Model Factsheets
| Name | European Electricity Market Model |
| Acronym | E2M2 |
| Methodical Focus | Optimization |
| Institution(s) | IER |
| Author(s) (institution, working field, active time period) | As of 2026: Annika Gillich; Clara Fuhrer; Erdal Tekin; Heike Brand; Ron George. All working at IER - University of Stuttgart; |
| Current contact person | Tekin, Erdal |
| Contact (e-mail) | erdal.tekin@ier.uni-stuttgart.de |
| Website | https://www.ier.uni-stuttgart.de/en/research/models/E2M2/ |
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| Primary Purpose | Optimizing dispatch and investment for the electricity and heat sector |
| Primary Outputs | investment and de-investment decisions, utility dispatch and unit commitment, hourly spot market prices, CO2 emissions, NTC-Transmission |
| Support / Community / Forum | |
| Framework | |
| Link to User Documentation | no |
| Link to Developer/Code Documentation | no |
| Documentation quality | expandable |
| Source of funding | Various, including Federal Ministry of Research, Technology and Space (BMFTR) and Federal Ministry for Economic Affairs and Energy (BMWE) |
| Number of developers | less than 20 |
| Number of users | less than 100 |
| Open Source | |
| Planned to open up in the future | |
| Costs | - |
| Modelling software | GAMS; |
| Internal data processing software | MS Access; MS Excel |
| External optimizer | |
| Additional software | Sourcetree |
| 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 | demand side management as a negative storage with time constraints, optional user behaviour for transport sector | ||||||
| Changes in efficiency | Variation over 5 year steps | ||||||
| Market models | fundamental model | ||||||
| Geographical coverage | 22 countries within Europe | ||||||
| Geographic (spatial) resolution |
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| Time resolution | hour | ||||||
| Comment on geographic (spatial) resolution | |||||||
| Observation period | <1 year, 1 year, >1 year | ||||||
| Additional dimensions (sector) |
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| Model class (optimisation) | LP, MILP |
| Model class (simulation) | Bottom up |
| Other | |
| Short description of mathematical model class | The model can be run in LP or MILP mode |
| Mathematical objective | costs |
| Approach to uncertainty | Deterministic, Stochastic |
| Suited for many scenarios / monte-carlo | |
| typical computation time | less than a day |
| Typical computation hardware | dual CPU with 72 threads @2,3GHz, 768GB RAM |
| Technical data anchored in the model | no |
| Interfaces | no special API other than GAMS GUI is used |
| Model file format | .gms |
| Input data file format | text |
| Output data file format | text |
| Integration with other models | |
| Integration of other models |
| Citation reference | Sun, Ninghong (2013): Modellgestützte Untersuchung des Elektrizitätsmarktes. Kraftwerkseinsatzplanung und -investitionen. Universität Stuttgart. |
| Citation DOI | http://dx.doi.org/10.18419/opus-2159 |
| Reference Studies/Models | Tekin, Erdal; Achenbach, David; Gillich, Annika; Hufendiek, Kai (2026): Event-independent and anticipative resilience indicators for future-oriented electricity system model analysis. In: Energy strategy reviews 64, S. 102162.; Tekin, Erdal; Gillich, Annika; Hufendiek, Kai (2024): Impact of different CO2 price paths on the development of the European electricity system. In: Energy strategy reviews 55, S. 101499; Gillich, Annika; Brand, Heike; Schmid, Tobias; Hufendiek, Kai (2024): Die Schlüsselrolle von Flexibilität im Stromsystem 2030. Nutzenanalyse und kritische Flex-Technologien.; Gillich, Annika (2024): Techno-economic Analysis of an Instrument Mix to Decarbonize the Electricity Sector. Dissertation. Uni Stuttgart, Stuttgart. Institut für Energiewirtschaft und Rationelle Energieanwendung; Savvidis, Georgios (2024): Umgang mit zeitlich hoher Auflösung in Elektrizitätsmarktmodellen im Zusammenhang mit der Bewertung von Flexibilitäten. Dissertation. Universität Stuttgart, Stuttgart.; Seckinger, Nils (2021): Methodische Weiterentwicklung dynamischer, prospektiver Treibhausgasemissionsfaktoren zur Analyse von Technologien der Sektorkopplung. Dissertation. Universität Stuttgart, Stuttgart.; Fahl et al. (2015): "Systemanalyse Energiespeicher. Schlussbericht von Oktober 2015". Institut für Energiewirtschaft und Rationelle Energieanwendung (IER). Universität Stuttgart; Eberl et al. (2015): "Optimale Strukturen des deutschen Elektrizitätssystems bei hohen Anteilen erneuerbarer Energien: Bedarf und Bedeutung vn Integrations- und Flexibilisierungsoptionen." Institut für Energiewirtschaft und Rationelle Energieanwendung (IER). Universität Stuttgart; Hundt et al. (2010) „Herausforderungen eines Elektrizitätsversorgungssystems mit hohen Anteilen erneuerbarer Energien.“ Institut für Energiewirtschaft und Rationelle Energieanwendung (IER). Universität Stuttgart. |
| Example research questions | How high is the future flexibility demand in the electricity and heat sector and how to satisfy it with least costs? What price effects does the splitting of a bidding zone impose? |
| Model usage | - |
| Model validation |
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| Example research questions | How high is the future flexibility demand in the electricity and heat sector and how to satisfy it with least costs? What price effects does the splitting of a bidding zone impose? |
| further properties | |
| Model specific properties | strengths: simultaneous optimization of dispatch and investment, fully represented year with 8760 hours, myopic and perfect foresight, detailed biomass depiction possible, detailed demand side management possible, special stochastic version available (E2M2s); weakness: methodological weakness due to restriction on single sectors |