Here you can find some of the quantitative models developed in the Energy, Transportation, Environment Department. In connection with quantiative modeling, various types of energy market data are collected and maintained in the department.
The Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) has initially been developed to study the role of electricity storage and other flexibility options in greenfield scenarios with high shares of variable renewable energy sources. The model minimizes overall system costs in a long-run equilibrium setting, determining least-cost capacity expansion and use of various generation and flexibility technologies. DIETER can capture multiple system benefits of electricity storage related to capacity, energy arbitrage and reserve provision.
DIETER is an open source model which may be freely used and modified by anyone. The code is licensed under the MIT License. Input data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License. To view a copy of these licenses, click here and here. Whenever you use this model, please refer to http://www.diw.de/dieter. We are happy to receive your feedback.
The model is implemented in the General Algebraic Modeling System (GAMS). Running the model thus requires a GAMS system, an LP solver, and the respective licenses. We use the commercial solver CPLEX, but other LP solvers work as well.
Below you find an overview of available DIETER versions and respective academic papers that include descriptions and documentations. The ZIP files include the GAMS code, an Excel file with all necessary input parameters, and partly also a short documentation of model equations and changes compared to earlier versions.
Future Versions of DIETER will also be made available on this page.
Version 1.0.0 is used and documented in Zerrahn, A., Schill, W.-P. (2015): A greenfield model to evaluate long-run power storage requirements for high shares of renewables. DIW Discussion Paper 1457.
Version 1.0.2 introduces a few minor modifications. These include nomenclature adjustments, changes with respect to the modeling of primary reserves, and some corrections regarding reserve provision by storage technologies. The ZIP file includes an updated model description compared to the one included in DIW Discussion Paper 1457.
This model version has been documented in an article published in the Journal Renewable and Sustainable Energy Reviews. See http://dx.doi.org/10.1016/j.rser.2016.11.098. In a companion article, this model version has been applied to the analysis of long-run power storage requirements in a greenfield setting that is loosely calibrated to the German power system. See http://dx.doi.org/10.1016/j.rser.2017.05.205.
Version 1.1.0 introduces power system interactions of electric vehicles. In contrast to earlier versions, input data is not calibrated to the year 2050, but to 2035.
This model version has been used to study the provision of reserves by electric vehicles in Germany. See Schill, W.-P., Niemeyer, M., Zerrahn, A., Diekmann, J. (2016): Bereitstellung von Regelleistung durch Elektrofahrzeuge: Modellrechnungen für Deutschland im Jahr 2035. Zeitschrift für Energiewirtschaft 40(2), 73-67
Version 1.2.0 introduces a stylized representation of prosumage, i.e. households that make use of decentralized PV-battery systems to increase their self-consumption solar energy.
This model version has been used to study electricity sector effects of prosumage in Germany in future scenarios of the year 2035.
See Schill, W.-P., Zerrahn, A., Kunz, F. (2017): Prosumage of solar electricity: pros, cons, and the system perspective. Economics of Energy & Environmental Policy, 6(1). A slightly more detailed version of this article, including a description of the augmented model, is available as DIW Discussion Paper 1637
Version 1.3.0 introduces residential space heating with a focus on different types of electric heating (power-to-heat). This version also introduces a spatial resolution that allows for geographic coverage beyond Germany.
This model version has been used to study the system effects of making existing electric storage heaters in Germany more flexible in the year 2030. See Schill, W.-P. and Zerrahn, A. (2020): Flexible electricity use for heating in markets with renewable energy. Applied Energy 266, 114571].
A German publication with a policy focus is published as Schill, W.-P., Zerrahn, A., May, N., Neuhoff, K. (2018): Flexible Nutzung von Nachtspeicherheizungen kann ein kleiner Baustein für die Energiewende sein. DIW Wochenbericht 46/2018, 988-995.
Version 1.3.1 introduces a CO2 cap and some minor reporting modifications.
This model version has been used to study the system effects of cheaper stationary batteries in European scenarios of 2030. This has been done in the context of a coordinated multi-model exercise of several European power sector models. Further input data is available on Zenodo (https://doi.org/10.5281/zenodo.4065575).
This is a simplified DIETER version, which was derived from model version 1.3.0 and calibrated to Western Australia. It was used to explore the wider power sector impacts of solar PV battery prosumage in Western Australia.
