Methods and toolsThe main tools of EEG comprise energy-system models. This type of model refers to a process-oriented representation of the energy system with technological detail. The energy-system models used by EEG are from the MARKAL (MARKet ALlocation) and TIMES (The Integrated MARKAL EFOM System) family of models (Fishbone et al., 1981; Loulou et al., 2005), a group of perfect-foresight, energy-system optimization models that represent current and potential future energy technologies. Such models are typically used to determine the least-cost configuration of the energy system for a given time horizon under a set of assumptions about end-use demands, technology characteristics and resource potentials. In addition to energy-system models, EEG continues to develop and apply MERGE-ETL, an integrated assessment model (Kypreos 2007a,b; Marcucci and Turton 2012). To support the groups modeling activities, EEG is a member of the Energy Technology Systems Analysis Programme (ETSAP) of the International Energy Agency (IEA). The ETSAP program supports the development of the MARKAL/TIMES family of models and related user interfaces and data management tools.
Overview of tools and frameworks...
The models currently used at EEG are as follows:
- The Swiss TIMES energy system model (STEM)
- European Swiss TIMES Electricity Model (EUSTEM)
- Global Multi-regional MARKAL (GMM) model
- Integration of the GMM model and MCDA (GMM-MCDA)
- The global integrated assessment MERGE-ETL model
- The Swiss energy-system MARKAL model (SMM)
- The European energy-conversion MARKAL (EuroMM) model
- Coupling energy-system models and macro-economic models
- Game-theoretic and stochastic programming models
The Swiss TIMES energy system model (STEM)In the Swiss TIMES energy system model (STEM), the full energy system is depicted from resource supply to end-use energy service demands (ESDs), such as space heating, mechanical processes, and personal/freight transport (in vehicle- or tonne-kilometre). The model represents a broad suite of energy and emission commodities, technologies and infrastructure as illustrated in the reference energy system below. The model also combines a long time horizon (2010-2100) with an hourly representation of weekdays and weekends in three seasons. The model is used to identify the least-cost combination of technologies and fuels to meet future ESDs (which are given exogenously based on a set of scenario drivers), while fulfilling other technical, environmental and policy constraints (e.g. CO2 mitigation policy). The model outputs include technology investment and energy commodity use across all sectors, which can be aggregated to report primary energy supply and final energy consumption, seasonal/daily/hourly electricity demand and supply by technology type, carbon dioxide (CO2) emissions, cost of energy supplies, and the marginal cost of energy and emission commodities, among others.
See Model documentation and publications for details
European Swiss TIMES Electricity Model (EUSTEM)EUSTEM is a multiregional electricity model of Europe. It is an extension of the Cross border Swiss TIMES electricity model (CRoSSTEM), by extending the geographical scope to include wider EU electricity markets (as shown in Figure). The model covers 90% of the total installed capacity and 95% of the total electricity generation of EU-28 + Switzerland and Norway. EUSTEM optimises electricity supply for an exogenously given electricity demand over a long time horizon (to account for long term policy goals and investment decisions) while simultaneously representing sufficient intra-annual detail (i.e., seasonal, weekly and hourly) to account for variations in electricity supply and demand. The model encompasses a wide range of electricity generation technologies and related electricity and environmental policies of all 11 regions. EUSTEM was developed using the TIMES framework.
The regions in the EUSTEM model
See Model documentation and publications for details
Global Multi-regional MARKAL (GMM) modelThe global multi-regional MARKAL (GMM) model provides a long-term (2100) bottom-up representation of the global energy system, with a detailed representation of energy supply technologies and an aggregate representation of demand technologies. The model has recently been disaggregated into 15 world regions. Specific assumptions on the dynamics of technology characteristics, resource availability and demands are applied for each region. GMM has been developed sequentially over several years at PSI, with specific studies focusing on electricity sector, technology learning, hydrogen, biofuels and transportation (Barreto 2001; Rafaj 2005; Gül et al. 2009; Densing et al. 2011). The model has been used for analysis of long-term transportation scenarios, with Volkswagen and recently in an ongoing partnership with the World Energy Council (WEC) (Densing and Turton 2011).
The 15 regions depicted in the GMM model
Integration of the GMM model and MCDA (GMM-MCDA)PSI's Global Multi-regional MARKAL (GMM) model is extended by the integration of Multi-criteria Decision Analysis (MCDA) indicators.
More information is provided on the GMM-MCDA project website.
The global integrated assessment MERGE-ETL modelMERGE-ETL is an integrated assessment model combining a bottom-up description of the energy system disaggregated into electric and non-electric sectors, a top-down model based on a macroeconomic production function, and a simplified climate cycle. This model has been progressively developed by PSI from the MERGE model of Manne et al. (1995)). The integrated approach in MERGE-ETL accounts for linkages between economic activity and the energy sector, such that the model determines endogenously energy demands, prices, technology choice and economic output. In addition, technological learning is represented in MERGE-ETL by two-factor learning curves for technology investment costs, applying the paradigm of technology clusters described in Seebregts et al. (2000) (Kypreos 2007; Magne et al. 2010; Marcucci and Turton 2011). The regional disaggregation in MERGE-ETL has been revised recently to provide a more contemporary representation of important political groupings (Marcucci, 2012).
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.
ReferencesKypreos, 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.