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Solar Potential in the Himalayan Landscape

DOI: 10.5402/2012/203149

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Abstract:

Estimation of solar energy reaching the earth’s surface is essential for solar potential assessment. Solar radiation data based on satellites provide higher spatial and temporal coverage of regions compared to surface based measurements. Solar potential of the Indian hill state of Himachal Pradesh has been assessed using reliable satellite based global horizontal insolation (GHI) datasets validated based on its complex terrain. Solar maps representing regional and temporal resource availability in the state have been generated using geographical information systems (GIS). Spatial analyses show that the state receives annual average GHI above 4.5?kWh/m2/day and a total of 99530395 million kWh (or million units, MU). The regional availability of GHI in Himachal Pradesh is influenced by its eclectic topography, seasons as well as microclimate. The lower and middle elevation zone (<3500?m) with tropical to wet-temperate climate receives higher GHI (>5?kWh/m2/day) for a major part of the year compared to the higher elevation zone (>3500?m) with dry-temperate to alpine climate (4–4.5?kWh/m2/day). Results show that Himachal Pradesh receives an average insolation of 5.86 ± 1.02–5.99 ± 0.91?kWh/m2/day in the warm summer months; 5.69 ± 0.65–5.89 ± 0.65?kWh/m2/day in the wet monsoon months; 3.73 ± 0.91–3.94 ± 0.78?kWh/m2/day in the colder winter months. 1. Introduction Energy plays a pivotal role in the development of a region. However, energy shortages in recent times, the imminent energy crisis, and threat of climate change have focused the attention for a viable sustainable alternative through renewable sources of energy. Sun being the vital source of renewable energy manifested in different forms in the solar system; it is necessary to understand the mechanism of energy flow involved. The geometry of the earth-sun movements causes large spatial, diurnal, and seasonal variations in the amount of solar radiation received on earth. The 23.5° tilt of the earth’s rotational axis with respect to the plane of orbital revolution causes larger annual variations near the poles and smaller variations near the equator [1]. Due to the variations in the sun-earth distance, intercepted solar radiation fluctuates by ±3.3% around its mean value. The variations, due to sunspots, prominences and solar flares, can be neglected as they constitute small fraction compared to the total energy emitted by the sun. The average solar radiation falling on the earth’s atmosphere called the solar constant is estimated to be 1.36?kW/m2. The presence of clouds, suspended dust, gas molecules,

