terrain features could facilitate micro-scale (1~10 km) wind resource assessment in Himachal Pradesh.
4.3 Small-scale wind applications in Himachal Pradesh
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Figure11 Percentage occurrence of winds above 2 m/s and 4 m/s from hourly measurements projected to 30 m hub height
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With advancements in technology, small-scale wind turbines functional at moderately low wind speeds are technically feasible and economically viable (Cabello and Orza, 2010). Some of these are listed in Table 3. The Savonius rotor Vertical Axis Wind Turbine (VAWT) that can function in wind speeds as low as 1 m/s (Ayhan and Saglam, 2012) is of special interest in this region.
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Table 3 Available small-scale wind turbines (European Wind Energy Association, 2012, http://www. wind-energy-the-fa cts.org/)
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Wind pump for drawing water is an attractive small-scale wind technology for rural energy needs (Mathew et al., 2002). The agriculture intensive sub-tropical to wet-temperate zones of Himachal Pradesh could get benefited by wind pumps that function at low wind speeds. As seen in Nahan and Bhuntar, increased hub heights (30 m) could deliver prolonged winds above 2 m/s.
Reduction in wind speeds and duration could be compensated by hybridizing wind with available alternative resources. Our assessment of solar energy potential in Himachal Pradesh substantiates that it receives monthly average global insolation (incoming solar radiation) > 4 kWh/m2/day except for the winter months (December and January). Higher altitude alpine zone (> 3500 m) receives lower insolation values but higher wind speeds. This trends inverts in lower altitude tropical zone i.e higher insolation and lower wind speeds (Ramachandra et al., 2011; Ramachandra et al., 2012). Hence wind-solar hybrid systems could be considered for endured energy supply in the region. Small-scale wind turbines could also be used in conjunction with diesel generators especially in remote areas (Clausen and Wood, 1999), although not a clean option. Battery charging based on wind systems supplements the energy requirements during reduced wind speeds.
4.4. Constraints
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Figure 12 Turbulence intensity in Himachal Pradesh based on surface wind speed measurements
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