+++ Data underlying the paper: Introducing site selection flexibility to techno-economic onshore wind potential assessments: new method with application to Indonesia +++ Authors: Jannis Langer1, Michiel Zaaijer2, Jaco Quist1, Kornelis Blok1 1Delft University of Technology, Faculty of Technology, Policy and Management, Department of Engineering Systems and Services Jaffalaan 5 2628 BX Delft The Netherlands 2Delft University of Technology, Faculty of Aerospace Engineering Kluyverweg 1 2629 HS Delft The Netherlands Corresponding author: Jannis Langer Contact: j.k.a.langer@tudelft.nl Jaffalaan 5 2628 BX Delft The Netherlands +++ General Information +++ These datasets were used to report and discuss the onshore wind potential in Indonesia in the paper 'Introducing site selection flexibility to techno-economic onshore wind potential assessments: new method with application to Indonesia' ('the paper' for the remainder of this README). The dataset consists of the (1) ESRI shapefiles produced from the steps in Figure 1 of the paper, and a (2) table summarising the properties of the wind farm sites. +++ Data Description +++ ++ (1) ESRI shapefiles ++ This file contains the shapefiles of all onshore wind farm sites based on the methodology deployed in the paper. To see the shapefile, the .shp file must be imported to a GIS software, like QGIS or ArcGIS. ++ (2) .csv file with wind farm properties ++ This file contains the same site property information as the .dbf file of the (1) ESRI shapefile after step 5 in Figure 1 of the paper, and contains the results of the techno-economic analysis (electricity production and LCOE). Total_ID: Unique ID identifying each wind farm polygon after step 1 in Figure 1 of the paper. Total_Area: Area of the total wind farm polygon in km2. Island: Island (group) on which the wind farm is located. Province: Province in which the wind farm is located Sub_ID: Unique ID identifying each gridded wind fram polygon after step 2 in Figure 1 of the paper. Sub_Area: Area of the gridded wind farm polygon in km2. Sub_Sub_ID: Unique ID intentifying each finely subdivided polygon after step 5 in Figure 1 of the paper Sub_Sub_Area: Area of the finely subdivided polygon in km2. Land_Type: Land use of wind farm site. 1: Open Land, 2: Agriculture & Mining, 3: Forestry, 4: Nature Conservation Zones and Natural-Catastrophe-Prone Zones (Earthquakes and Landslides) Wind_Speed_Level: Wind speed at site rounded to next integer (e.g. 2.3 m/s belongs to level 2, 6.6 belongs to level 7) GWA_100m, GWA_50m: Average 100 m and 50 m wind speed from Global Wind Atlas across wind farm in m/s, obtained with Zonal Statistics tool in QGIS 3.16. ERA5_100m: Average 100 m wind speed from ERA5 wind profile closest to wind farm in m/s. Correction_Factor: Correction factor for bias correction of ERA5 data. It reflects the deviation of GWA_100m/ERA5_100m. Slope: Average slope in ° at the finely subdivided polygon, determined with the Zonal Statistics tool of GIS 3.18 Zurich. Elevation: Elevation in m at the finely subdivided polygon, determined with the Zonal Statistics tool of GIS 3.18 Zurich. ERA5_ID: Unique ID identifying the ERA5 point closest to a finely subdivided wind farm polygon. BPP_2018: As explained in the paper, electricity tariffs for renewable power producers are based on the Basic Costs of Power Provision, or BPP, in US¢(2018)/kWh. BPP are only assigned to polygons with a sub-sub-area of more than 0.15 km2, otherwise they are NaN. Road_500m, Road_250m: Sub_Area of gridded polygon in km2 if a buffer around roads of 250 m/ 500 m was deployed as site exclusion criteria. Subs_10km, Subs_100km: Sub_Sub_Area of finely subdivided polygon in km2 if a maximum proximity to the closest substation of 10 km/ 100 km was deployed. Urb_2000m, Urb_1000m: Sub_Sub_Area of finely subdivided polygon in km2 if a buffer around settlements of 1000 m/ 2000m was deployed as site exclusion criteria. elec_prod_sites_1-28: Annual net electricity production of the 28 studied wind turbines in MWh/year. LCOE_1-28: Levelized Cost of Electricity of 28 studied wind turbines in US¢(2021)/kWh. LCOE_med: Median levelized cost of electricity across the 28 studied wind turbines in US¢(2021)/kWh. LCOE_qlow: 25th percentile levelized cost of electricity across the 28 studied wind turbines in US¢(2021)/kWh. LCOE_qup: 75th percentile levelized cost of electricity across the 28 studied wind turbines in US¢(2021)/kWh.