Model Factsheet

Overview / The Open Source energy Model Base for the European Union (OSeMBE)
Name The Open Source energy Model Base for the European Union
Acronym OSeMBE
Methodical Focus ESM
Institution(s) KTH Royal Institute of Technology
Author(s) (institution, working field, active time period) Hauke Henke; KTH Royal Institute of Technology
Current contact person Hauke Henke
Contact (e-mail)
Primary Purpose OSeMBE is designed as an engagement tool. From the choice of modelling system to the representation of the modeled energy system. The aim is to provide a platform for a set of different audiences who want to get engaged with energy modelling, but have no to little previous modelling experience. This could be university students, researchers in and outside the energy field, but also stakeholders like policy makers who intend to get a better understanding of the energy nexus and or modelling. The model provides the base to build a sound and diverse community of modellers and model users that can contribute and develop the model according to their needs in a transparent way.
Primary Outputs Key outputs of OSeMBE are the power generation capacities and generation mixes for all EU28, Switzerland and Norway, as well as the overall system cost.
Support / Community / Forum
Framework OSeMOSYS
Link to User Documentation
Link to Developer/Code Documentation
Documentation quality good
Source of funding REEEM, H2020 project, European Comission
Number of developers less than 10
Number of users less than 10
Open Source
License Creative Commons Attribution 4.0
Source code available
Access to source code
Data provided all data
Collaborative programming
GitHub Organisation
GitHub Contributions Graph
Modelling software OSeMOSYS
Internal data processing software MoManIv1.10, Excel
External optimizer
Additional software
Modeled energy sectors (final energy) electricity
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro
Conventional gas, oil, liquid fuels, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage -
User behaviour and demand side management
Changes in efficiency The change in efficiency can be considered in a deterministic way, i.e. the efficiency can be entered on an annual base.
Market models -
Geographical coverage
Geographic (spatial) resolution continents, national states
Time resolution user defined
Comment on geographic (spatial) resolution The electricity system of all 30 countries included are modeled and connected by the existing and planned trans-border transmission lines.
Observation period >1 year
Additional dimensions (sector) economic
Model class (optimisation) LP
Model class (simulation) Bottom up
Short description of mathematical model class
Mathematical objective costs
Approach to uncertainty Deterministic
Suited for many scenarios / monte-carlo
typical computation time less than a day
Typical computation hardware RAM: 256 GB, CPU: 3.5 GHz
Technical data anchored in the model -
Interfaces MoManI
Model file format .exe
Input data file format text
Output data file format text
Integration with other models
Integration of other models
Citation reference Henke, H.T.J., et al., The Base for a European Engagement Model - An Open Source Electricity Model of seven countries around the Baltic Sea, CYSENI 2018, Kaunas, Lithuania
Citation DOI -
Reference Studies/Models
Example research questions Impacts of EU decarbonisation pathways
Model usage REEEM H2020 project,
Model validation checked with measurements (measured data)
Example research questions Impacts of EU decarbonisation pathways
further properties
Model specific properties Stakeholder engagement model


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