| Name | European Electricity Market Model |
| Acronym | E2M2 |
| Methodical Focus | None |
| Institution(s) | IER |
| Author(s) (institution, working field, active time period) | Weber; Christoph; IER - University of Stuttgart; Sun; Ninghong; IER - University of Stuttgart; Bothor; Sebastian; IER - University of Stuttgart; Brand; Heike; IER - University of Stuttgart; Fleischer; Benjamin; IER - University of Stuttgart; and more |
| Current contact person | Savvidis, Georgios |
| Contact (e-mail) | georgios.savvidis@ier.uni-stuttgart.de |
| Website | https://www.ier.uni-stuttgart.de/forschung/modelle/E2M2/ |
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| Primary Purpose | Optimizing dispatch and investment for the electricity and heat sector |
| Primary Outputs | investment decisions, utility dispatch, hourly spot market prices |
| Support / Community / Forum | |
| Framework | |
| Link to User Documentation | no |
| Link to Developer/Code Documentation | no |
| Documentation quality | expandable |
| Source of funding | university, research funds |
| Number of developers | less than 20 |
| Number of users | less than 100 |
| Open Source | |
| Planned to open up in the future | |
| Costs | only for internal use |
| Modelling software | GAMS; Git |
| 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, no user behaviour | ||||||
| Changes in efficiency | |||||||
| Market models | - | ||||||
| Geographical coverage | |||||||
| 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) | - |
| 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 | "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 | no |
| 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, detailed biomass depiction possible, detailed demand side management possible, special stochastic version available (E2M2s); weakness: methodological waekness of all perfect foresight optimization models: storages have a higher damping effect on price peaks than in reality |