Enhancing offshore wind resource assessment with LIDAR-validated reanalysis datasets: A case study in Gujarat, India

Offshore wind project planning requires resource assessment to account for wind uncertainties. Wind data can be gathered through meteorological measurements, satellite observations, and reanalysis datasets but must be validated and corrected to ensure accuracy. The present study aims to validate long-term reanalysis datasets (1979-present) to find the most reliable reanalysis datasets for wind resource assessment. Four reanalysis datasets, such as EMD-ERA, ERA5, CFSR2, and MERRA2 have been considered. Further, validated these datasets with the help of the short-term (8 months) wind data recorded by LIDAR at an offshore location in Gujarat, India. The reanalysis datasets have been observed to underestimate the wind speed recordings. Moreover, ERA5 is the most reliable among the four considered reanalysis datasets, with an utmost correlation coefficient of 0.9329 with reference to LIDAR data. According to the ERA5 dataset, a conceptual wind farm comprising 100 units of 6 MW wind turbines with an overall capacity factor of 39.27% can generate a maximum power of 2.064 TWh.