+++ 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. 