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 http://www.ier.uni-stuttgart.de/forschung/modelle/E2M2s/index.html
Logo /media/logos/ier_logo.gif
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
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 -
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
Modeled energy sectors (final energy) electricity, heat
Modeled demand sectors Households, Industry, Commercial sector, Transport
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro
Conventional gas, oil, liquid fuels, nuclear
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat distribution
Properties electrical grid -
Modeled technologies: components for storage battery, compressed air, pump hydro, heat
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 continents, national states, TSO regions, federal states, regions
Time resolution hour
Comment on geographic (spatial) resolution
Observation period <1 year, 1 year, >1 year
Additional dimensions (sector) ecological, additional dimensions sector ecological text, economic, additional dimensions sector economic text
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

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