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Assessment of wave energy potential and its harvesting approach along the Indian coast

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  • Sannasiraj, S.A.
  • Sundar, V.

Abstract

The present scenario of energy market is highly volatile due to large oscillation in the fossil fuel prices. During these periods, the high energy demand for the industries is being partially met through non-conventional energy sources such as wind and solar power. The large untapped energy potential in the Ocean is yet to be exploited due to many technological constraints. The recent decades have shown positive developments worldwide towards the ocean wave energy converters. In the present study, an improved wave energy potential estimate has been made. Based on various parameters such as physical site characteristics, environmental conditions and socio-economic regional state, the selection criteria have been suggested. This would form the basis for energy device selection for the decision makers.

Suggested Citation

  • Sannasiraj, S.A. & Sundar, V., 2016. "Assessment of wave energy potential and its harvesting approach along the Indian coast," Renewable Energy, Elsevier, vol. 99(C), pages 398-409.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:398-409
    DOI: 10.1016/j.renene.2016.07.017
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    References listed on IDEAS

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    1. Rao, T.V.S.Narasimha & Sundar, V., 1982. "Estimation of wave power potential along the Indian coastline," Energy, Elsevier, vol. 7(10), pages 839-845.
    2. Clément, Alain & McCullen, Pat & Falcão, António & Fiorentino, Antonio & Gardner, Fred & Hammarlund, Karin & Lemonis, George & Lewis, Tony & Nielsen, Kim & Petroncini, Simona & Pontes, M. -Teresa & Sc, 2002. "Wave energy in Europe: current status and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 6(5), pages 405-431, October.
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    6. Sánchez, Antonio Santos & Rodrigues, Diego Arruda & Fontes, Raony Maia & Martins, Márcio Fernandes & Kalid, Ricardo de Araújo & Torres, Ednildo Andrade, 2018. "Wave resource characterization through in-situ measurement followed by artificial neural networks' modeling," Renewable Energy, Elsevier, vol. 115(C), pages 1055-1066.
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