Welcome to OnSSET’s documentation!
KTH dES - http://www.onsset.org
Electricity for all
In September 2015, the United Nations General Assembly adopted Agenda 2030, which comprises a set of 17 Sustainable Development Goals (SDGs) defined by 169 targets. “Ensuring access to affordable, reliable, sustainable and modern energy for all by 2030” is the seventh goal (SDG7). According to the Tracking SDG 7: The Energy Progress Report of 2020 approximately 789 million people lacked access to electricity in 2018, and many more rely on poor quality electricity services. The majority of those without access (85%) reside in rural areas. Access to electricity services is a prerequisite for sustainable development and a powerful factor in poverty alleviation and well-being.
Contents
Introduction to OnSSET
Universal access to electricity
The 2030 Agenda for Sustainable Development has set the goal of universal access to electricity by 2030 (SDG7). The challenge is significant. It involves reaching population with limited income, often living in sparsely populated areas, mostly in developing and least developed countries. The choice of which technology to use for increasing electricity access, depends on a number of parameters, from social to techno-economic including e.g. target level of energy access, local population density, distance to the national grid and local resource availability. These parameters are spatial in nature, making geospatial information very useful for their evaluation on both regional, national and sub-national scale.
What is OnSSET?
Over the past few years, the division of Energy Systems at KTH (formerly division of Energy Systems Analysis) has been embracing the advancements in the geospatial field, by developing together with partners an open source, geospatial electrification toolkit – the Open Source Spatial Electrification Tool (OnSSET). OnSSET is a GIS based tool developed to identify the least-cost electrification option(s) between seven alternative configurations:
Grid connection/extension
Mini grid systems (Solar PV, Wind Turbines, Diesel gensets, Small scale hydropower)
Stand-alone systems (Solar PV, Diesel gensets)
The results indicate the technology mix, capacity and investment requirements for achieving universal access in the modelled country, under a certain time period (usually until 2030). The findings can be presented in various formats such as interactive maps, graphs, images, tables etc.
Scope and Objective
OnSSET output, can be used to support policy development for electrification by taking into consideration specific guidelines and targets for each country. This, could help answer questions like:
What would it cost to provide universal electricity access in the country? How does this cost change based on the targeted consumption level per capita?
In case of limited resources, which customers/areas shall be prioritized? What key consumers should electrification activities focus on?
What part of population lives within 50 km from the existing grid infrastructure and what part in remote (last-mile) communities?
What is the most cost effective way to electrify populations in the last mile, what technologies and costs would that imply?
How do diesel generators compare with off grid renewables (solar PV, wind, hydro) in terms of overall economic performance? What are the implications of an imminent carbon pricing scheme?
What is the importance/impact of diesel price in the generation mix for electrification?
How does fuel cost for diesel compares with high upfront cost of renewables in terms of subsidy schemes (continuous vs one-time subsidy)?
To the current state, OnSSET has supported electrification efforts in many countries around the globe including Afghanistan, Nigeria, Ethiopia, Kenya, India, Tanzania, Madagascar and Benin as part of joint collaboration with the World Bank, the United Nations and SNV. These organizations, have warmly embraced the tool in their fight against energy poverty. In addition, OnSSET has featured in several peer reviewd publications including the World Energy Outlook in 2014, 2015, 2017 and 2019.
The OnSSET Model
OnSSET is a bottom-up medium to long term optimization model. Its principle is simple. Using population settlements together with different geospatial characteristics and socio- and tecno-economic data the tool tries to identify the least-cost supply option in order to reach universial electrification in each location.
The least-cost technology mix and investments required to fully electrify a country within a defined timeframe (e.g. 2030) can vary significantly depending on demand and supply characteristics, such as:
The level of electricity access to be achieved in a country (kWh/person/year).
The current status and plans of the national electricity grid along with its associated costs.
Local energy resource availability and off-grid power systems costs.
The following paragraphs describe in brief how an electrification anaysis with OnSSET works.
Electricity demand
The goal of OnSSET is clear, to identify the mix of technologies that will help to fully electrify the population of a country within a certain timeframe. Currently, OnSSET only accounts for residential electricity demand.
Geo-location of population and current electrification status
Using population density maps, OnSSET can identify where population is located whithin the study area. Depending on the number of people living in a settlement, OnSSET categorizes a settlement as either urban or rural. Other geospatial information such as the distance between a settlement and its closest electricity grid, its distance from closest transformer, population density and its brightness index at night (from satellite imagery), helps OnSSET identify the current electrification status per settlement. Each settlement is characterized as electrified or un-electrified.
Residential Electricity Demand
Once the electrified and un-electrified population has been spatially identified, OnSSET uses population growth rates per type of settlement (urban/rural) to estimate the projected population at the end year of the analysis (e.g. 2030). This is one of the two parameters we need to know to quantify and locate the future electricity demand. The second parameter is the target level of electricity; OnSSET adopts the consumption levels suggested by the Global Tracking Framework (2015).
Consumption levels start from 8 kWh/person/year, enough to support minor daily activities (few hours of lighting, phone charging, radio etc.) and reach up to 598 kWh/person/year, supporting the use of heavier or continuous appliances in a household like refrigerator, washing machine, oven etc. These values can easily be changed in the OnSSET model in order to better fit the study area of your choice. The combination of projected population and targeted consumption level, indicates the electricity demand for residential purposes per location.
Note, that OnSSET provides the option of determining the demand either through a top-down approach or a bottom-up one. In the top-down approach the user selects which tier the urban and rural settlements reach respectively and the demand in each settlement is determined as:
Electricity Demand = Population in 2030 X Selected Access Tier
In the bottom-up approach the user can use a combination of different GIS-datasets (e.g. GDP and poverty) in order to generate a custom demand estimate for each settlement. This approach requires the user to generate a demand layer and gives more dissagregated measure of deamnd than the urban-rural division in the top-down approach.
Electricity supply
OnSSET uses GIS to seize location specific information of power infrastructure. It also relies on GIS data in order to grasp the spatial and temporal fluctuation of energy resources. All these data, are later translated into techno-economic inputs and are very useful in assessing the performance of various technologies.
Energy resources assessment
Wind energy potential and capacity factors
Wind speed data extracted into a GIS environment are used to calculate wind turbine capacity factors per location. The latter is defined as the ratio of the yearly expected wind energy production to the energy production if the wind turbine were to operate at its rated power throughout the year. The capacity factor reflects the potential wind power at a given site and it can be used for comparing different sites before the installation of wind power plants. Additional documentation on wind power assessment is available here.
Solar energy potential
GIS data for Global Horizontal Irradiation (GHI - kWh/m^2/time) are used to indicate the available solar energy that Photovoltaic (PV) systems can utilize per location. Documentation on solar power assessment is available here.
Hydropower potential
A number of GIS datasets (runoff, accumulation, elevation, river network) have been utilized in a novel methodology developed by KTH dES in order to spatially identify potential sites for small scale hydropower deployment. Documentation on hydropower assessment together with a GIS based assessment tool is available here.
Transportation Cost for Diesel
Diesel is an important energy carrier, especially in remote areas of many developing countries and is therefore included in the OnSSET analysis. Transportation of diesel incurs costs, which may lead to high costs of electricity for isolated and low populated communities. OnSSET uses GIS datasets that indicate travel time and distance from main urban hubs, in order to calculate and assign transportation costs for diesel in each location. These costs are then included as fuel costs in the calculation of LCoE for diesel gensets. Documentation on diesel cost assessment is available here.
Electrification options
Over the last few decades, access to electricity in many parts of the world has been primarily achieved through the extension of the national electricity grid. Levereging on economies of scale, centralized grids offer electricity at low costs. Power grid expansion is however a capital intensive and lenthy process. In contrary, decentralized power system configurations i.e. mini-grids or stand-alone systems may be a better fit for electrification of remote, sparsely populated areas. Technological advances over the past decades along with innovative business models, have made such systems rather cost competitive. In addition, if based on local renewable sources they increase supply reliability while decreasing reliance on imported fuels.
OnSSET estimates and compares the performance of seven technology configurations that fall within three main categoroes: grid-extension, mini-grids and stand-alone systems.
Grid extension
Central grids can offer low generating costs. However, grid extension might not be economically or socially feasible if the purpose is to meet a relatively small electricity demand or reaching remotely located areas.
