oemof app BBB (abbb)

Name oemof app BBB
Acronym abbb
Institution(s) Reiner Lemoine Institut (RLI)
Author(s) (institution, working field, active time period) Elisa Gaudchau (RLI), Birgit Schachler (RLI), Ludwig HĂĽlk (RLI)
Current contact person Elisa Gaudchau
Contact (e-mail) elisa.gaudchau@rl-institut.de
Website http://reiner-lemoine-institut.de
Primary Purpose Simulation of the energy system in 2030 in the German region Berlin and Brandenburg
Primary Outputs CO2 emissions, Full Load Hours, export, ...
Support / Community / Forum
Framework oemof (
Link to User Documentation https://github.com/rl-institut/appBBB
Link to Developer/Code Documentation https://github.com/rl-institut/appBBB
Documentation quality expandable
Source of funding Private funding
Number of devolopers less than 10
Number of users less than 10
Open Source
License Not decided yet
Source code available
Link to source code https://github.com/rl-institut/appBBB.
Data provided some
Cooperative programming
GitHub Organisation
GitHub Contributions Graph
Modelling software Python
Internal data processing software
External optimizer
Additional software
Citation reference -
Citation DOI -
References to reports produced using the model -
Example research questions -
Larger scale usage -
Model validation -
Model specific properties -
Modeled energy sectors (final energy) electricity, heat
Modeled demand sectors Households, Industry, Commercial sector
Modelled energy carriers (primary energy carrier)
Gas Natural gas, Biogas
Liquids -
Solid Hard coal, Lignite, Biomass, Sun, Wind, Geothermal heat
Renewables Sun, Wind, Geothermal heat
Modeled technologies: components for generation or conversion
Renewables PV, Wind, Biomass, Biogas, Solar thermal, generation renewables others text
Conventional gas, coal, oil
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat -
Properties electrical grid net transfer capacities
Modeled technologies: components for storage heat
User behaviour and demand side management
Changes in efficiency -
Market models -
Geographical coverage
Geographic (spatial) resolution regions
Time resolution hour
Comment on geographic (spatial) resolution Regionale Planugsgmeinschaften Brandenburg + Berlin
Observation period 1 year
Additional dimensions (sector) -
Model class (optimisation) LP
Model class (simulation) -
Short description of mathematical model class -
Mathematical objective costs
Approach to uncertainty -
Suited for many scenarios / monte-carlo
typical computation time less than an hour
Typical computation hardware -
Technical data anchored in the model -
Interfaces -
Model file format .py
Input data file format .csv
Output data file format .csv
Integration with other models -
Integration of other models -

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