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| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
| | |math_modeltype_shortdesc=Linear optimization model | | |math_modeltype_shortdesc=Linear optimization model |
| | + | Decision variables include investment and dispatch of generation, storage, DSM and different sector coupling options including both wholesale and balancing markets |
| | |math_objective=Minimization of total system costs | | |math_objective=Minimization of total system costs |
| | |deterministic=Deterministic; Perfect foresight; Sensitivity analysis ; | | |deterministic=Deterministic; Perfect foresight; Sensitivity analysis ; |
Revision as of 09:29, 19 May 2021
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Electricity Transshipment Model
by Technische Universität Dresden (ee2)
Authors:
Dominik Möst, David Gunkel, Theresa Ladwig, Daniel Schubert, Hannes Hobbie, Christoph Zöphel, Steffi Misconel, Carl-Philipp Anke
Contact:
Dominik Möst
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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.
Based on GAMS; CPLEX. Using for data processing.
Website / Documentation
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 Not Open Source
Not directly downloadable
Some input data shipped
 Planned to open up further in the future
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| Model Scope |
Model type and solution approach |
| Model class
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German and European Electricity Market
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| Sectors
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Electricity including sector coupling (EVs, PtX)
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| Technologies
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Renewables, Conventional Generation, CHP
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| Decisions
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dispatch, investment
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| Regions
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EU-27 + Norway + Switzerland + United Kingdom + Balkan countries
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| Geographic Resolution
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NUTS0 - NUTS3
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| Time resolution
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Hour
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| Network coverage
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transmission, net transfer capacities
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| Model type
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Optimization
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Linear optimization model
Decision variables include investment and dispatch of generation, storage, DSM and different sector coupling options including both wholesale and balancing markets
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| Variables
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| Computation time
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minutes
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| Objective
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Minimization of total system costs
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| Uncertainty modeling
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Deterministic; Perfect foresight; Sensitivity analysis ;
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| Suited for many scenarios / monte-carlo
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No
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References
Scientific references
Demand Side Management in Deutschland zur Systemintegration erneuerbarer Energien
https://dx.doi.org/urn:nbn:de:bsz:14-qucosa-236074
Reports produced using the model
Schreiber, S., Zöphel, C., Möst, D., 2021. Optimal Energy Portfolios in the Electricity Sector: 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 and 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.
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