Mini-grids - Wind Turbines, Solar PVs, Mini/Small Hydro, Diesel generators
Mini-grids usually provide electricity from small power plants with generating capacity of a few MW. They tap locally available energy resources such as solar, hydro, wind, or can use commonly available fuels such as diesel. Overall, they can provide affordable electricity to rural and remote areas with low to medium electricity consumption habits. Cost-wise, if based on renewable sources, they usually have moderate to high upfront investment costs but small operational monetary costs and no fuel costs. On the other hand, diesel generator sets (gensets) are a mature technology with low upfront investment cost but subjected to operational costs depending on diesel pump price and transport costs fluctuations.
Stand-alone systems (SA) - Solar PVs, Diesel generators
Like mini-grids, these systems are usually based on local energy resources but the difference is that these can produce only few kWh per day, suitable to cover the electricity demand of a single household or a small business, but no more. Stand-alone systems do not require a T&D network nor construction investments. The capital cost of these systems is not high and depends mainly on size. Batteries, allowing for electricity when dark, may increase the upfront cost for PV systems.
Methodology Overview
Brief description of the electrification algorithm
The electrification algorithm procedure is based on two separate, yet complementary processes. On the one hand, a GIS analysis is required to obtain a settlement table referencing each settlement’s position –i.e., its x and y coordinates – and information related to demand, resource availability, infrastructure and economic activities. Night-time light datasets are used in combination with population density and electricity related infrastructure (transformers, transmission lines and distribution lines) in order to identify the presently electrified population. The initial electrification status is listed as either 1 (electrified) or 0 (non-electrified).
The algorithm calculates the cost of generating electricity at each cell for different electrification configurations based on the local specificities and cost related parameters. Depending on the electricity demand, transmission and distribution network requirements, energy resource availability etc. the LCoE for each of the seven technology configurations is calculated in each settlement. The LCoE of a specific technology configuration represents the final cost of electricity required for the overall system to break even over the project lifetime.
Note
The LCoE calculations for the mini-grid and standalone electrification options reflect the total system costs while the LCoE for the grid option is the sum of the average LCoE of the national grid plus the marginal LCoE of transmitting and distributing electricity from the national grid to the demand location.
Once the LCoEs for all the off-grid technology configurations have been calculated the grid extension algorithm is executed. For each cell electrified by the national grid the algorithm iterates through all non-electrified cells to test if the conditions for their connection to the electrified settlement are fulfilled. These conditions include: a) lower cost of generating, transmitting and distributing electricity as compared to the off-grid technologies and b) not causing the total additional MV grid length to exceed 50 km if it is connected.
If these conditions are fulfilled, the settlement status is set to electrified (by the national grid). At the same time, the algorithm stores the length of the additional MV lines that have been built thus far by the model to connect this new settlement. This is required to ensure all newly electrified cells comply with the 50 km limit for the length of MV lines. Further, this is also used to consider cost increases for each additional MV extension, due to the requirement to strengthen the previously built grid line. This process is repeated with the newly electrified cells until no additional cells are being electrified, and thus until all settlements to which the grid can be economically extended are reached. Settlements that are not connected to the grid will get access to electricity through mini grid or stand-alone systems. This decision is based on a cost comparison process where the off-grid technology which can meet the electricity demand at the lowest LCoE selected for each cell.
Penalty cost assignment to electricity grid expansion processess
The expansion of the transmission network to areas lacking access is a capital intensive process. The investment costs are influenced by several factors such as the capacity and the length of the lines as well as by the topology of the subjected area. In this analysis, a number of geospatial factors that affect the investment costs of the transmission network are identified and considered in order to assign an incremental capital cost in locations that indicate specific topological features. Particularly, investment cost is influenced by elevation, the road network, land cover type, slope gradient and distance from substations.
Software installation
Requirements
QGIS
OnSSET is a spatial electrification tool and as such highly relies on the usage of Geographic Information Systems (GIS). A GIS environment is therefore necessary for two main reasons:
Extract trivial characteristics for the electrification analysis from GIS layers and combine them all together in a format easy to read in python (a .csv file with all the attributes per population point).
Visualize the final results in maps.
Historically OnSSET has relied on ArcGIS, but recently the process has been moved to open source GIS environments. OnSSET in it’s current form relies on QGIS (together with in QGIS included tools from SAGA, GDAL and GRASS)
Note
In order to assure that the QGIS section of OnSSET functions correctly make sure to donwload the lastest long-term release of QGIS (version 3.10 as of the 7th of August 2020)
Download QGIS for free from the official QGIS website.
Python - Anaconda package
OnSSET is written in python, an open source programming language used widely in many applications. Python is a necessary requirement for the OnSSET tool to work. Programming in python usually relies on the usage of pre-defined functions that can be found in the so called modules. In order to work with OnSSET, certain modules need to be installed/updated. The easiest way to do so is by installing Anaconda, a package that contains a wide range of Python packages in one bundle. Anaconda includes all the Python packages required to run OnSSET successfully. Python itself can be downloaded and installed for free from the official Python website.
Python Interfaces - Integrated Development Environment (IDEs)
PyCharm
Integrated Development Environments are used in order to ease the programming process when multiple or long scripts are required. There are plenty of IDEs developed for Python, KTH dESA has been using PyCharm as the standard IDE to run OnSSET.
Jupyter notebook (via Anaconda)
Jupyter notebook is a console-based, interactive computing approach providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. Jupyter notebook is used for the online OnSSET interface, recommended for small analyses and exploring code and results.
GitHub
GitHub is a web-based Git repository hosting service. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for every project. OnSSET is an open source tool therefore the code behind it is open and freely accessible to any user. The code behind the OnSSET tool is called “PyOnsset” and is available in KTH dESA’s Github space. A GitHub account will allow you to propose changes, modifications and upgrades to the existing code. Access the repository on Github.
Software installation and setup
Download **Anaconda** here and install.
Please make sure that you download the version that is compatible with your operating system (Windows/MacOS/Linux - In case you run Windows open the Windows Control Panel, go to System and Security System and check e.g. Windows 32-bit or 64-bit).
Following the installation process make sure that you click on the option “Add Python X.X to PATH”. Also by choosing to customize the installation, you can specify the directory of your preference (suggest something convenient e.g. C:/Python35/..).
After the installation you can use the Anaconda command line (search for “Anaconda Prompt”) to run python. It should work by simply writing “python” and pressing enter, since the path has already been included in the system variables. In case this doesn’t work, you can either navigate to the specified directory and write “python” there, or add the directory to the PATH by editing the environment variables.
Download PyCharm here and install.
Please make sure that you download the version that is compatible with your operating system. Select the “Community”, open-source version.
Download the code from GitHub here.
Additional Info
GIS data acquisition
Geographic Information Systems
A Geographic Information System (GIS) is an integrated set of hardware and software tools, designed to capture, store, manipulate, analyse, manage, and digitally present spatial (or geographic) data and related attribute information. GIS can relate information from different sources, using two key index variables space (or location) and time. Common GIS data types (models) include:
Spatial Data: Describe the absolute and relative location of geographic features.
Vectors
Arcs (Polylines): Line segments forming individual linear features
Polygons: Areas enclosed by arcs
Points: Single coordinate pairs
Rasters
Grid-Cells: single column/row positions
Cell size: Resolution or else the accuracy of the data
Attribute data: Describe characteristics of the spatial features. These characteristics can be quantitative and/or qualitative in nature. Attribute data is often referred to as tabular data.
The selection of a particular data model, vector or raster, is dependent on the source and type of data, as well as the intended use of the data. Certain analytical procedures require raster data while others are better suited to vector data.
GIS data sources
EnergyData.info
Every day governments, private sector and development aid organizations collect data to inform, prepare and implement policies and investments. Yet, while elaborate reports are made public, the data underpinning the analysis remain locked in a computer out of reach. Because of this, the tremendous value they could bring to public and private actors in data-poor environments is too often lost.
Energydata.info is an open data platform launched recently by The World Bank Group and several partners, trying to change energy data paucity. It has been developed as a public good available to governments, development organizations, non-governmental organizations, academia, civil society and individuals to share data and analytics that can help achieving universal access to modern energy services. The database considers a variety of open, geospatial datasets of various context and granularity. KTH Division of Energy Systems (KTH-dES), formerly known as KTH division of Energy Systems Analysis (KTH-dESA), contributes on a contnuous basis by providing relevant datasets for electrification planning.
