Model Factsheet

General Information

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)	haukeh@kth.se
Website	http://www.osemosys.org/osembe.html
Logo
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	http://www.osemosys.org/osembe.html
Link to Developer/Code Documentation	https://github.com/KTH-dESA/OSeMBE
Documentation quality	good
Source of funding	REEEM, H2020 project, European Comission
Number of developers	less than 10
Number of users	less than 10

Openness

Open Source
License	Creative Commons Attribution 4.0
Source code available
GitHub
Access to source code	https://github.com/HauHe/OSeMBE
Data provided	all data
Collaborative programming

GitHub Organisation

Software

Modelling software	OSeMOSYS
Internal data processing software	MoManIv1.10, Excel
External optimizer
Additional software
GUI

Coverage

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

Mathematical Properties

Model class (optimisation)	LP
Model class (simulation)	Bottom up
Other
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	-

Model Integration

Interfaces	MoManI
Model file format	.exe
Input data file format	text
Output data file format	text
Integration with other models
Integration of other models

References

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	https://doi.org/10.5281/zenodo.3368574
Example research questions	Impacts of EU decarbonisation pathways
Model usage	REEEM H2020 project, reeem.org
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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