Supplementary data to the paper “The role of repowering India’s ageing wind farms in achieving net-zero ambitions”. The Excel data file contains three data sheets: (1) “timeseries_1721”, (2) “timeseries_79_21”, and (3) wind_farms_data. (1)    Contains synthetic capacity factor values for India and seven Indian states for the period 2017-2021, reflecting changes to the wind fleet composition over that period (i.e., the data for Figure2 in the main article). (2)    Contains synthetic capacity factor values for India and seven Indian states for the period 1979-2021, reflecting wind capacity at the end of 2021 (i.e., the data for Section 3.5 in the main article). (3)    Contains data for the compiled dataset of Indian wind farms. Column headers are described below. commissioning_year: year wind farm stated operation. commissioning_month: month wind farm stated operation. commissioning_day: day wind farm stated operation. state: one of seven Indian states in which farm is located. total_capacity_mw: total wind farm nameplate capacity in Megawatts. N.B. many wind farms are listed as single or small collections of wind turbines. This reflects the underlying structure of the CEA dataset, which disaggregates wind farms by individual owner entities. number_of_turbines: number of turbines in wind farm. turbine_rating_kw: rating of turbine in Kilowatts. hubheight: turbine hub height in meters. rotor: rotor diameter in meters. lat: wind farm latitude. N.B. location data based on geocoding of nearest village settlement names using Google API implemented with GeoPy and so is considered approximate. lon: wind farm longitude pc: manufacturer power curve data (unsmoothed) of wind turbines at wind farm, with values representing % of rated power as a function of wind speed at hubheight. Power curve data values in 401 bins, corresponding to windspeeds between 0 and 40 meters per second at a precision of 0.1. 1. ABOUT THE DATASET -------------------- Title: Supplementary data to the paper “The role of repowering India’s ageing wind farms in achieving net-zero ambitions”. Authors: James Norman(1), Amanda C. Maycock(1), Alberto Troccoli(2) and Suraje Dessai(3) Organisation(s): 1 Institute for Climate and Atmospheric Science, School of Earth and Environment,University of Leeds, Leeds, United Kingdom 2 World Energy and MeteorologyCouncil, The Enterprise Centre, University of East Anglia, Norwich, United Kingdom 3 Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds, United Kingdom Rights-holder: Copyright 2023 University of Leeds Publication Year: 2023 Cite as: Norman, James and Maycock, C. Amanda and Troccoli, Alberto and Dessai, Suraje (2023). Supplementary data to the paper “The role of repowering India’s ageing wind farms in achieving net-zero ambitions”. University of Leeds [Dataset]: https://doi.org/10.5518/1418 Related publication: Norman, James and Maycock, C. Amanda and Troccoli, Alberto and Dessai, Suraje (2023). The role of repowering India’s ageing wind farms in achieving net-zero ambitions. (Submitted - Environmental Research Letters) Contact: James Norman (eejn@leeds.ac.uk) 2. TERMS OF USE --------------- Copyright 2023 University of Leeds. Unless otherwise stated, this dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/. 3. PROJECT AND FUNDING INFORMATION ---------------------------------- James Norman was supported by a PhD scholarship from the Natural Environment Research Council (Grant ref: NE/S007458/1) and additional grant funding provided by the World Energy and Meteorology Council (WEMC).