Indicative open libraries of GIS data
Over the past few years, KTH dES has been actively involved in the field of geospatial analysis. The following table presents a list of libraries and directories that provide access to open GIS data.
Source |
Type |
Link |
|---|---|---|
Penn |
World per region |
http://guides.library.upenn.edu/content.php?pid=324392&sid=2655131 |
MIT |
World per region |
|
EDEnextdata |
World per region |
|
Stanford |
World per region |
https://library.stanford.edu/research/stanford-geospatial-center/data |
GIS Lounge |
Finding GIS data |
|
dragons8mycat |
Different countries |
|
rtwilson |
Different types |
|
Planet OSM |
Different types |
|
Berkeley |
Different types |
|
Kings College |
Different types |
|
CSRC |
Different types |
|
Data Discovery Center |
Different types |
|
Spatial Hydrology |
Different types |
|
Africa Information Highway |
Different types |
|
The Humanitarian Data Exchange |
Different types |
Country specific databases
With geospatial analysis gaining momentun in many research areas, many countries have set up their own geo-databases in an effort to facilitate interdisciplinary research activities under a geospatial context. Here are few examples:
Country |
Source |
|---|---|
Bolivia |
|
Brazil |
http://www.ibge.gov.br/english/geociencias/default_prod.shtm#REC_NAT |
East Timor |
|
Malawi |
|
Namibia |
http://www.uni-koeln.de/sfb389/e/e1/download/atlas_namibia/main_namibia_atlas.html |
Nepal |
|
Russia |
GIS data in OnSSET
OnSSET is a GIS-based tool and therefore requires data in a geographical format. In the context of the power sector, necessary data includes those on current and planned infrastructure (electric grid networks, road networks, power plants, industry, public facilities), population characteristics (distribution, location), economic and industrial activity, and local renewable energy flows. The table below lists all layers required for an OnSSET analysis.
# |
Dataset |
Type |
Description |
|---|---|---|---|
1 |
Population density & distribution |
Raster |
Spatial identification and quantification of the current (base year) population. This dataset sets the basis of the ONSSET analysis as it is directly connected with the electricity demand and the assignment of energy access goals. |
2 |
Administrative boundaries |
Polygon |
Delineates the boundaries of the analysis. |
3 |
Existing HV network (Optional) |
Line shapefile |
Used to identify and spatially calibrate the currently electrified/non-electrified population. This is layer is optional. |
4 |
Power Substations (Optional) |
Point shapefile |
Current Substation infrastructure used to identify and spatially calibrate the currently electrified/non-electrified population. It is also used in order to specify grid extension suitability. This is layer is optional. |
5 |
Roads (Optional) |
Line shapefile |
Current Road infrastructure used to,identify and spatially calibrate the currently electrified/non-electrified population. It is also used in order to specify grid extension suitability. This is layer is optional. |
6 |
Planned HV network (Optional) |
Point shapefile |
Represents the future plans for the extension of the national electric grid. It also includes extension to current/future substations, power plants, mines and queries. This is layer is optional. |
7 |
Existing MV network (Optional) |
Line shapefile |
Used to identify and spatially calibrate the currently electrified/non-electrified population. This is layer is optional. |
8 |
Planned MV network (Optional) |
Point shapefile |
Represents the future plans for the extension of the national electric grid. This is layer is optional. |
9 |
Nighttime lights |
Raster |
Dataset used to,identify and spatially calibrate the currently electrified/non-electrified population. |
10 |
GHI |
Raster |
Provide information about the Global Horizontal Irradiation (kWh/m2/year) over an area. This is later used to identify the availability/suitability of Photovoltaic systems. |
11 |
Wind speed |
Raster |
Provide information about the wind velocity (m/sec) over an area. This is later used to identify the availability/suitability of wind power (using Capacity factors). |
12 |
Hydro power potential (Optional) |
Point shapefile |
Points showing potential mini/small hydropower potential. Dataset developed by KTH dESA including environmental, social and topological restrictions and provides power availability in each identified point. Other sources can be used but should also provide such information to reassure the proper model function. This is layer is optional. |
13 |
Travel time |
Raster |
Visualizes spatially the travel time required to reach from any individual cell to the closest town with population more than 50,000 people. |
14 |
Elevation Map |
Raster |
Filled DEM maps are use in a number of processes in the analysis (Energy potentials, restriction zones, grid extension suitability map etc.). |
15 |
Land Cover |
Raster |
Land cover maps are use in a number of processes in the analysis (Energy potentials, restriction zones, grid extension suitability map etc.). |
16 |
Service transformers (Optional) |
Point shapefile |
Current Transformer infrastructure used to identify and spatially calibrate the currently electrified/non-electrified population. This is layer is optional. |
17 |
Custom demand (Optional) |
Raster |
User defined electricity demand in the end year in each setltement. This is layer is optional. |
Note
Before a model can be built, one must acquire the layers of data outlined above.
You are recommended to use all the layers listed in the table above, but some of the are optional and can be omited (see table above)
More often than not, each layer must be acquired on its own. The final outcome is a .csv-file conveying all the information necessary to initiate an OnSSET electrification analysis.
GIS basic datasets
Administrative boundaries
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
shapefile |
Counties,provinces, departments, bibhag, bundeslander, daerah istimewa, fivondronana,,krong, landsvæðun, opština, sous-préfectures, counties & thana |
2011 |
GADM |
|
World,(& per country) |
shapefile |
Countries |
2011 |
DIVA-GIS |
|
Europe |
geodatabase/shapefile |
Countries, provinces |
2013 |
Eurostat |
http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units |
Population data
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
Various |
1 arc-second |
(depending on country) |
HDX |
|
World |
raster |
250 meter and 1 km |
1975, 1990, 2000, 2015 |
Global Human Settlement Layer |
|
Africa, Asia, America |
Raster |
100 m grid cells |
(depending on country) |
Worldpop |
|
World |
grid |
2.5 arc-minute grid cells |
90/95/00 |
SEDAC |
http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density/data-download |
World |
shapefile, raster (grid) |
2.5 arc-minute grid cells |
2000 |
UNEP |
|
Ghana, Haiti, Malawi, South Africa, Sri Lanka |
raster (grid) |
1 arc-second |
2015 |
CIESIN |
|
World |
Various |
Various |
2016 |
dhsprogram |
Transmission lines data
UK |
shapefile |
Power transmission lines, underground cables, stations etc. |
na |
National Grid |
http://www2.nationalgrid.com/uk/services/land-and-development/planning-authority/shape-files/ |
US |
raster |
100 m grid cells |
2015 |
ArcGIS online |
http://www.arcgis.com/home/item.html?id=918e6d9b1cc84d15ba13e911d18a0c5e |
World |
OSM potential |
points or polylines |
2015 |
OSM of various mirrors |
|
World |
From Vmap level 0 |
Power lines and utilities |
na |
Can be downloaded from: |
Power plants location data
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
shapefile (4 levels) |
Generators, substations,masts |
2009 |
Vmap level 0 |
Elevation
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
geoTIFF |
30 m spatial resolution |
2009 |
METI Japan, NASA |
|
World |
geoTIFF |
30 m posting, 1x1 degree tiles |
2009, 2011 |
METI Japan, NASA |
|
World |
.bil and/or .tif |
15 arcseconds/30arcseconds |
various |
ISCGM |
|
World |
GeoTIFF |
16 arcseconds/30arcseconds |
various |
NOOA |
|
World |
GeoTIFF |
17 arcseconds/30arcseconds |
various |
DGADV |
|
World + Arctic areas |
GeoTIFF |
30 arcseconds |
various |
WebGIS |
Travel time to major cities
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
ESRI grid |
30 arc sec |
2008 (data from 2000) |
Joint Research Center EU |
|
Africa (sub-Saharan) |
csv, ESRI ASCII raster, GeoTIFF |
5 arc sec |
2010 |
Harvest Choice |
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YKDWJD |
World |
Raster, GeoTIFF |
5 arc sec |
2015 |
Univeristy of Oxford |
Mining and Quarrying
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
USA |
Shapefile, csv, KML, KMZ |
Active mines and mineral plants in the US |
2003 |
USGS |
|
World |
Shapefile, dBase, HTML, Tab text,csv, Google earth |
points |
2012-2013 |
||
Solar
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
ESRI ASCII GRID, GeoTIFF |
250 m |
2017 |
SolarGIS |
Wind
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
GeoTIFF |
250m |
2018 |
Technological University of Denmark |
Land cover
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
HDF-EOS |
500 m |
2001-2018 |
NASA-MODIS |
|
World |
CI Land cover - raster |
300 m |
time series from 1992 to 2015 |
ESA |
|
World |
GeoTiff, Google earth, jpeg,png |
1-0.1 degrees |
2001-2010 |
NASA-NEO |
|
World |
Raster, csv |
0.0028 - 0.0083 degrees |
2000, 2005, 2010 |
ESA-ENVISAT |
|
World/Protected areas |
Shapefile, KML, csv |
na |
2014 |
Protected planet |
|
World |
various |
various |
2015 |
Global Land Cover Facility |
|
World |
Rasters for: Costal areas, Cultivated areas, Forests, Mountains, Islands, Inland waters etc. |
0.00833 degrees |
2000 |
SEDAC |
http://sedac.ciesin.columbia.edu/data/set/ma-ecosystems/data-download |
World |
Raster for croplands |
0.0833 degrees |
2000 |
SEDAC |
http://sedac.ciesin.columbia.edu/data/set/aglands-croplands-2000/data-download |
World |
Various Rasters on Land Use |
various |
1990-2010 |
Nelson Institute |
|
World |
Soil type |
various |
na |
Worldmap.Harvard |
|
World |
Various Rasters on Land Use |
various |
1980-2014 |
EarthStat |
The model classifies the land cover in order to calculate the grid extension penalties. The default classification values are based on the MODIS dataset found here, where the legend ranges from 1-17 with the values and corresponding land cover type can be seen below. If land cover data is retrieved from other data sources with different classification values they should be reclassified in GIS (using the Reclassify tool in ArcGIS or r.reclass in QGIS) to match those below. Alternatively changes can be made in the Python code instead. If this reclassification is not performed it may lead to an incorrect grid penalty factor or, if the highest values are above 17, an error message while running the code.
