The power curve for the GE 2.5 MW wind turbine was applied in this study to calculate hourly wind power outputs. The hub height of the GE 2.5 MW wind turbine is 100 m. Previous studies estimated wind speeds at hub height through interpolation using the oversimplified 1/7 coefficient that ignores the variation due to changes in surface roughness or atmospheric stability (39). We obtained wind speeds at 10 and 50 m from the NASA MERRA-2 dataset, a replacement for the MERRA dataset with more observational constraints (40). MERRA-2 provides hourly wind speeds at 10 and 50 m on a grid of 0.5° latitude by 0.625° longitude. Wind speeds from MERRA-2 were validated using four reanalysis datasets [National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research Reanalysis-1, NCEP/Department of Energy Reanalysis-2, ERA-Interim reanalysis, and MERRA reanalysis] and measurements of wind speeds at 10 and 100 m in India (section S1). Validations at both near-surface levels and 100 m displayed consistent declining trends. We used Eqs. 1 and 2 (10, 39, 41) to estimate the friction coefficient (α) and wind speeds at 100 m (v100)Embedded Image(1)Embedded Image(2)

Estimated hourly wind speeds at 100 m were substituted into the power curve for the GE 2.5 MW wind turbine to calculate hourly wind power outputs and hourly CFs. CF values are defined byEmbedded Image(3)where Preal denotes the real hourly wind power output and Prated refers to the rated power of the turbine. To eliminate areas judged unsuitable for deployment of wind power systems, such as regions that are forested, urban, or covered with water or ice, we used land cover information obtained from the NASA MODIS (Moderate Resolution Imaging Spectroradiometer) satellite MCD12C1 dataset (42). The SRTM 90m digital elevation database version 4.1 (43) was used to calculate terrain elevation and slopes for each grid. Grids characterized by slopes of more than 20% or by heights of more than 3000 m were excluded as inappropriate for deployment of wind power systems. We assumed that the turbines should be separated by approximately 9 by 9 rotor diameters (0.58 km2) to minimize turbine-turbine interference (44). The area for each wind reanalysis grid was divided by 0.58 km2 to calculate the number of turbines in each grid. Grids with CFs less than 0.15 were considered uneconomical and were excluded accordingly. PE generation from wind in India was calculated usingEmbedded Image(4)where ng denotes the number of grids in India judged as feasible for deployment of wind power systems, nh represents number of operational hours for each year, and Aj refers to the area for each grid (in square kilometers).

We note that the wind power potential derived here is based on a fixed separation distance for wind turbines, whereas actual wind turbine siting for specific wind farms would be optimized on the basis of the microlevel information on geographical topography and would yield a relatively higher source of electricity from the wind. Hence, we would argue that our results for the total wind power potential for India are relatively conservative. The interannual variation of available wind power, however, should be similar once the specific locations of the wind turbines are fixed. The main conclusions drawn here associated with the interannual variation and the long-term declining trend are not significantly influenced by this simplification.

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