Methodlogy

5. Methodology

5.1. Solar energy

Stations where measurements of global solar radiation were available were used directly and for locations where the data was not available, indirect methods were used. They are as follows.

From extra-terrestrial radiation, allowing for its depletion by absorption and scattering by atmospheric gases, dusts, aerosols and clouds. This is theoretically based and requires some approximation of the absorbing and the scattering property of the atmosphere.
From other meteorological elements, such as duration of sunshine and cloudiness using regression technique. This method is empirical based, and the form usually used involves actual and potential hours of sunshine, which gives the regression constants for global and diffused solar radiation at the particular location or site.

The average monthly global solar radiation is calculated using Eq. (1). Based on the R2 value and the least value of standard error of the y estimate, empirical formula consisting of SH and mean temperature is the best relationship compared to others,

where Tm is the mean temperature and SH is the specific humidity
The resulting global solar radiation is further classified based on seasons as summer (February–May), monsoon (June–September) and winter (October–January). Thematic maps are generated using GIS to assess the variability in global solar radiation. These maps will help in identifying the potential sites for harnessing solar energy.

5.2. Wind energy

Wind energy potential is calculated based on the wind data (annual average wind speed). Annual average wind velocity data for 29 wind-monitoring stations in Karnataka were collected from the India Meteorological Department (IMD), Government of India, Pune.

To analyse variations across seasons, data was grouped seasonwise as summer (February–May), monsoon (June–September) and winter (October–January). Seasonwise wind velocity and standard deviation were computed for 29 wind-monitoring stations. GIS is used for mapping wind resources spatially and to quantify and analyse temporal changes. Based on these, GIS thematic layers were generated, which would help in assessing the variability. The map helps to identify the most and the least suitable potential areas for harnessing wind energy.

5.3. Hydroenergy

Districtwise distribution of SHPs and their capacity were collected from Karnataka Renewable Energy Development Limited (KREDL). These data were then implemented in GIS to obtain districtwise hydroenergy variation considering SHPs.

5.4. Bioenergy

Bioenergy potential assessment is based on compilation and computation of bioresource supply for the energy generation. Bioresource supply from agricultural residue, forest, horticulture residue, plantation and livestock dung are considered to assess the energy potential talukwise. The data required for this purpose were collected from the State agencies such as Agriculture, Horticulture, Forest and Veterinary departments. These values were implemented in GIS to generate talukwise bioenergy availability maps. The talukwise potential is evaluated using maps of administrative boundaries (taluk boundaries) and statistical data.

The theoretical potential is presented as a thematic map of the total amount of biomass available in each region. The information contained in such a map can be used to identify regions where extensive cultivation is located and a more precise evaluation of the potential is justified.

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