Analysis and visualizations

The output of the analysis are two csv files. One large file containing data for each geolocated cell and one smaller file with summarized data.

Output parameters

The following table displays the parameters included in the excel file that includes all the grid cells. The first 18 parameters come from the input csv file while the remaining stem from running the Python code.

Parameter Description
Country Name of the country
Pop Population in base year
X Longitude
Y Latitude
GHI Global Horizontal Irradiation (kWh/m2/year)
SolarRestriction Defines if an areas is restricted to solar PV deployment (1: restricted, 0: non restricted)
WindVel Wind speed (m/s)
TravelHours Distance to the nearest town (hours)
NightLights Nighttime light intensity (0-63)
Elevation Elevation from sea level (m)
Slope Ground surface slope gradient (degrees)
LandCover Type of land cover as defined by the source data
GridDistCurrent Distance from the existing electricity grid network (km)
GridDistPlan Distance from the planned electricity grid network (km)
SubstationDist Distance from the existing sub-stations (km)
RoadDist Distance from the existing and planned road network (km)
HydropowerDist Distance from identified hydropower stations (km)
Hydropower Closest hydropower technical potential identified
X_deg Longitude in degrees
Y_deg Latitude in degrees
RoadDistClassified Classification of the distance from road network based on a suitability index ranging from 1 to 5 (1 implying the lowest suitability)
SubstationDistClassified Classification of the distance from sub-stations based on a suitability index ranging from 1 to 5 (1 implying the lowest suitability)
LandCoverClassified Classification of the land cover based on a suitability index ranging from 1 to 5 (1 implying the lowest suitability)
ElevationClassified Classification of the elevation value based on a suitability index ranging from 1 to 5 (1 implying the lowest suitability)
SlopeClassified Classification of the slope gradient based on a suitability index ranging from 1 to 5 (1 implying the lowest suitability)
GridClassification Classification of the distance to the grid network based on a suitability index ranging from 1 to 5 (1 implying the lowest suitability)
GridPenalty Multiplier of the grid expansion cost based on a suitability index deriving from a weighted aggregation of the previous geospatial components
WindCF Identified capacity factor of wind turbines (%)
Pop2015Act Population in 2015
IsUrban Percentage of urban population
Pop2030 Population in 2030
Elec2015 Electrification status of population in 2015 (0 un-electrified - 1 electrified)
NewConnections Number of un-electrified population that is expected to get access to electricity by the end year
mg_hydro Levelized cost of electricity achieved by mini grid hydropower
mg_pv Levelized cost of electricity achieved by mini grid PV
mg_wind Levelized cost of electricity achieved by mini grid wind turbines
mg_diesel Levelized cost of electricity achieved by mini grid diesel
sa_diesel Levelized cost of electricity achieved by stand-alone diesel
sa_pv Levelized cost of electricity achieved by stand-alone PV
minimum_tech Off grid technology that provides the lowest levelized cost of electricity
minimum_tech_lcoe The lowest levelized cost of electricity selected between off grid technologies
Elec2030 Grid connection status in 2030 (1 implies grid connection - 0 implies off grid solution)
grid Levelized cost of electricity achieved by the grid (99 implies grid un-suitability)
MinGridDist Distance from the existing and planned grid network (km)
minimum_overall Technology that provides the lowest levelized cost of electricity
minimum_overall_lcoe The lowest levelized cost of electricity selected
minimum_overall_code Code of technology selected (1:grid, 2:stand-alone diesel, 3:stand-alone PV, 4:Mini-grid diesel . 5: Mini-grid PV, 6: Mini-grid Wind, 7:Mini-grid Hydro)
minimum_category Type of optimal supply type (Grid – Mini grid – Stand-alone)
NewCapacity Capacity requirement (kW)
InvestmentCost Investment requirement (USD)

Summaries output

The values in the summaries file provide summaries for the whole country/study area.

Variable Description Unit
Population The population served by each,technology in the end year. people
New Connections The number of newly electrified,population by each technology in the end year. people
Capacity The additional capacity required by,each technology to fully cover the demand in the end year. kW
Investment The investment required by each,technology to reach the electrification target in the end year. $
Technology mix Generation mix as calculated by the,OnSSET analysis.
LCoE Lowest LCoE achieved in each location,as calculated by the OnSSET analysis. $/kWh

Maps output

Creating maps in ArcGIS or other GIS software

Visualization of the model outputs are an important part of a geospatial electrification analysis. This allows for an easy display of electrification strategies and enhanced understanding of the results. The most flexible way of visualizing the results are to create the maps using a GIS program. In QGIS, go to Layer>Add Layer>Add Delimited TextLayer and select the result .csv file containing the scenario results to be displayed. Next, choose the column with the coordinates as well as the projection system to generate a map. For the X and Y Field choose X and Y for coordinates in km or X_deg and Y_deg for coordinates in degrees.

_images/vizstep1.PNG

After doing this you get a point layer in your layer panel. Right-click on the layer and go to Properties>Symbiology. Remove the border of all the circles.

_images/vizstep2.png

And then you can choose how to vizualise your data. Choose Categorized for discrete data and Graduated for continuous data. In column choose the data you want to vizualise and then click on classify.

_images/vizstep3.png _images/vizstep4.png

After these steps you can easily create your own maps and include north arrows, legends and scale bars by going to Project>New Print Layout