Value |
Label |
1 |
Evergreen Needleleaf forest |
2 |
Evergreen Broadleaf forest |
3 |
Deciduous Needleleaf forest |
4 |
Deciduous Broadleaf forest |
5 |
Mixed forest |
6 |
Closed shrublands |
7 |
Open shrublands |
8 |
Woody savannas |
9 |
Savannas |
10 |
Grasslands |
11 |
Permanent wetlands |
12 |
Croplands |
13 |
Urban and built-up |
14 |
Cropland/Natural vegetation mosaic |
15 |
Permanent snow and ice |
16 |
Barren |
17 |
Water bodies |
Others
Coverage |
Type |
Resolution |
Year |
Source |
Link |
|---|---|---|---|---|---|
World |
Coast Lines, oceans |
Physical vectors, ESRI shapefiles, GeoTIFF (1:10, 1:50 and 1:110 m) |
2015 |
Natural Earth |
|
World |
Climate data |
30 arc seconds and 2.5/5/10 arc minutes |
na |
WorldClim |
|
World/USA |
Climate change scenarios |
various |
na |
na |
|
World/Australia |
Water and Landscape Dynamics |
0.05 to 1 degrees |
1979-2012 |
Australian National University |
|
Open Street Map (OSM) - Osmosis |
osm.pbf |
depending on mirror source |
up to date |
NOAA |
|
Nighttime lights |
Raster file |
0.0042 degrees |
2012-2020 |
na |
|
Africa information Highway |
various |
vectors |
various |
AfDB |
|
World |
Cliamte data |
various |
various |
Oregon State University |
Methodology for Open Street Map data and Osmosis
Note
Open Street Map (OSM) is a collaborative project that intends to provide free and open access data used in mapping the world. This document aims at describing in brief the methodology used in order to obtain OSM data and transform them in compatible and useful information with the use of Osmosis and QGIS.
To begin with, bulk download of updated OSM data can be performed through the Planet OSM: http://planet.osm.org/.
The files can be downloaded as .xml and .pbf format. However, due to the large volume of data there are various mirrors/extracts that provide access to masked data for different regions of the planet. More information can be found here: http://wiki.openstreetmap.org/wiki/Planet.osm#Downloading. In previous cases Geofabrik.de where used successfully (https://download.geofabrik.de/africa.html).
From Geofabrik, data can be downloaded per region in .pbf format. In the latest version of QGIS it is possible to insert this data directly by simply dragging the file onto the QGIS window. However, since the files are usually very large it is recommended to transform the .pbf into a spatialite database.
To do this transformation open up the OSGeo shell following with your installation, navigate to the folder in which you have your .pbf file (by typing cd [folder path]) and enter the following line: ogr2ogr -f SQLite X.sqlite Y.pbf (Note! change X to the name you want to use for your spatialite database and Y to the name of your downloaded .pbf file)
Once This transformation is finished (it may take some time) drag the resulting .sqlite file into QGIS and work with it instead of the .pbf file.
OSM data provide access to a tremendous amount of information of various types. Feel free to explore the potential and share the results with an enthusiastic community.
GIS data preparation
Once all necessary layers have been succesfully acquired, the user would need to prepare the datasets for their input into the OnSSET model. This requires the creation of a .csv file. There are four steps that need to be undertaken to process the GIS data so that it can be used for an OnSSET analysis.
Step 1. Proper data types and coordinate system
In this first step the user would need to secure all the datasets. Before starting the analysis make sure that all datasets have the same coordiante system (preferably World Geodetic Datum 1984 (WGS84)) You can check the coordinate system of your layers by importing them into QGIS and then right-clicking on them and open the Properties window. In the Properties window go to the Information tab, here the coordinate system used is listed under CRS for both rasters and vectors.
Step 2. Layer projection
In this step the user would need to determine the projection system he/she wish to use. Projection systems always distort the datasets and the system chosen should be one that minimizes this distortion. Do not manually project the datasets yourself (the extraction plugin presented below does this for you) it is however good to have an idea of which system to use before starting to work with the datasets. Here follows a few important key aspects:
Projection is the systematic transformation of the latitude and longitude of a location into a pair of two dimensional coordinates or else the position of this location on a plane (flat) surface. A projection is necessary every time a map is created and all map projections distort the surface in some fashion.
Coordinate System: Simply put, it is a way of describing a spatial property relative to a center.
Datum: The center and orientation of the ellipsoid
Step 3. Generate population clusters
Once the previous steps are finished we can generate our population clusters. These clusters represents settlements and will set the base of your OnSSET analysis. To generate the clusters you need the three following dataset: Administratvie boundaries (polygon vector), Population (raster) and night-time lights (raster). In order to facilitate the cluster generation KTH-dES has developed QGIS based plugins available here
In the repository go to the Plugin folder and select the option that you wish to use. Currently there are four options to choose from:
Option 1. Plugin developed for QGIS 3.10 (the latest stable version) and works with HRSL
Option 2. Plugin developed for QGIS 3.4 and works with HRSL
Option 3. Plugin developed for QGIS 3.2 and works with HRSL
Option 4. Plugin developed for QGIS 3.2 and works with GHS
Once you have selected the option you wish to use, download the file named hrsl_clustering.zip. In order to install and use the plugin please refer to the instructions published in its repository.
Step 4. Generate the OnSSET input file
Once the clusters are generated, we can combine all layers together into a single table. The principle is simple. We will use the clusters from Step 3 in order to create a base table. Every row in this table represents a population settlement. Then, we will adhere one by one all the layers into this table so that every row (settlement) acquires its specific characteristics based on its location. One can perform the process manually by identifying the tools in the GIS environment of his/her preference.
In order to facilitate the process KTH-dES has prepared a set of QGIS plugins that can be used. The plugin is available in the Plugin folder in the following repository.
There are three options:
Option 1. Works for QGIS 3.10 (latest LTR)
Option 2. Works for QGIS 3.4
Option 3. Works for QGIS 3.2
Choose the one that suits you, download the corresponding .zip-file named gep_onsset and follow the instructions in its repository in order to intall and run the plugin.
GIS country file
The table below shows all the parameters that should be sampled and put into the csv file representing the study area.
