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Model Factsheets

Name REMix (Renewable Energy Mix)
Acronym REMix
Methodical Focus Sector coupling , Model speed-up , Multi-criteria optimization
Institution(s) DLR - German Aerospace Center
Author(s) (institution, working field, active time period) Y. Scholz; H.C. Gils; K.-K. Cao; M. Wetzel; S. Sasanpour; H. Gardian; M. Yeligeti; J. Schmugge; E. Arellano; G. Recht; S. Le Bihan; D. Zapf (all DLR); T. Fichter; F. Cebulla; D. Luce de Tena; D. Heide; F. Borggrefe; D. Hess; A. Rubbert; K.von Krbek; N. Wulff (ex-DLR)
Current contact person Hans Christian Gils
Contact (e-mail) hans-christian.gils@dlr.de
Website https://www.dlr.de/ve/desktopdefault.aspx/tabid-15971/25909_read-66550/
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Primary Purpose development and analysis of energy supply scenarios with focus on infrastructures
Primary Outputs least cost configuration and operation of the considered system, least cost paths for future energy system development, systems costs, CO2 emissions, indicators for security of power supply, renewable energy usage, storage demand, sector coupling implementation
Support / Community / Forum
Framework REMix
Link to User Documentation https://dlr-ve.gitlab.io/esy/remix/framework/dev/documentation/index.html
Link to Developer/Code Documentation https://dlr-ve.gitlab.io/esy/remix/framework/dev/documentation/tech-docs/index.html
Documentation quality good
Source of funding German Federal Ministry of Economic Affairs and Energy, German Federal Ministry of Research, Technology and Space
Number of developers less than 20
Number of users less than 100

Open Source
License BSD 3-clause 'New' or 'Revised' license
Source code available
GitHub
Access to source code https://gitlab.com/dlr-ve/esy/remix/framework
Data provided example data
Collaborative programming

Modelling software GAMS; Python
Internal data processing software JavaScript; Excel; Python
External optimizer CPLEX; gurobi; PIPS-IPM++
Additional software
GUI

Modeled energy sectors (final energy) electricity, heat, Transport
Modeled demand sectors Households, Industry, Commercial sector, Transport
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro, Biomass,Biogas,Biofuels, Solar thermal, Geothermal heat
Conventional gas, lignite, hard coal, oil, liquid fuels, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas transmission
Heat distribution
Properties electrical grid DC load flow, transshipment model, single-node / copper plate model
Modeled technologies: components for storage battery, kinetic, compressed air, pump hydro, chemical, heat, gas
User behaviour and demand side management Consideration of demand response in industry, household and tertiary sector, see http://dx.doi.org/10.1016/j.apenergy.2015.10.083
Changes in efficiency Considered only in input data
Market models fundamental model
Geographical coverage Global
Geographic (spatial) resolution continents, national states, TSO regions, federal states, regions, NUTS 3
Time resolution hour
Comment on geographic (spatial) resolution Global renewable energy database. Past model applications mostly to Germany and Europe, but also Brazil, USA, China, Australia, New Zealand and others
Observation period 1 year, >1 year
Additional dimensions (sector) ecological, additional dimensions sector ecological text, Resource criticality

Model class (optimisation) LP, MILP
Model class (simulation) -
Other
Short description of mathematical model class
Mathematical objective CO2, costs, RE-share, any considered, could also be e.g. material demand
Approach to uncertainty Deterministic, Stochastic
Suited for many scenarios / monte-carlo
typical computation time more than a day
Typical computation hardware -
Technical data anchored in the model no

Interfaces Own Python API
Model file format .gms
Input data file format .dat, .csv
Output data file format .gdx, .csv, .xls
Integration with other models EnDAT (global wind and solar power generation potential database), AMIRIS, venco.py
Integration of other models

Citation reference see below
Citation DOI https://doi.org/10.21105/joss.06330
Reference Studies/Models see full list of publications here: https://helmholtz.software/software/remix
Example research questions How do overall power generation costs develop at high variable renewable energy (VRE) shares and at different proportions of wind and solar power? How does VRE power generation interact with transmission, storage, sector coupling and backup power generation? Which infrastructural developments and investments are required to maintain security of supply? Which technologies can provide backup at the lowest cost? To what extent are curtailments expected as an ultimate balancing measure after transmission and storage?
Model usage -
Model validation cross-checked with other models, checked with measurements (measured data)
Example research questions How do overall power generation costs develop at high variable renewable energy (VRE) shares and at different proportions of wind and solar power? How does VRE power generation interact with transmission, storage, sector coupling and backup power generation? Which infrastructural developments and investments are required to maintain security of supply? Which technologies can provide backup at the lowest cost? To what extent are curtailments expected as an ultimate balancing measure after transmission and storage?
further properties
Model specific properties -