The model results were published in [Say, K., Schill, W.-P., John, M. (2020): Degrees of displacement: The impact of household PV battery prosumage on utility generation and storage. Applied Energy 276, 115466]
We also developed reduced DIETER versions which were used for more high-level analyses of the role of electricity storage for renerable integration. Reduced DIETER version have been used, amongst others, for the illustrations presented in Schill, W.-P. (2020): Electricity storage and the renewable energy transition. Joule 4, 1-6 and Zerrahn, A., Schill, W.-P., Kemfert, C. (2018): On the economics of electrical storage for variable renewable energy sources. European Economic Review 108, 259-279.
The stylized DIETER versions may also be useful for students or researchers looking for a simple toy model to gain some first experience in the field of energy modeling."
The electricity sector model family ELMOD includes a variety of spatial optimization models with detailed representations of the European electricity sector including the generation portfolio and the physical transmission network. ELMOD models apply a bottom-up approach which combines economic and engineering features of the electricity sector. ELMOD models determine the cost-minimizing or welfare-maximizing dispatch taking into account flows in the high-voltage transmission network using a DC load flow approach as well as technical characteristics of generation units. ELMOD models allow addressing a variety of research questions concerning, e.g., market design, congestion management, and investments in electricity infrastructures. The development of ELMOD was initiated in 2006 by Florian Leuthold, Hannes Weigt, and Christian von Hirschhausen.
The initial ELMOD version and the dataset has been continuously extended, updated, and applied at DIW Berlin and TU Berlin. By now, four members of the ELMOD family have evolved:
ELMOD represents the European electricity system on a nodal level with an hourly time resolution. The model comprises a detailed representation of the European transmission network and the spatial generation and load pattern.
The initial ELMOD formulation is documented and applied in Leuthold, F.U., Weigt, H., Hirschhausen, C. (2012): A Large-Scale Spatial Optimization Model of the European Electricity Market. In: Networks and Spatial Economics. 10, 1, pp. 75-107.
A detailed description of data and data sources for the European electricity system and the handling of data is provided in DIW Data Documentation 72.
ELMOD-DE represents the German electricity system on a nodal level with hourly resolution for the year 2012. It is an open-source model which may be freely used and modified by anyone. The code is licensed under the MIT License. Some of the input data is licensed under the Open Data Commons Open Database License (ODbL). To view a copy of these licenses, click here and here. Whenever you use this model, please refer to http://www.diw.de/elmod. We are happy to receive your feedback.
The model is implemented in the General Algebraic Modeling System (GAMS). Running the model thus requires a GAMS system, an LP solver, and the respective licenses. We use the commercial solver CPLEX, but other LP solvers work as well.
Below you find an overview of available ELMOD-DE versions that include model applications, descriptions, and documentations. The ZIP files include the GAMS code and an Excel file with all necessary input parameters.
The ELMOD-DE model is described in DIW Data Documentation 83
stELMOD is a stochastic optimization model for analyzing the impact of uncertain wind generation on the following day and intraday electricity markets as well as network congestion management. After clearing of the daily following day and the subsequent hourly intraday markets, the final power plant dispatch is determined by the transmission system operator considering network congestion arising from previous market commitments. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure resembling the market process of most European markets.
Below you find an overview of available open-source stELMOD versions. The ZIP file includes the GAMS code and data files with all necessary input parameters. The stELMOD code is licensed under the MIT License. To view a copy of the license, click here. Whenever you use this model, please refer to http://www.diw.de/elmod.
The model is implemented in the General Algebraic Modeling System (GAMS). Running the model thus requires a GAMS system, a MIP solver, and the respective licenses. We use the commercial solver CPLEX.
Model development of stELMOD takes place at GitHub
The model is used and documented in Abrell, J. Kunz, F. (2013): Integrating Intermittent Renewable Wind Generation: A Stochastic Multi-Market Electricity Market. DIW Discussion Paper 1301
The model is applied to cross-border congestion management in Kunz, F., Zerrahn, A. (2016): Coordinating Cross-Country Congestion Management. DIW Discussion Paper 1551
dynELMOD is a multi-period investment model of the European electricity sector until 2050. The model optimizes the electricity generation, storage, and network infrastructure investments by minimizing total system cost, given the policy targets constraints. Investments are determined in five or ten-year steps considering the hourly dispatch of existing and new built generation technologies. Interactions between countries through the interconnected transmission network are incorporated by using either a country-sharp power transfer distribution factor matrix (PTDF) based on the actual transmission network or using net transfer capacities (NTCs) based on commercial cross-border transactions.