Parameter |
Description |
|---|---|
Country |
Name of the country |
Nigthlights |
Maximum light intensity observed in cluster |
Pop |
Population of cluster |
id |
Id of cluster, important when generating maps |
GridCellArea |
Area of each cluster (km2) |
ElecPop |
Population that lives in areas with visible night-time lights |
WindVel |
Wind speed (m/s) |
GHI |
Global Horizontal Irradiation (kWh/m2/year) |
TravelHours |
Distance to the nearest town (hours) |
ResidentialDemandTierCustom |
Indicative residential electricity demand target |
Elevation |
Elevation from sea level (m) |
Slope |
Ground surface slope gradient (degrees) |
LandCover |
Type of land cover as defined by the source data |
CurrentHVLineDist |
Distance to the closest existing HV line (km) |
CurrentMVLineDist |
Distance to the closest existing MV line (km) |
PlannedHVLineDist |
Distance to the closest planned HV line (km) |
PlannedMVLineDist |
Distance to the closest planned MV line (km) |
TransformerDist |
Distance from closest existing transformers (km) |
SubstationDist |
Distance from the existing sub-stations (km) |
RoadDist |
Distance from the existing road network (km) |
HydropowerDist |
Distance from closest identified hydropower potential (km) |
Hydropower |
Closest hydropower technical potential identified |
HydropowerFID |
ID of the nearest hydropower potential |
X_deg |
Longitude |
Y_deg |
Latitude |
IsUrban |
All 0 after extraction, urban/rural split gets assigned in the algorithm |
PerCapitaDemand |
Indicative residential electricity demand target |
HealthDemand |
Indicative electricity demand for health |
EducationDemand |
Indicative electricity demand for educational facilities |
AgriDemand |
Indicative electricity demand for agricultural processes |
ElectrificationOrder |
Indicates order of electrification; retrieved by grid extension algorithm; default =0 |
Conflict |
Indicator of level of conflict (default =0; otherwise option 1-4) |
CommercialDemand |
Indicative electricity demand target for commercial activity |
ResidentialDemandTier1 |
Indicative residential electricity demand target equal to Tier 1 |
ResidentialDemandTier2 |
Indicative residential electricity demand target equal to Tier 2 |
ResidentialDemandTier3 |
Indicative residential electricity demand target equal to Tier 3 |
ResidentialDemandTier4 |
Indicative residential electricity demand target equal to Tier 4 |
ResidentialDemandTier5 |
Indicative residential electricity demand target equal to Tier 5 |
Note
It is very important that the columns in the csv-file are named exactly as they are namned in the Parameter-column in the table above.
Techno-economic and demographic parameter
Input model parameters
In order to run the OnSSET analysis certain input parameters have to be used. Some of these are specified directly in the Python code while others are specified in an excel specs file.
Python input parameters
The values for the parameters in the table below are already filled in the OnSSET Python files. However, some of these may need to be updated with new or country specific values.
Parameter |
Description |
Unit |
BASE_YEAR |
The base year of the analysis. Note that this parameter is highly related to the input GIS data, which should, if possible, be calibrated towards this year (e.g. the population distribution map should represent the base year values). |
|
END_YEAR |
The final year of the analysis. |
|
Scenario |
The input value here represents the annual electricity consumption per household that is expected to be achieved,by the end year. |
kWh/hh/year |
sa_diesel_capital_cost |
The capital cost (per capacity unit) of a stand- alone diesel generator. |
$/kW |
sa_pv_capital_cost |
The capital cost (per capacity unit) of a stand- alone PV module. |
$/kW |
mg_diesel_capital_cost |
The capital cost (per capacity unit) of a mini grid diesel generator. |
$/kW |
mg_pv_capital_cost |
The capital cost (per capacity unit) of a mini grid PV system. |
$/kW |
mg_wind_capital_cost |
The capital cost (per capacity unit) of a mini grid wind powered system. |
$/kW |
mg_hydro_capital_cost |
The capital cost (per capacity unit) of a mini grid hydropower system. |
$/kW |
existing_grid_cost_ratio |
Incremental cost increase for extension of the grid from an electrified settlement to an un-electrified one. Default value set at 10%. |
ratio |
discount_rate |
The discount rate applied to different technology configuration choices throughout the period of analysis |
ratio |
grid_cell_area |
The size of each grid cell used in the study area. This value typically ranges between 1 and 10 km2 |
km2 |
Discount rate
The discount rate is a rather important factor when doing modelling and planning for energy systems. This has to do with the fact that the discount rate is aimed at the variable cost. In other words a high discount rate tends to favour technologies with high variable costs (e.g. technologies that use non-renewable fuels), while lower discount rates favours technologies with high capital investment (e.g. renewables). This makes it a very good variable for running sensitivity analysis. For realistic values of the discount rate study the literature regarding your studyarea.
Off-grid technology costs
When calculating the total cost for the off-grid technologies (all the stand-alone and mini-grid systems) there are several different costs that need to be taken into account. The technology is a fixed cost that need to be taken into account, but the variable costs necessary for generating electricity also have to be taken into account (e.g. fuel costs). The cost of installing the system also need to be taken into account. The connection to the grid is however excluded and hence not taken into consideration.
Specs-file input parameters
The inputs in the table below should be entered in an .xlsx file namned CountrySpecs.xlsx (Replace Country with the name of the area studied)
Parameter | Description |
Unit |
|
Country |
Enter the name of the study area |
|
Pop2015 |
The population of the selected area in the base year. |
People |
UrbanRatio2015 |
The ratio of urban population in the selected area in the base year. |
Ratio |
Pop2030 |
The projected population |
People |
UrbanRatio2030 |
The ratio of urban population in the selected area in the end year. |
Ratio |
NumPeoplePerHHRural |
Number of people per household in rural areas |
People |
NumPeoplePerHHUrban |
Number of people per household in urban areas |
People |
DieselPriceLow |
The low price of diesel. Used in the calculation of the operational costs for diesel generators (in both mini grid and stand-alone systems). |
$/liter |
DieselPriceHigh |
The high price of diesel. Used in the calculation of the operational costs for diesel generators (in both mini grid and stand-alone systems). |
$/liter |
GridPrice |
The cost of which the national grid generates electricity. This value is based on the mix of technologies used in the country. |
$/kWh |
GridCapacityInvestmentCost |
The investment required per unit of additional capacity for the national grid. This is an average value based on,the mix of technologies used in the country. |
$/kWh |
GridLosses |
This value represents the country’s average technical losses on transmission and distribution. |
Ratio |
BaseToPeak |
This value represents the ratio between the base and peak load in the selected country. It is used for sizing the necessary capacity to be installed per settlement in order to cover the respective demand. |
Ratio |
ExistingGridCostRatio |
Incremental cost increase for extension of the grid from an electrified settlement to an un-electrified one. Default value set at 10%. |
Ratio |
MaxGridExtensionDist |
The input parameter sets the maximum distance for which the grid can be extended in order to electrify a settlement due to techno-ecoomic considerations. The default value in the model is 50km. |
km |
ElecActual |
The electrification rate in the selected area in the base year. What ratio of population is electrified. |
Ratio |
MinNightLights |
Night time light is an indicator retrieved by NASA’s satellite imageries and it represents light intensity over a geographical area. The value ranges from 0 to 63 with higher value representing higher light intensity over the night. The input parameter sets the minimum light intensity value under which the model will consider a settlement as electrified. |
[0-63] |
MaxGridDist |
The input parameter sets the maximum distance from the existing or planned grid network under which the model will consider a settlement as electrified. |
km |
MaxRoadDist |
The input parameter sets the maximum distance from the existing or planned road network under which the model will consider a settlement as electrified. |
km |
PopCutOffRoundOne |
These input parameters sets the minimum population value under which the model will consider a settlement as electrified. If the value at Round one is not satisfactory the program will move on to Round two (make sure that round two has a higher value than round one.) |
People |
PopCutOffRoundTwo |
||
UrbanCutOff |
This input parameter sets the minimum population value under which the model will consider a settlement urban |
People |
The variables MinNightLights, MaxGridDist, MaxRoadDist, PopCutOffRoundOne, PopCutOffRoundTwo and UrbanCutOff are not found in the literature but rather defined by the user. When running the code these values are altered to find a combination that satisfies that the cells are assigned electrified and urban status matching the values entered at ElecActual and UrbanRatio2015.
