Open Source | |
License | MIT |
Source code available | |
GitHub | |
Access to source code | https://gitlab.com/diw-evu/dieter_public |
Data provided | none |
Collaborative programming |
Modelling software | GAMS, Python |
Internal data processing software | |
External optimizer | |
Additional software | |
GUI |
Modeled energy sectors (final energy) |
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Modeled demand sectors |
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Modeled technologies: components for power generation or conversion |
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Modeled technologies: components for transfer, infrastructure or grid |
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Properties electrical grid |
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Modeled technologies: components for storage |
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User behaviour and demand side management | DSM: Detailed representation of load shifting and load curtailment; "User behaviour": If this means empirically-founded behavioural aspects, the answer is no. | ||||||
Changes in efficiency | Exogenous parameter assumptions | ||||||
Market models | fundamental model | ||||||
Geographical coverage | In most applications so far; focus on Germany; which is treated as one node; extended version with additional central European country nodes is available | ||||||
Geographic (spatial) resolution |
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Time resolution | hour | ||||||
Comment on geographic (spatial) resolution | DIETER's geographic scope and resolution tend to improve over time (as probably is the case for all models) | ||||||
Observation period | 1 year | ||||||
Additional dimensions (sector) |
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Model class (optimisation) | LP |
Model class (simulation) | - |
Other | |
Short description of mathematical model class | Linear optimization model (usually cost minimization) |
Mathematical objective | costs, (CO2 minimization has been implemented in an intermediate version, but is currently not operational) |
Approach to uncertainty | Currently being developed (project LKD-EU) |
Suited for many scenarios / monte-carlo | |
typical computation time | less than an hour |
Typical computation hardware | Runs on a standard PC |
Technical data anchored in the model | - |
Interfaces | None (we use GAMS GDX files and Excel) |
Model file format | .gms |
Input data file format | .xls |
Output data file format | .gdx, converted to .xls |
Integration with other models | |
Integration of other models |
Citation reference | Zerrahn, A., Schill, W.-P. (2017): Long-run power storage requirements for high shares of renewables: review and a new model. Renewable and Sustainable Energy Reviews 79, 1518-1534 |
Citation DOI | https://doi.org/10.1016/j.rser.2016.11.098 |
Reference Studies/Models | https://doi.org/10.1016/j.rser.2017.05.205, https://doi.org/10.5547/2160-5890.6.1.wsch, https://doi.org/10.1007/s12398-016-0174-7 |
Example research questions | Which capacities of various flexibility / sector coupling options prove to be optimal under different shares of renewables, and what are their effects on quantities and prices? |
Model usage | - |
Model validation |
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Example research questions | Which capacities of various flexibility / sector coupling options prove to be optimal under different shares of renewables, and what are their effects on quantities and prices? |
further properties | DIETER also includes reserve markets and stylized representations of solar prosumage, V2G, and various types of residential power-to-heat. Further power-to-x (hydrogen) details are currently being developed. |
Model specific properties | Strengths: lean, computationally efficient, traceable; challenges: not as detailed and not as well-calibrated to present-day analyses as some other models |