Model Factsheet

Overview / Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER)

Model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER)

General Information
Name Dispatch and Investment Evaluation Tool with Endogenous Renewables
Acronym DIETER
Methodical Focus None
Institution(s) DIW Berlin
Author(s) (institution, working field, active time period) Wolf-Peter Schill (DIW Berlin; Department of Energy; Transportation; Environment; working on the model since 2014), Alexander Zerrahn (DIW Berlin; Department of Energy; Transportation; Environment; working on the model since 2014)
Current contact person Wolf-Peter Schill
Contact (e-mail) wschill@diw.de
Website http://www.diw.de/dieter
Logo
Primary Purpose The Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) has initially been developed in the research project StoRES to study the role of power storage and other flexibility options in a greenfield setting with high shares of renewables. Meanwhile, several model extensions have been developed and applied to different research questions. The model determines cost-minimizing combinations of power generation, demand-side management, and storage capacities as well as their respective dispatch in both the wholesale and the reserve markets. DIETER thus captures multiple system values of energy storage and other flexibility options related to arbitrage, firm capacity, and reserves.
Primary Outputs Capacities, dispatch (and prices)
Support / Community / Forum
Framework
Link to User Documentation http://www.diw.de/dieter
Link to Developer/Code Documentation http://www.diw.de/dieter
Documentation quality good
Source of funding Various projects
Number of developers less than 10
Number of users -
Openness
Open Source
License MIT
Source code available
GitHub
Access to source code https://gitlab.com/diw-evu/dieter_public
Data provided none
Collaborative programming
Software
Modelling software GAMS, Python
Internal data processing software
External optimizer
Additional software
GUI
Coverage
Modeled energy sectors (final energy) electricity, heat
Modeled demand sectors Households, Transport
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro
Conventional gas, oil, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity -
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage battery, compressed air, pump hydro, chemical, heat
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 national states
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) Solar prosumage
Mathematical Properties
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 -
Model Integration
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
References
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 checked with measurements (measured data)
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

Actions

Edit Delete

Tags

Powerplant Renewable Germany Electricity Heat Households Transport Solar Wind MODEX Storage long-term OSE model comparison MIT