Spatial Analysis of Renewable Energy in Papua New Guinea through Remote Sensing and GIS
Affiliation(s)
1 Department of Surveying and Land Studies, PNG University of Technology, Lae, Papua New Guinea. 2 Department of Electrical and Communications Engineering, PNG University of Technology, Lae, Papua New Guinea.
1 Department of Surveying and Land Studies, PNG University of Technology, Lae, Papua New Guinea. 2 Department of Electrical and Communications Engineering, PNG University of Technology, Lae, Papua New Guinea.
ABSTRACT
Electrification and sustainable energy uses are increasing in Papua New Guinea (PNG) over the last few decades. The bulk of PNG’s population (85%) lives in isolated and dispersed villages in the rural areas. Most of these isolated and dispersed areas are still yet to be connected to an electricity supply. Papua New Guinea (PNG) is richly endowed with natural resources, but exploitation has been hampered by rugged terrain, land tenure issues, and the high cost of developing infrastructure. The study is focused on mapping of enriched renewable energy zones of the entire country. Different variables related to renewable, like surface albedo index, earth skin temperature, solar insolation incident, and wind speed are used for this purpose. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and kriging, are evaluated to interpolate all variables. Rating and weight sum overlay operation is applied to derive potential renewable energy zones in this equatorial country. Results show that potential renewable energy distribution is high in Papua New Guinea on the March and September equinoxes. Yearly average distribution of renewable energy source variables is significantly higher in most areas of Manus, New Ireland, North Solomon, West New Britain, Northern, Central and Milne Bay; a larger portion of East New Britain; the northern part of West and East Sepik, Central, Morobe and eastern part of Madang province. The potential renewable energy distribution data can help to establish sustainable energy production in the country.
Electrification and sustainable energy uses are increasing in Papua New Guinea (PNG) over the last few decades. The bulk of PNG’s population (85%) lives in isolated and dispersed villages in the rural areas. Most of these isolated and dispersed areas are still yet to be connected to an electricity supply. Papua New Guinea (PNG) is richly endowed with natural resources, but exploitation has been hampered by rugged terrain, land tenure issues, and the high cost of developing infrastructure. The study is focused on mapping of enriched renewable energy zones of the entire country. Different variables related to renewable, like surface albedo index, earth skin temperature, solar insolation incident, and wind speed are used for this purpose. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and kriging, are evaluated to interpolate all variables. Rating and weight sum overlay operation is applied to derive potential renewable energy zones in this equatorial country. Results show that potential renewable energy distribution is high in Papua New Guinea on the March and September equinoxes. Yearly average distribution of renewable energy source variables is significantly higher in most areas of Manus, New Ireland, North Solomon, West New Britain, Northern, Central and Milne Bay; a larger portion of East New Britain; the northern part of West and East Sepik, Central, Morobe and eastern part of Madang province. The potential renewable energy distribution data can help to establish sustainable energy production in the country.
Cite this paper
Samanta, S. and Aiau, S. (2015) Spatial Analysis of Renewable Energy in Papua New Guinea through Remote Sensing and GIS. International Journal of Geosciences, 6, 853-862. doi: 10.4236/ijg.2015.68069.
Samanta, S. and Aiau, S. (2015) Spatial Analysis of Renewable Energy in Papua New Guinea through Remote Sensing and GIS. International Journal of Geosciences, 6, 853-862. doi: 10.4236/ijg.2015.68069.
References
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[1] Omar, E., Haitham, A.R. and Frede, B. (2014) Renewable Energy Resources: Current Status, Future Prospects and Their Enabling Technology. Renewable and Sustainable Energy Reviews, 39, 748-764. http://dx.doi.org/10.1016/j.rser.2014.07.113 [2] REN21 (2014) Renewable Energy Policy Network for the 21st Century. Renewables 2014: Global Status Report, 13-25. ISBN 978-3-9815934-2-6m. [3] IEA (2012) International Energy Agency, Energy Technology Perspectives 2012. Archived from the Original on 6 July 2015. [4] APEC (2009) Asia-Pacific Economic Cooperation, Report on Energy Balance. [5] PPL (2009) Annual Report. Papua New Guinea Power Limited, Port Moresby. [6] APERC (Asia Pacific Energy Research Centre) (2009) APEC Energy Demand and Supply Outlook. 4th Edition, Institute of Energy Economics, Japan. APEC # 209-RE-01.6, ISBN 978-4-931482-42-5. [7] ESMAP (2013) Energy Sector Management Assistance Program, Renewable Energy Resource Mapping Initiative. ESMAP Knowledge Exchange Forum, The World Bank. [8] EWDA (2010) An Analysis of Wind Energy in the EU-25. European Wind Energy Association. http://www.ewea.org/fileadmin/ewea_documents/documents/publications/WETF/Facts_Summary.pdf [9] Philibert, C. (2011) Renewable Energy Technology, Solar Energy Perspectives. Organisation for Economic Co-operation and Development/International Energy Agency (OECD/IEA), Paris. ISBN 9264124578. [10] BPSRW (2014) BP Statistical Review of World Energy June 2014. http://www.bp.com/statisticalreview [11] Turcotte, D.L. and Schubert, G. (2002) Geodynamics. 2nd Edition, Cambridge University Press, Cambridge. http://dx.doi.org/10.1017/CBO9780511807442 [12] PREA (1992) Papua New Guinea Issues and Options in the Energy Sector, Pacific Island Series, Report #1. Vol. 8, World Bank, Washington DC. [13] Samanta, S., Pal, D. K., Lohar, D. and Pal, B. (2012) Interpolation of Climate Variable and Temperature Modeling. Theoretical and Applied Climatology, 107, 35-45. http://dx.doi.org/10.1007/s00704-011-0455-3 [14] Burrough, P.A. and McDonnell, R.A. (1998) Principles of Geographical Information Systems. Oxford University Press, New York. [15] Watson, D.F. and Philip, G.M. (1985) A Refinement of Inverse Distance Weighted Interpolation. Geo-Processing, 2, 315-327. [16] Eckstein, B.A. (1989) Evaluation of Spline and Weighted Average Interpolation Algorithms. Computers and Geoscience, 15, 79-94. http://dx.doi.org/10.1016/0098-3004(89)90056-3 [17] Hutchinson, M.F. and Gessler, P.E. (1994) Splines—More than Just a Smooth Interpolator. Geoderma, 62, 45-67. http://dx.doi.org/10.1016/0016-7061(94)90027-2 [18] Matheron, G. (1970) The Theory of Regionalized Variables and Its Applications. Ecole Nationale Supérieure des Mine, 5, 212. [19] Triantaphyllou, E. (2000) Multi-Criteria Decision Making: A Comparative Study. Kluwer Academic Publishers, Dordrecht, 320. http://dx.doi.org/10.1007/978-1-4757-3157-6 [20] Paul, G. (2013) 100 Percent Renewable Vision Building. Renewable Energy World, Berkshire. [21] Delucchi, M.A. and Jacobson, M.Z. (2011) Providing all Global Energy with Wind, Water, and Solar Power, Part II: Reliability, System and Transmission Costs, and Policies. Energy Policy, 39, 1170-1190. http://dx.doi.org/10.1016/j.enpol.2010.11.045 [22] John, W., et al. (2013) Post Carbon Pathways. University of Melbourne, Melbourne.