The model is documented in the DIW Data Documentation 88 by Clemens Gerbaulet and Casimir Lorenz.
The full source code and data sets can be found in the public repository of dynELMOD.
Recent applications are:
Gerbaulet, C., Kemfert, C., Lorenz, C., von Hirschhausen C. and Oei, P. (2017): Scenarios for decarbonizing the European electricity sector. 14th International Conference on the European Energy Market (EEM), 2017.
Lorenz, C. (2017): Balancing Reserves within a Decarbonized European Electricity System in 2050: From Market Developments to Model Insights. DIW Berlin Discussion Paper 1656.
Gerbaulet, C., Kunz, F., Lorenz, C., von Hirschhausen C. and Reinhard, B., (2014): Cost-minimal investments into conventional generation capacities under a Europe-wide renewables policy. 11th International Conference on the European Energy Market (EEM), Krakow, 2014.
Kemfert, C., Gerbaulet, C., von Hirschhausen, C. Lorenz, C., Reitz, F. (2015): European Climate Targets Achievable without Nuclear Power. DIW Economic Bulletin 47 / 2015.
An extension of dynELMOD also covers interactions with the German individual and district heating sector. It contains additional technologies, such as CHP and "heat only" plants, heat storage as well as power-to-heat to cover heating demands. The full source code and a data set are available here
The Global Gas Model (GGM) was developed and is maintained in cooperation with NTNU (Prof. Ruud Egging).
GGM is a partial equilibrium model of the global natural gas sector (incl. fossil natural gas, shale gas, and LNG). All countries with significant natural gas production, consumption, and transportation are included in the data set. Depending on the data set, up to 100 nodes are distinguished.
Below you find the link to the current open source version of the Global Gas Model. The ZIP file includes the GAMS code and data files with all necessary input parameters. The Global Gas Model code is licensed under the MIT License. Whenever you use this model, please refer to https://www.ntnu.edu/web/iot/ggm.
The model is written as a quadratic program and implemented in the General Algebraic Modeling System (GAMS). Running the model thus requires a GAMS system, an optimization solver, and respective licenses. We use the commercial CPLEX solver. The current GGM data set contains more than 100 nodes and, therefore, requires a sophisticated calibration and is to be handled with caution. Any changes in the code or data should be based on profound economic and modelling knowledge.
The Global Gas Model has been used for several studies of the European and global natural gas markets
The model and data set is documented and explained in: Egging and Holz
The model was used in the SET-Nav project (2016-2019) to investigate natural gas infrastructure requirements in a decarbonizing Europe in Kotek et al. (2018): http://www.set-nav.eu/sites/default/files/common_files/deliverables/WP6/D6.6%20Case%20Study%20on%20PCI%20and%20gas%20producers%20pricing%20strategy.pdf
A stochastic version was used in Egging and Holz (2016) here
OILMOD is a numerical partial equilibrium model of the international crude oil market. The producer-focused model allows us to investigate the consequences of climate and market policies as well as different sector developments such as technical innovation. Crude oil exporting countries are modelled as profit-maximizing suppliers in oligopolistic quantity competition. Different versions of the model feature refinery, node-to-node transportation, endogenous investments, and varying competition setups.
As the crude oil market is dominated by a small number of large producers as well as the presence of the Organization of the Petroleum Exporting Countries (OPEC), the market can hardly be defined as competitive. Therefore, OILMOD allows for a flexible approach of modelling different (asymmetric) competition set-ups by using conjectural variation and an explicit formulation of multi-level frameworks, i.e. sequential decision-making in production quantities.
The model is formulated and solved as a mixed coplanarity problem (MCP) in the General Algebraic Modeling System (GAMS) using the solver PATH, using MS Excel for data processing and output reports. The multi-level formulation of OILMOD is formulated as a mixed-integer nonlinear problem (MINLP) and solved using BONMIN.
Dawud Ansari (2017): OPEC, Saudi Arabia, and the Shale Revolution: Insights from Equilibrium Modelling and Oil Politics. Energy Policy 111, 166–178.
Daniel Huppmann (2013): Endogenous Shifts in OPEC Market Power: A Stackelberg Oligopoly with Fringe. DIW Discussion Paper 1313, 26 S..