References

[1]  V. V. N. Kishore, Renewable Energy Engineering and Technology: A knowledge Compendium, TERI Press, New Delhi, India, 2008.
[2]  A. Mani, Handbook of Solar Radiation, Allied Publishers, New Delhi, India, 1981.
[3]  World Radiation Data Centre, St.Petersburg, August 2011, http://wrdc.mgo.rssi.ru/.
[4]  R. D. Varshita and M. K. Gupta, Modernisation of Radiation Network, Indian Meteorological Department, Pune, India, 2010.
[5]  NASA, Surface Meteorology and Solar Energy Release 6.0 Methodology, July 2010, http://eosweb.larc.nasa.gov/sse/documents/SSE6Methodology.pdf.
[6]  A. Ortega, R. Escobar, S. Colle, and S. L. de Abreu, “The state of solar energy resource assessment in Chile,” Renewable Energy, vol. 35, no. 11, pp. 2514–2524, 2010.
[7]  C. A. Gueymard and D. R. Myers, “Evaluation of conventional and high-performance routine solar radiation measurements for improved solar resource, climatological trends, and radiative modeling,” Solar Energy, vol. 83, no. 2, pp. 171–185, 2009.
[8]  R. Pitz-Paal, G. Norbert, H.-K. Carsten, and S. Christoph, How to get bankable meteodata ? DLR solar resource assessment, NREL 2007 parabolic trough technology workshop, Golden, Colorado, August 2010, http://www.nrel.gov/csp/troughnet/pdfs/2007/pitz_paal_dlr_solar_resource_assessment.pdf.
[9]  T. V. Ramachandra and D. K. Subramanian, “Potential and prospects of solar energy in Uttara Kannada, district of Karnataka state, India,” Energy Sources, vol. 19, no. 9, pp. 945–988, 1997.
[10]  K. Yang, T. Koike, and B. Ye, “Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets,” Agricultural and Forest Meteorology, vol. 137, no. 1-2, pp. 43–55, 2006.
[11]  X. Liu, X. Mei, Y. Li et al., “Evaluation of temperature-based global solar radiation models in China,” Agricultural and Forest Meteorology, vol. 149, no. 9, pp. 1433–1446, 2009.
[12]  D. L. Liu and B. J. Scott, “Estimation of solar radiation in Australia from rainfall and temperature observations,” Agricultural and Forest Meteorology, vol. 106, no. 1, pp. 41–59, 2001.
[13]  J. S. G. Ehnberg and M. H. J. Bollen, “Simulation of global solar radiation based on cloud observations,” Solar Energy, vol. 78, no. 2, pp. 157–162, 2005.
[14]  T. V. Ramachandra, “Solar energy potential assessment using GIS,” Energy Education Science and Technology, vol. 18, no. 2, pp. 101–114, 2007.
[15]  A. S?zen and E. Arcaklio?lu, “Solar potential in Turkey,” Applied Energy, vol. 80, no. 1, pp. 35–45, 2005.
[16]  C. M. Kishtawal, Meteorological Satellites, Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 2010, http://www.wamis.org/agm/pubs/agm8/Paper-4.pdf.
[17]  A. Q. Malik, A. Mufti, H. W. Hiser, N. T. Veziroglu, and L. Kazi, “Application of geostationary satellite data for determining solar radiations over Pakistan,” Renewable Energy, vol. 1, no. 3-4, pp. 455–461, 1991.
[18]  J. E. Hay, “Satellite based estimates of solar irradiance at the earth's surface-I. Modelling approaches,” Renewable Energy, vol. 3, no. 4-5, pp. 381–393, 1993.
[19]  C. Sorapipatana, “An assessment of solar energy potential in Kampuchea,” Renewable and Sustainable Energy Reviews, vol. 14, no. 8, pp. 2174–2178, 2010.
[20]  J. Polo, L. F. Zarzalejo, M. Cony et al., “Solar radiation estimations over India using Meteosat satellite images,” Solar Energy, vol. 85, no. 9, pp. 2395–2406, 2011.
[21]  S. Tanahashi, H. Kawamura, T. Matsuura, T. Takahashi, and H. Yusa, “Improved estimates of hourly insolation from GMS S-VISSR data,” Remote Sensing of Environment, vol. 74, no. 3, pp. 409–413, 2000.
[22]  E. Cogliani, P. Ricchiazzi, and A. Maccari, “Generation of operational maps of global solar irradiation on horizontal plan and of direct normal irradiation from Meteosat imagery by using SOLARMET,” Solar Energy, vol. 82, no. 6, pp. 556–562, 2008.
[23]  S. Janjai, P. Pankaew, and J. Laksanaboonsong, “A model for calculating hourly global solar radiation from satellite data in the tropics,” Applied Energy, vol. 86, no. 9, pp. 1450–1457, 2009.
[24]  T. V. Ramachandra, R. Jain, and G. Krishnadas, “Hotspots of solar potential in India,” Renewable and Sustainable Energy Reviews, vol. 15, no. 6, pp. 3178–3186, 2011.
[25]  R. Perez, R. Seals, and A. Zelenka, “Comparing satellite remote sensing and ground network measurements for the production of site/time specific irradiance data,” Solar Energy, vol. 60, no. 2, pp. 89–96, 1997.
[26]  P. Illera, A. Fernández, and A. Pérez, “A simple model for the calculation of global solar radiation using geostationary satellite data,” Atmospheric Research, vol. 39, no. 1–3, pp. 79–90, 1995.
[27]  Statistical Data of Himachal Pradesh upto 2009-10, Himachal Pradesh Planning Department, Govt. of Himachal Pradesh, August 2010, http://hpplanning.nic.in/Statistical data of Himachal Pradesh upto 2009-10.pdf.
[28]  SRB Data and Information, Atmospheric Science Data Center, NASA, August 2010, http://eosweb.larc.nasa.gov/PRODOCS/srb/table_srb.html.
[29]  R. T. Pinker and I. Laszlo, “Modeling surface solar irradiance for satellite applications on a global scale,” Journal of Applied Meteorology, vol. 31, no. 2, pp. 194–211, 1992.
[30]  Q. Fu, K. N. Liou, M. C. Cribb, T. P. Charlock, and A. Grossman, “Multiple scattering parameterization in thermal infrared radiative transfer,” Journal of the Atmospheric Sciences, vol. 54, no. 24, pp. 2799–2812, 1997.
[31]  S. K. Gupta, A. C. Wilber, D. P. Kratz, and P. W. Stackhouse Jr., “The langley parameterized shortwave algorithm (LPSA) for surface radiation budget studies,” NASA/TP-2001-211272, 2001.
[32]  S. K. Gupta, C. H. Whitlock, N. A. Ritchey, and A. C. Wilber, An Algorithm for Longwave Surface Radiation Budget for Total Skies, CERES ATBD Subsystem 4.6.3, August 2010, http://ceres.larc.nasa.gov/documents/ATBD/pdf/r2_2/ceres-atbd2.2-s4.6.3.pdf.
[33]  NASA/GEWEX Surface Radiation Budget Project, August 2010, http://gewex-srb.larc.nasa.gov/.
[34]  R. Perez, P. Ineichen, K. Moore et al., “A new operational model for satellite-derived irradiances: description and validation,” Solar Energy, vol. 73, no. 5, pp. 307–317, 2002.
[35]  National Renewable Energy Laboratory, Colorado, USA, September 2011, http://www.nrel.gov/international/docs/readme_india_solar_maps.txt.
[36]  Solar resource assessment methodology for Bhutan, National Renewable Energy Laboratory, Colorado, United states, March 2012, http://www.nrel.gov/international/pdfs/ra_bhutan_solar_methods_final.pdf.
[37]  A. Z. Kotarba, “Satellite-derived cloud climatology over high elevation areas based on circulation types: a 2007 analysis of the Tatra Mountains,” Physics and Chemistry of the Earth, vol. 35, no. 9–12, pp. 462–468, 2010.
[38]  Himachal Pradesh Finance Department, September 2011, http://himachal.nic.in/finance/ES_201011/EconomisSurveyEng1011.pdf.

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