Methods and tools
Overview of tools and frameworks

The models currently used at EEG are as follows:
Swiss TIMES energy system model (STEM)
See Model documentation and publications for details
European Swiss TIMES Electricity Model (EUSTEM)
The regions in the EUSTEM model

See Model documentation and publications for details
Global Multi-regional MARKAL (GMM) model
The 15 regions depicted in the GMM model
GMM-MCDA: Integration of GMM model and MCDA
More information is provided on the GMM-MCDA project website.
MERGE-ETL: Global integrated assessment model

Recent work on the model has included the review and update of energy technology input assumptions based on recent literature estimates; review and update of the climate sub-model to better reflect recent estimates of climate sensitivity, the carbon cycle and the influence of the ocean on temperature change; and implementation of increased detail in the representation of nuclear technologies and fuels, given recent developments and interest in nuclear policy (Marcucci, 2012). MERGE-ETL has been applied to explore uncertainty related to global climate and nuclear policies in the wake of the Fukushima disaster, focusing on the impact on Switzerland (Marcucci and Turton 2012). MERGE-ETL was also used in the AMPERE project.
Documentation
References
Kypreos, Socrates (2007). A MERGE model with endogenous technological change and the cost of carbon stabilization. Energy Policy 35: 5327–5336.Magne, Bertrand, Socrates Kypreos, and Hal Turton (2010). Technology options for low stabilization pathways with MERGE. The Energy Journal. Special Issue 1 31: 83–108.
Manne, Alan, Robert Mendelsohn, and Richard Richels (1995). MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23: 17–34.
Marcucci, Adriana. (2012) Realizing a Sustainable Energy System in Switzerland in a Global Context. Ph.D. thesis, ETH Zurich.
Marcucci, A. and H. Turton (2012). Swiss Energy Strategies under Global Climate Change and Nuclear Policy Uncertainty, The Swiss Journal of Economics and Statistics, Vol. 148 (2), pp. 317-345.
Marcucci, A. and H. Turton (2011). Analyzing Energy Technology Options for Switzerland in the Face of Global Uncertainties: An Overview of the MERGE model, NCCR climate Research paper 2011/05.
Seebregts, A., Bos S., Kram T., and G. Schaeffer (2000). Endogenous Learning and Technology Clustering: Analysis with MARKAL Model of the Western European Energy System. International Journal of Energy Issues 14: 289–319.
Cross-border electricity market model of Switzerland (BEM)
See Model documentation and publications for details
PeSto
See Model documentation and publications for details
Socio-Economic Energy model for Digitalization (SEED)
The model's purpose is to quantify digitalization's impact on technology investment choices, energy consumption and emissions on different energy sectors.
SEED represents the heterogeneity of decision processes of different actors in the energy sectors (households, sub-sectors of the services sector, and industries) to analyze synergies and interactions in the adoption of low-energy consuming digital services and practices induced by digitalization (so-called spillover effects). Household adoption of new digital practices can accelerate the adoption of e-services and vice-versa from the services sub-sectors. The adoption of digital technologies by households and services sub-sectors can trigger the adoption of digital technologies for process optimization in industry. Furthermore, adopting digital practices by households changes their transport and residential energy consumption, altering their investment decisions in transport and residential technologies.