All the values with ratio as the unit should take a value between 0 and 1 in the excel file.
Diesel pump price
For the diesel price the best option is to find the diesel price directly from the literature regarding your studyarea. In cases for which this is not possible you should try to find the crude oil price in your country. 1.15 is an emperical value used in order to estimate the diesel price ($/liter) from the crude oil price($/liter). It is assumed that the price of diesel is 15% higher than the crude oil price. If you have difficulties finding the crude oil price in your area of interest you can refer to e.g. http://www.oecd-ilibrary.org/energy/world-energy-outlook_20725302.
Household size
The household size is an important parameter in the electrification planning analysis as it affects the connection costs per household. These are calculated based on:
the projected mean national household size values
the existing and projected national, urban and rural populations
the urban to rural household size ratio given in demographics and health country surveys.
Note
It is very important that the columns in the Specs-file are named exactly as they are namned in the Parameter-column in the table above.
Running the analysis
Setting up PyCharm - Afghanistan Demo
Download the code from GitHub here.
Select “Download ZIP” from the “Clone or Download” tab and save the zipped folder on your Desktop. Unzip it and open the “Afghanistan_PyOnSSET” folder. If not already there, create a folder and name it “run”. This folder will contain the final output with the results.
Download the “Afghanistan.csv” dataset from here (to be updated) and save it in “~/PyOnSSET-master/Afghanistan_PyOnSSET/db”.
Once you have successfully completed all the previous steps, open PyCharm or any other Python IDE framework. Select “Open” and then select “~/Desktop/PyOnSSET-master/Afghanistan_PyOnSSET” as your working directory as shown below:
Go to File->*Default Settings*->*Project Interpreter* and from the drop-down list select the Anaconda directory which we installed in the previous steps. Once all the packages load, click Apply and OK.
Once you have set your working directory, select “pyonsset”, and from the drop-down menu double-click on a) onsset.py and b) runner.py in that order, in order to open them.
Now that you have opened “onsset.py”, right-click on its tab and click on “Create/Select ‘onsset’…”.
Note
In the configuration window, make sure that the Anaconda interpreter is selected in the “Python interpreter” option before you click OK.
You are now ready to run “onsset.py” by clicking the Run button at the upper-right corner of your window.
Repeat the same steps explained above for “runner.py” only if you get a “Process finished with exit code 0” message in the built-in console. Otherwise, move to step 7 before you run “runner.py”.
After running a python script, the output is shown in the console built in PyCharm.
If you get a “Process finished with exit code 0” message, that means that everything worked OK.
Otherwise, the console shows messages with errors or warnings. In case you get a “Module Not Found Error” message, that means that a module or package is missing.
Installing/updating modules and packages
Using Anaconda
Search for “Anaconda Prompt” in your Windows Search. In the command line, type conda install <package name> and the installation/update will go through automatically. (Note: In the Proceed ([y]/[n])? prompt, type “y”).
e.g. installation of module “pyproj”
pip
pip is a package management system used to install and manage software packages written in Python. Search for “Command Prompt” in your Windows Search. Using the command line, navigate to your Python directory and then type “pip” and enter. This will show you all the possible choices you have within pip.
In order to update pip itself you can just type
pip install --upgrade pip.
In order to install any additional module required you can type
pip install <package name>.e.g. installation of module “matplotlib”
pip install matplotlib
Directly in PyCharm
Go to the project interpreter (File>Settings>Project Interpreter). Click on the green plus-sign in the upper right corner.
Search for the required package and click Install Package.
Running the code
There are two Python codes that are required to run the analysis; onsset.py and runner.py.
onsset.py defines most of the functions and calculations behind the analysis. Running this code however does not perform the analysis. runner.py is the code that actually executes the analysis. When running this code the user will be propmpted to enter several inputs, explained below, and runner.py will then retrieve the functions from onsset.py.
Note
- Please make sure to:
Not edit or modify in any way your input/output .csv files unless required.
Close any open .csv files before running the programs.
Two changes in the Python code may be required to be able to run the analysis.
Line 8 in runner.py may be changed from from pyonsset.onsset import * to from onsset import * if runner.py and
onsset.py are saved in the same folder.
In runner.py line 11, change directory from db to the one where the .csv file containing the prepared GIS data and the
specs file are stored. The line should then look like e.g. os.chdir('C:/Desktop/Afghanistan_PyOnSSET/db').
OnSSET is then ready to be executed. Press run and enter the specified information when prompted. The runner will appear at the bottom of the window and look like:
First the program will ask for the name of the specs file. Enter the name of the specs file and press enter, and remember to include the file extension. The specs
file should be an excel file, and the entry should therefore be e.g. Specs.xlsx.
Secondly the countries of the study are required. Enter the name of the country and press enter. If more than one country
are included in the study enter the names of all countries separated by a blank space (Example: Afghanistan Pakistan)
Note
Python is sensitive to upper and lower case letters. Always make sure that names of files, directories and countries are matching exactly when running the code and entering information.
The name of the specs file and the country names need to be entered every time. The rest of thecode has been divided into
three parts with different functionalities. Next you will be asked to 'Enter 1 to split, 2 to prep, 3 to run a scenario: '.
The function of the three options are to:
1. Split a csv file containing GIS data for multiple countries into csv files that each has the data for one of the countries. If the original .csv file is for one country only this step can be skipped.
2. Calculate some initial data needed for the electrification analysis, such as projected population, wind capacity factors etc. This step needs to be run only once to use OnSSET for one area.
3. Perform the electrification analysis. Once the previous steps have been run the third part can be run multiple times to examine scenarios with e.g. different electricity access targets or fuel costs.
Step 1: Split
When prompted, enter the name of the file containing data for multiple countries and press enter. Example: Countries.csv.
Again remember to include the file extension.
Next enter the directory where the new files should be saved. This can be the same directory as the file containing all the countries
or a new one with separated countries only e.g. C:/Desktop/Afghanistan_PyOnSSET/db/countryfiles. A new directory is recommended
to prevent overwriting of the original .csv file if it is named after a country.
OnSSET then creates new .csv files with data for each country. The files will be stored with the name of the country followed by the .csv extension, e.g. Afghanistan.csv.
Step 2: Prep
The preparation step starts by prompting the user to enter the directory where the separated country files are saved.
If selection 1 was executed the entry should be the one specified for output in selection 1 (C:/Desktop/Afghanistan_PyOnSSET/db/countryfiles),
otherwise it is the directory where the original .csv file is stored (e.g. C:/Desktop/Afghanistan_PyOnSSET/db).
If selection 1 has not been executed the csv file must be named country.csv (e.g. Afghanistan.csv) or OnSSET will not be able
to find it.
For each country chosen initially the prepping will be executed and the .csv file with GIS data will be overwritten in the same folder with a new file with the same name (e.g. Afghanistan.csv) containing additional information. The specsfile is overwritten as well with updated data for MaxGridDist, MinNightLights and MaxRoadDist as well as ElecModelled which is the calculated value for the electrified population in the base year. After running step 2, open the specs file to check if ElecModelled is sufficiently close to ElecActual. If not, change tha values of MaxGridDist, MinNightLights, MaxRoadDist, PopCutOffRoundOne and/or PopCutOffRoundTwo and run step 2 again until ElecModelled is sufficiently close to ElecActual.
Step 3: Run scenario(s) In the third selection the electrification scenarios are run. This part of the code need to be run once for every scenario. The user is first prompted to specify where the prepped files from selection 2 are saved. Enter the same as was entered in selection 2. Secondly the user is prompted to enter where the result files should be saved. The result files will save as Scenario_Tier_Dieselchoice.csv and Scenario_Tier_Dieselchoice.csv (Example: Afghanistan_1_High.csv for a Tier 1 analysis with high diesel price).
The following step is to enter the electricity target simply by typing the tier number between 1-5 and pressing enter for urban and rural areas respectively. The annual household electricity consumption is displayed in the terminal:
Next step is to choose between the high and low diesel price from the specs file. For the question Use high diesel value? <y/n> ``
enter ``y to use the high diesel cost or n to use the low diesel cost.
The final input required before running the analysis is if a result file should be created for each country in the analysis
or if a combined result file should be created: Combine countries into a single file? <y/n>. Enter y to combine or
n to generate separate files. Following this step the electrification analysis is executed.
