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| | {{Model | | {{Model |
| − | |Full_Model_Name=OpeN Source Spatial Electrification Toolkit | + | |Full_Model_Name=Open Source Spatial Electrification Tool |
| − | |Acronym=ONSSET | + | |Acronym=OnSSET |
| | |author_institution=KTH Royal Institute of Technology | | |author_institution=KTH Royal Institute of Technology |
| − | |authors=Christopher Arderne | + | |authors=Dimitrios Mentis, Mark Howells, Holger Rogner, Alexandros Korkovelos, Christopher Arderne, Oliver Broad, Manuel Welsch, Francesco Fuso Nerini |
| | + | |contact_persons=Dimitrios Mentis |
| | + | |contact_email=mentis@kth.se |
| | |source_download=https://github.com/KTH-dESA/PyOnSSET | | |source_download=https://github.com/KTH-dESA/PyOnSSET |
| − | |text_description=ONSSET is used to estimate, analyze, and visualize the most cost-effective electrification access option, be it grid, mini-grid, or stand-alone. It is designed to be used in developing countries like Ethiopia, India, and Nigeria. | + | |logo=Onsset logo 3.png |
| | + | |text_description=OnSSET has been designed for identifying least-cost technology options to electrify areas presently unserved by grid-based electricity and to estimate associated investment needs related to electrification. OnSSET uses energy-related data and information on a geographical basis such as settlement sizes and locations, distances from existing and planned transmission network, power plants, economic activity, local renewable energy flows,road network, nighttime light etc. |
| | + | |Primary outputs=Optimal Electrification Mix, Investment Needs |
| | + | |Source of funding=SIDA, UNDESA, UNDP, IEA, ABB, World Bank |
| | |open_source_licensed=Yes | | |open_source_licensed=Yes |
| | |license=MIT license (MIT) | | |license=MIT license (MIT) |
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| | |open_future=No | | |open_future=No |
| | |modelling_software=Python | | |modelling_software=Python |
| | + | |processing_software=Python |
| | |GUI=No | | |GUI=No |
| | |technologies=Renewables, Conventional Generation | | |technologies=Renewables, Conventional Generation |
| | + | |Demand sectors=Households |
| | + | |Energy carriers (Renewable)=Hydro, Sun, Wind |
| | + | |Transfer (Electricity)=Distribution, Transmission |
| | |Storage (Gas)=No | | |Storage (Gas)=No |
| | |Storage (Heat)=No | | |Storage (Heat)=No |
| | + | |georegions=Sub-Saharan Africa, developing Asia, Latin America |
| | + | |georesolution=1 km to 10 km |
| | + | |timeresolution=Multi year |
| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
| | |math_objective=Cost minimization | | |math_objective=Cost minimization |
| | |is_suited_for_many_scenarios=No | | |is_suited_for_many_scenarios=No |
| | |montecarlo=No | | |montecarlo=No |
| | + | |citation_references=Mentis, Dimitrios; Welsch, Manuel; Fuso Nerini, Francesco; Broad, Oliver; Howells, Mark; Bazilian, Morgan; Rogner, Holger (December 2015). "A GIS-based approach for electrification planning: a case study on Nigeria". Energy for Sustainable Development. 29: 142–150. doi:10.1016/j.esd.2015.09.007. ISSN 0973-0826. |
| | + | |citation_doi=10.1016/j.esd.2015.09.007 |
| | + | |report_references=IEA World Energy Outlook 2014, IEA World Energy Outlook 2015, IEA and World Bank Global Tracking Framework 2015 |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
Mentis, Dimitrios; Welsch, Manuel; Fuso Nerini, Francesco; Broad, Oliver; Howells, Mark; Bazilian, Morgan; Rogner, Holger (December 2015). "A GIS-based approach for electrification planning: a case study on Nigeria". Energy for Sustainable Development. 29: 142–150. doi:10.1016/j.esd.2015.09.007. ISSN 0973-0826.
https://dx.doi.org/10.1016/j.esd.2015.09.007
IEA World Energy Outlook 2014, IEA World Energy Outlook 2015, IEA and World Bank Global Tracking Framework 2015
