Model Factsheet

Overview / Electricity Transshipment Model (ELTRAMOD)

Model Electricity Transshipment Model (ELTRAMOD)

General Information
Name Electricity Transshipment Model
Acronym ELTRAMOD
Methodical Focus linear optimisation
Institution(s) TU Dresden; Chair of Energy Economics; Germany
Author(s) (institution, working field, active time period) Dominik Möst (2010 - today); David Gunkel; Theresa Ladwig; Daniel Schubert; Hannes Hobbie (2012 - current); Christoph Zöphel (2015 - 2021); Steffi Misconel (2018 - current); Carl-Philipp Anke (2018 - 2021)
Current contact person Steffi Misconel
Contact (e-mail) steffi.misconel@tu-dresden.de
Website https://tu-dresden.de/bu/wirtschaft/bwl/ee2/forschung/modelle/eltramod
Logo
Primary Purpose ELTRAMOD is a fundamental bottom-up electricity market model incorporating the electricity markets of the EU-27 states, Norway, Switzerland, United Kingdom and the Balkan region as well as the Net Transfer Capacities (NTC) between these countries. Each country is treated as one node with country-specific hourly time series of electricity demand and renewable feed-in. The country-specific wind and photovoltaic feed-in is characterised by the installed capacity and an hourly capacity factor. The capacity factors are calculated with the help of publically available time series of wind speed and solar radiation. ELTRAMOD is a linear optimisation model which calculates the cost-minimal generation dispatch and investments in additional transmission lines, storage facilities and other flexibility options. The set of conventional power plants consists of fossil fired, nuclear and hydro plants where different technological characteristics are implemented, such as efficiency, emission factors and availability. Daily prices for CO2 allowances, as well as daily wholesale fuel prices supplemented by country-specific mark-ups are implemented in ELTRAMOD. The country- and technology-specific parameters and the temporal resolution of 8760 hours allow an in-depth analysis of various challenges of the future European electricity system. For example, the trade-off between network extension and storage investment as well as import and export flows of electricity in Europe can be analysed.
Primary Outputs Total system costs; wholesale electricity / market prices; investments and dispatch in conventional power plants, renewables, flexibility options (sector coupling technologies); renewable generation and curtailment; storage operation; CO2 emissions etc.
Support / Community / Forum
Framework
Link to User Documentation -
Link to Developer/Code Documentation -
Documentation quality expandable
Source of funding National tenders, EU tenders
Number of developers less than 10
Number of users less than 100
Openness
Open Source
Planned to open up in the future
Costs -
Software
Modelling software GAMS
Internal data processing software
External optimizer
Additional software
GUI
Coverage
Modeled energy sectors (final energy) electricity, heat, sector coupling with industry and transport sector
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro, Biomass,Biogas,Biofuels
Conventional gas, lignite, hard coal, oil, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat -
Properties electrical grid transshipment model, single-node / copper plate model
Modeled technologies: components for storage battery, compressed air, pump hydro, heat, gas
User behaviour and demand side management a) DSM as load shifting b) Power-to-gas / power-to-power c) Power-to-heat / heat pumps d) electrical vehicles (add-on)
Changes in efficiency
Market models fundamental model
Geographical coverage EU-27 + Norway + Switzerland + United Kingdom + Balkan countries
Geographic (spatial) resolution national states, TSO regions, federal states, regions, NUTS 3, power stations
Time resolution annual, hour
Comment on geographic (spatial) resolution
Observation period <1 year, 1 year, >1 year
Additional dimensions (sector) ecological, economic
Mathematical Properties
Model class (optimisation) LP
Model class (simulation) Bottom up
Other
Short description of mathematical model class Fundamental bottom-up model / linear optimisation / minimisation of total system costs
Mathematical objective costs
Approach to uncertainty Deterministic
Suited for many scenarios / monte-carlo
typical computation time less than a day
Typical computation hardware standard desktop PC (4-core, 8 GB RAM) or server infrastructure
Technical data anchored in the model -
Model Integration
Interfaces Data exchange to input and output databases, communication through .xls and .gdx
Model file format .gms
Input data file format .xls
Output data file format .gdx
Integration with other models
Integration of other models
References
Citation reference Demand Side Management in Deutschland zur Systemintegration erneuerbarer Energien
Citation DOI https://dx.doi.org/urn:nbn:de:bsz:14-qucosa-236074
Reference Studies/Models Misconel, S., Zöphel, C., Möst, D., 2021. Assessing the value of demand response in a decarbonized energy system – A largescale model application. Applied Energy 299. https://doi.org/10.1016/j.apenergy.2021.117326; Misconel, S., Leisen, R., Mikurda, J., Zimmermann, F., Fichtner, W., Möst, D., Weber, C., 2022. Systematic comparison of high resolved electricity market modeling approaches, their impact on investment-dispatch decisions and generation adequacy. Renewable and Sustainable Energy Reviews 153. https://doi.org/10.1016/j.rser.2021.111785; Schreiber, S., Zöphel, C., Möst, D., 2021. Optimal Energy Portfolios in the Electricity d carSector: Trade-offs and Interplay between Different Flexibility Options, in: Möst, D., Schreiber, S., Herbst, A., Jakob, M., Martino, A., Poganietz, W.-R. (Eds.), The Future European Energy System - Renewable Energy, Flexibility Options and Technological Progress. Springer International Publishing. https://doi.org/10.1007/978-3-030-60914-6. Anke, C.-P.; Hobbie, H.; Schreiber, S.; Möst, D.: Coal phase-outs carbon prices: Interactions between EU emission trading and national carbon mitigation policies. In: Energy Policy Vol. 144 (2020), Nr. 111647 Zöphel, Christoph; Schreiber, Steffi; Herbst, A.; Klinger, A-L; Manz, P.; Heitel, S.; Fermi, F.; Wyrwa, A.; Raczynski, M.; Reiter, U. D4.3 Report on cost optimal energy technology portfolios for system flexibility in the sectors heat, electricity and mobility. In: Report des REFLEX Projektes (2019) Energy System Analysis Agency (ESA²): Shaping our energy system - combining European modelling expertise, Brüssel, 2013. Gunkel, D.; Kunz, F.; Müller, T., von Selasinsky, A.; Möst, D.: Storage Investment or Transmission Expansion: How to Facilitate Renewable Energy Integration in Europe?. Tagungsband VDE-Kongress Smart Grid - Intelligente Energieversorgung der Zukunft, 2012. Müller, T.: Influence of increasing renewable feed-in on the operation of conventional and storage power plants. 1st KIC InnoEnergy Scientist Conference, Leuven, 2012. Müller, T.; Gunkel, D.; Möst, D.: Renewable curtailment and its impact on grid and storage capacities in 2030, Enerday Conference, Dresden 2013.
Example research questions -
Model usage -
Model validation cross-checked with other models
Example research questions -
further properties
Model specific properties -

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