Note
Sometimes it may be of interest to run scenarios with the same energy tier and diesel price while changing other variables such as the discount rate. In a case with two runs with the same energy tier and diesel price the first result files will be overwritten. Remember to move or rename them before initiating the second run.
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.
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.
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.
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
Online interfaces & Applications
For those who would like to just take a taste of an OnSSET electrification analysis, KTH dESA has collaborated international organizations in order to develop interactive platforms that allow a quick display of “pre-cooked” electrification results for a number of countries.
United Nations app
The online interface Universal Electrification Access is part of the modelling tools for sustainable development and is based on OnSSET. This interface is not a model but it provides the user easy access to the methodology behind OnSSET, the datasets used and key results -with regards to electrification planning - obtained for a set of 30 scenarios and model runs for 54 developing countries in Sub-Saharan Africa and Latin America.
World Bank app
A Web-based application developed by the World Bank, Energy Sector Management Assistance Program (ESMAP) and KTH Royal Institute of Technology in Stockholm, shows the dynamic of an OnSSET based electrification analysis. The tool considers a variety of open, geospatial data to determine the least-cost options for bringing electricity to areas as granular as 1 square km in size. So far three countries are included – Tanzania, Zambia, and Nigeria – and will be adapted for more.
References
Since its development, OnSSET has been employed for a high number of studies presented at international conferences and published on peer reviewed journals. Here you find a list of the publications on OnSSET, which is going to be continuously updated.
Publications
A GIS-based approach for electrification planning—A case study on Nigeria
The benefits of geospatial planning in energy access – A case study on Ethiopia
A cost comparison of technology approaches for improving access to electricity services
A climate, land-use, energy and water nexus assessment of Bolivia
UN Sustainable development goals from a Climate Land Energy and Water perspective for Kenya
Lighting up the World at NASA-ESA International Workshop on Environment and Alternative Energy
Open Geospatial Data for Energy Access
Sustainable Development Goals: The EU at the global partnership forefront
Taming a Wicked Problem: Energy Access Planning from an Energy-Poor Perspective
Lighting up the World at European Geosciences Union General Assembly (to be updated)
Contribution
Εnergy specialists
In the policymaking arena, electrification planning is often captured by private consultants’ analytical infrastructure. The open source nature and the modular structure of the developed tool allow users to build and modify the code according to their requirements and sources and to develop rapid cost effective electrification plans with minimal resource and computational requirements as compared to existing electrification planning tools that are time-, data- and resource- intensive.
Education
OnSSET can be used by graduate students to enhance its functionalities and develop new applications in order to help address energy access issues. Enabling graduate students to build and iteratively develop formal energy planning models will impart this knowledge base to very wide range of energy market roles and positions.
Community
OnSSET community welcomes professionals and experts from different levels: decision makers, policy officers, energy planners, developers of new model functionalities, programmers. All your valuable comments and contributions are welcomed here.
Partners
Contact
Ask any questions on OnSSET forum.
You will need just a few clicks to sign up to the forum. Find the link at the top-right corner of the Optimus.community page.
Contact us:
E-mail: media@desa.kth.se (tbu) or meet the team here.
Feedback
Please post all comments and feedback you may have regarding this manual here..
Funding
OnSSET is a non-profit project. We rely on external funding and welcome partnerships. In this regard please contact us at seap@desa.kth.se.
Admininstration
dES KTH
Postal Address:
KTH - School of Industrial Engineering and Management
division of Energy Systems
Brinellvägen 68
SE-100 44 STOCKHOLM
FAQ
[Q] What prior knowledge is required for someone to use OnSSET?
[A] One should be familiar with the basic principles of energy planning and energy economics. Experience in working with Geographic Information Systems is also essential in order to build, analyse and visualize spatial datasets. Python skills are necessary if one would like to modify the code.
[Q] What type of renewable sources are used in an OnSSET model? Is it possible to add other sources (biomass etc.)?
[A] Currently, solar photovoltaics, wind turbines and mini and small hydropower plants are considered as off grid renewable energy technologies. The inclusion of bioenergy and hybrid technologies constitutes an ongoing addition.
[Q] How long does it take to complete an OnSSET electrification analysis for one country?
[A] This depends on the size of the country, on the resolution of the analysis, on the country specific characteristics of the grid infrastructure and on the electricity consumption level scenarios. Indicatively, to run Nigeria (area: 923,768 km²), at 1 km resolution and the highest electricity consumption it takes ca. 4.5 hours (assuming that the necessary GIS layers are developed beforehand).
License
MIT License
Copyright (c) 2020 Andreas Sahlberg, Alexandros Korkovelos, Babak Khavari, Oluchi Monwe, Dimitrios Mentis, Christopher Arderne
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
onsset
onsset package
Submodules
onsset.gui_runner module
onsset.onsset module
- class onsset.onsset.SettlementProcessor(path)[source]
Bases:
objectProcesses the DataFrame and adds all the columns to determine the cheapest option and the final costs and summaries
- calc_summaries(df_summary, sumtechs, tech_codes, year)[source]
The next section calculates the summaries for technology split, capacity added and total investment cost
- calculate_investments_and_capacity(sa_diesel_investment, sa_diesel_capacity, sa_pv_investment, sa_pv_capacity, mg_diesel_investment, mg_diesel_capacity, mg_pv_investment, mg_pv_capacity, mg_wind_investment, mg_wind_capacity, mg_hydro_investment, mg_hydro_capacity, grid_investment, grid_capacity, year)[source]
- calculate_new_connections(year, time_step, start_year)[source]
this method defines new connections for grid related purposes
- Parameters:
year (int) –
time_step (int) –
start_year (int) –
- calculate_off_grid_lcoes(mg_hydro_calc, mg_wind_calc, mg_pv_calc, sa_pv_calc, mg_diesel_calc, sa_diesel_calc, year, end_year, time_step, techs, tech_codes, diesel_techs=0)[source]
Calculate the LCOEs for all off-grid technologies
- calculate_total_demand_per_settlement(year)[source]
this method calculates total demand for each settlement per year
- Parameters:
year (int) –
- calibrate_current_pop_and_urban(pop_actual, urban_current)[source]
The function calibrates population values and urban/rural split (as estimated from GIS layers) based on actual values provided by the user for the start year.
