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Combined wind and wave resource assessment and energy extraction along the Indian coast

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  • P Patel, Ravi
  • Nagababu, Garlapati
  • Kachhwaha, Surendra Singh
  • V V Arun Kumar, Surisetty
  • M, Seemanth

Abstract

The present study analyses the combined offshore wind and wave resource potential along the Indian coast. The state-of-the-art ERA5 reanalysis datasets that provide wind fields at 100 m height have been used for the first time to assess wind resources along the Indian Coast. Further, wave parameters were generated using the WAVEWATCH III (WWIII) model. Long-term spatial air density was considered to find accurate wind power potential. The results reveal that the southern coast of India exhibits higher wind power density and moderate wave energy flux (up to 0.65 kW/m2 and 10 kW/m), respectively. This region has the lowest variability (coefficient of variation <1, skewness <1.5, and kurtosis <4) and less correlation (<0.4). A suitable hotspot is identified based on the collocation feasibility index and depth constraint after excluding conflicted regions. A comprehensive investigation of local wind and wave characteristics was performed at the hotspot. Further, the yearly and monthly cross-correlations between wind and wave at different time lags were analysed. Actual estimation of power generation is carried out with various combinations of wind turbine (SG8.0-167 DD) and wave energy converter (Oceantec). The outcome of the present study will help to initiate the development of joint offshore wind and wave energy farms in India.

Suggested Citation

  • P Patel, Ravi & Nagababu, Garlapati & Kachhwaha, Surendra Singh & V V Arun Kumar, Surisetty & M, Seemanth, 2022. "Combined wind and wave resource assessment and energy extraction along the Indian coast," Renewable Energy, Elsevier, vol. 195(C), pages 931-945.
    • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:931-945
    DOI: 10.1016/j.renene.2022.06.082
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    4. Shi, Xueli & Liang, Bingchen & Li, Shaowu & Zhao, Jianchun & Wang, Junhui & Wang, Zhenlu, 2024. "Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering," Energy, Elsevier, vol. 299(C).

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