Oemof Application Berlin Brandenburg (oemof_abbb)

Name Oemof Application Berlin Brandenburg
Acronym oemof_abbb
Methodical Focus Energiestrategie
Institution(s) Reiner Lemoine Institut (RLI)
Author(s) (institution, working field, active time period) Elisa Gaudchau (RLI), Birgit Schachler (RLI), Ludwig Hülk (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
Please list references to reports and studies which were produced using the model
Example research questions
Larger scale usage
Model validation -
Example research questions
further properties
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, heat pump
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
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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