- calibrate_elec_current(elec_actual, elec_actual_urban, elec_actual_rural, start_year, min_night_lights=0, min_pop=50, max_transformer_dist=2, max_mv_dist=2, max_hv_dist=5, buffer=True)[source]
Calibrate the current electrification status
- choose_minimum_off_grid_tech(year, mg_hydro_calc, techs, tech_codes)[source]
Choose minimum LCOE off-grid technology
First step determines the off-grid technology with minimum LCOE Second step determines the value (number) of the selected minimum off-grid technology
- Parameters:
year (int) –
mg_hydro_calc (dict) –
- static classify_elevation(elevation)[source]
Classify the elevation distance according to bins and labels
- Parameters:
elevation (list) –
- static classify_land_cover(column)[source]
this is a different method employed to classify land cover and create new columns with the classification
- Parameters:
column (series) –
Notes
0, 11 = 1 6, 8 = 2 1, 3, 5, 12, 13, 15 = 3 2, 4 = 4 7, 9, 10, 14, 16 = 5
- static classify_road_distance(road_distance)[source]
Classify the road distance according to bins and labels
- Parameters:
road_distance (list) –
- static classify_slope(slope)[source]
Classify the slope according to bins and labels
- Parameters:
slope (list) –
- static classify_substation_distance(substation_distance)[source]
Classify the substation distance according to bins and labels
- Parameters:
substation_distance (list) –
- closest_electrified_settlement(new_electrified, unelectrified, cell_path_real, grid_penalty_ratio, elecorder)[source]
- compute_diesel_cost(dataframe: DataFrame, sa_diesel_cost: Dict, mg_diesel_cost: Dict, year: int)[source]
Calculate diesel fuel cost
- Parameters:
dataframe (pandas.DataFrame) –
sa_diesel_cost (Dict) –
mg_diesel_cost (Dict) –
year (int) –
- Return type:
pandas.DataFrame
- diesel_cost_columns(sa_diesel_cost: Dict[str, float], mg_diesel_cost: Dict[str, float], year: int)[source]
Calculate diesel fuel cost based on TravelHours column
- Parameters:
sa_diesel_cost (Dict) –
mg_diesel_cost (Dict) –
year (int) –
- elec_extension(grid_calc, max_dist, year, start_year, end_year, time_step, grid_capacity_limit, grid_connect_limit, new_investment, new_capacity, auto_intensification=0, prioritization=0)[source]
Iterate through all electrified settlements and find which settlements can be economically connected to the grid Repeat with newly electrified settlements until no more are added
- get_grid_lcoe(dist_adjusted, elecorder, additional_transformer, year, time_step, end_year, grid_calc)[source]
- grid_penalties(data_frame)[source]
this method calculates the grid penalties in each settlement
First step classifies the parameters and creates new columns with classification
Second step adds the grid penalty to increase grid cost in areas that higher road distance, higher substation distance, unsuitable land cover, high slope angle or high elevation
- static haversine_vector(lon1, lat1, lon2, lat2)[source]
Calculate the great circle distance between two points on the earth (specified in decimal degrees)
- pre_electrification(grid_price, year, time_step, end_year, grid_calc, grid_capacity_limit, grid_connect_limit)[source]
” …
- prepare_wtf_tier_columns(num_people_per_hh_rural, num_people_per_hh_urban, tier_1, tier_2, tier_3, tier_4, tier_5)[source]
Prepares the five Residential Demand Tier Targets based customized for each country
- project_pop_and_urban(pop_future, urban_future, start_year, years_of_analysis)[source]
This function projects population and urban/rural ratio for the different years of the analysis
- results_columns(techs, tech_codes, year, time_step, prio, auto_intensification)[source]
Calculate the capacity and investment requirements for each settlement
Once the grid extension algorithm has been run, determine the minimum overall option, and calculate the capacity and investment requirements for each settlement
- Parameters:
year (int) –
- set_residential_demand(rural_tier, urban_tier, num_people_per_hh_rural, num_people_per_hh_urban, productive_demand)[source]
this method defines residential demand per tier level for each target year
- Parameters:
rural_tier (int) –
urban_tier (int) –
num_people_per_hh_rural (float) –
num_people_per_hh_urban (float) –
productive_demand (int) –
- set_scenario_variables(year, num_people_per_hh_rural, num_people_per_hh_urban, time_step, start_year, urban_tier, rural_tier, end_year_pop, productive_demand)[source]
this method determines some basic parameters required in LCOE calculation it sets the basic scenario parameters that differ based on urban/rural so that they are in the table and can be read directly to calculate LCOEs
- Parameters:
year (int) –
num_people_per_hh_rural (float) –
num_people_per_hh_urban (float) –
time_step (int) –
start_year (int) –
urban_tier (int) –
rural_tier (int) –
end_year_pop (int) –
productive_demand (int) –
- class onsset.onsset.Technology(tech_life=0, base_to_peak_load_ratio=0, distribution_losses=0, connection_cost_per_hh=0, om_costs=0.0, capital_cost={inf: 0}, capacity_factor=0.9, grid_penalty_ratio=1, efficiency=1.0, diesel_price=0.0, grid_price=0.0, standalone=False, mini_grid=False, existing_grid_cost_ratio=0.1, grid_capacity_investment=0.0, diesel_truck_consumption=0, diesel_truck_volume=0, om_of_td_lines=0)[source]
Bases:
objectUsed to define the parameters for each electricity access technology, and to calculate the LCOE depending on input parameters.
- distribution_network(people, energy_per_cell, num_people_per_hh, grid_cell_area, productive_nodes=0)[source]
This method calculates the required components for the distribution network This includes potentially MV lines, LV lines and service transformers
- Parameters:
people (float) – Number of people in settlement
energy_per_cell (float) – Annual energy demand in settlement (kWh)
num_people_per_hh (float) – Number of people per household in settlement
grid_cell_area (float) – Area of settlement (km2)
productive_nodes (int) – Additional connections (schools, health facilities, shops)
Notes
Based on: https://www.mdpi.com/1996-1073/12/7/1395
- get_lcoe(energy_per_cell, people, num_people_per_hh, start_year, end_year, new_connections, total_energy_per_cell, prev_code, grid_cell_area, additional_mv_line_length=0.0, capacity_factor=0.9, grid_penalty_ratio=1, fuel_cost=0, elec_loop=0, productive_nodes=0, additional_transformer=0, penalty=1)[source]
Calculates the LCOE depending on the parameters.
- Parameters:
people (float or pandas.Series) – Number of people in settlement
new_connections (float or pandas.Series) – Number of new people in settlement to connect
prev_code (int or pandas.Series) – Code representation of previous supply technology in settlement
total_energy_per_cell (float or pandas.Series) – Total annual energy demand in cell, including already met demand
energy_per_cell (float or pandas.Series) – Annual energy demand in cell, excluding already met demand
num_people_per_hh (float or pandas.Series) – Number of people per household in settlement
grid_cell_area (float or pandas.Series) – Area of settlement (km2)
additional_mv_line_length (float or pandas.Series) – Distance to connect the settlement
additional_transformer (int) – If a transformer is needed on other end to connect to HV line
productive_nodes (int or pandas.Series) – Additional connections (schools, health facilities, shops)
elec_loop (int or pandas.Series) – Round of extension in grid extension algorithm
penalty (float or pandas.Series) – Cost penalty factor for T&D network, e.g. https://www.mdpi.com/2071-1050/12/3/777
start_year (int) –
end_year (int) –
capacity_factor (float or pandas.Series) –
grid_penalty_ratio (float or pandas.Series) –
fuel_cost (float or pandas.Series) –
- Return type:
lcoe or discounted investment cost
- classmethod set_default_values(base_year, start_year, end_year, discount_rate, hv_line_type=69, hv_line_cost=53000, mv_line_type=33, mv_line_amperage_limit=8.0, mv_line_cost=7000, lv_line_type=0.24, lv_line_cost=4250, lv_line_max_length=0.5, service_transf_type=50, service_transf_cost=4250, max_nodes_per_serv_trans=300, mv_lv_sub_station_type=400, mv_lv_sub_station_cost=10000, mv_mv_sub_station_cost=10000, hv_lv_sub_station_type=1000, hv_lv_sub_station_cost=25000, hv_mv_sub_station_cost=25000, power_factor=0.9, load_moment=9643)[source]
Initialises the class with parameter values common to all Technologies
- td_network_cost(people, new_connections, prev_code, total_energy_per_cell, energy_per_cell, num_people_per_hh, grid_cell_area, additional_mv_line_length=0, additional_transformer=0, productive_nodes=0, elec_loop=0, penalty=1)[source]
Calculates all the transmission and distribution network components
- Parameters:
people (float) – Number of people in settlement
new_connections (float) – Number of new people in settlement to connect
prev_code (int) – Code representation of previous supply technology in settlement
total_energy_per_cell (float) – Total annual energy demand in cell, including already met demand
energy_per_cell (float) – Annual energy demand in cell, excluding already met demand
num_people_per_hh (float) – Number of people per household in settlement
grid_cell_area (float) – Area of settlement (km2)
additional_mv_line_length (float) – Distance to connect the settlement
additional_transformer (int) – If a transformer is needed on other end to connect to HV line
productive_nodes (int) – Additional connections (schools, health facilities, shops)
elec_loop (int) – Round of extension in grid extension algorithm
penalty (float) – Cost penalty factor for T&D network, e.g. https://www.mdpi.com/2071-1050/12/3/777
- transmission_network(peak_load, additional_mv_line_length=0, additional_transformer=0, mv_distribution=False)[source]
This method calculates the required components for connecting the settlement Settlements can be connected to grid or a hydropower source This includes potentially HV lines, MV lines and substations
- Parameters:
peak_load (float) – Peak load in the settlement (kW)
additional_mv_line_length (float) – Distance to connect the settlement
additional_transformer (int) – If a transformer is needed on other end to connect to HV line
mv_distribution (bool) – True if distribution network in settlement contains MV lines
Notes
Based on: https://www.mdpi.com/1996-1073/12/7/1395
onsset.runner module
onsset.specs module
Columns in the specs file must match these exactly
Module contents
The onsset package contains the following modules:
onsset.py : main functions of the model
runner.py : runner is used to calibrate inputs and specify scenario runs