Daniel Huppmann and Franziska Holz (2012): Crude Oil Market Power: A Shift in Recent Years?. The Energy Journal 33 (2012), 4, S. 1-22
Zaklan, Aleksandar; Bernoth, Kerstin; Huppmann, Daniel; Kemfert, Claudia; von Hirschhausen, Christian (2011). Entwicklung der Erdölmärkte: Reservekapazität im Nahen Osten wirkt derzeit stabilisierend. DIW Wochenbericht 21/2011
Daniel Huppmann and Franziska Holz (2009): A Model for the Global Crude Oil Market Using a Multi-Pool MCP Approach. The Energy Journal 33 (2012), 4, 1-2
COALMOD-World is a model of the international steam coal market that can be readily used to explore implications of climate policies and to analyze market structure or to investigate issue of supply security. It features a detailed representation of both domestic and international steam coal supply, based on endogenously calculated Cost, Insurance, Fright (CIF) costs, and prices that take into account additional rents. It features endogenous investment into production, land transport, and export capacity, as well as an endogenous mechanism assessing production cost increase due to resource depletion.
Below you find an overview of available open-source COALMOD-World versions. The ZIP file includes the GAMS code and data files with all necessary input parameters. The COALMOD-World code is licensed under the MIT License. To view a copy of the license, click here. Whenever you use this model, please refer to http://www.diw.de/coalmod.
The model is implemented in the General Algebraic Modeling System (GAMS). Running the model thus requires a GAMS system, a MCP solver, and the respective licenses. We use the commercial solver PATH.
The model is used and documented in Holz, F. Haftendorn, C. Mendelevitch, R. Hirschhausen C. v. (2016): A Model of the International Steam Coal Market (COALMOD-World). DIW Data Documentation 85
Earlier versions of the model were applied to test for market power abuse in the international steam coal market in
Haftendorn, C. Holz, F. (2010): Modeling and Analysis of the International Steam Coal Trade. The Energy Journal Vol. 31 No. 3 and later in
Haftendorn, C. (2012): Evidence of Market Power in the Atlantic Steam Coal Market Using Oligopoly Models with a Competitive Fringe. DIW Discussion Paper 1185
Moreover, they were used to study the mid- and long-term effect of climate policies on the international steam coal market in
Haftendorn, C. Kemfert, C. Holz, F. (2012): What about Coal? Interactions between Climate Policies and the Global Steam Coal Market until 2030. Energy Policy Vol. 48
A two stage version of the model is introduced in Richter, P. Mendelevitch, R. Jotzo F. (2015): Market Power Rents and Climate Change Mitigation: A Rationale for Coal Taxes?” DIW Discussion Paper 1471
The current version of the model is used to analyze the effects of different, currently discussed supply-side climate policies for the steam coal market in
Mendelevitch, R. (2016): Testing Supply-Side Climate Policies for the Global Steam Coal Market – Can They Curb Coal Consumption?” DIW Discussion Paper 1604
CCTSMOD is designed to simulate the potential development of a carbon capture, transport and storage (CCTS) infrastructure in Europe.
CCTSMOD has a focus on CO2 transport with an explicit representation of economies of scale. It optimizes the costs of deploying and using CCTS infrastructure.
The model represents the entire CO2 value chain: starting with CO2 emitting facilities which decide whether to purchase CO2 certificates (with exogenous price path) or to invest into a capture facility. Captured CO2 must use a CO2 pipeline system to be transported to CO2 storage. The pipeline system must first be invested in before being operational; costs for both investment and operations are taken into account. Two types of CO2 storage are distinguished: permanent storage (in saline aquifer or depleted hydrocarbon fields) or re-use of CO2 in Enhanced Oil Recovery (EOR). In the case of EOR, CO2 storage does not have a cost but instead a revenue from selling the additionally recovered crude oil.
The model is set-up as a quadratic optimization problem and implemented in GAMS. Several data sets for Europe and European subregions (e.g. the North Sea region) exist.
CCTSMOD was used for an analysis of the role of EOR in kick-starting the development of CCTS infrastructure in Europe in Oei and Mendelevitch (2016) here
The model was used in the SET-Nav project (2016-2019) to investigate the role of CCS in decarbonizing the European industry and electricity sector in Holz et al. (2018): http://www.set-nav.eu/sites/default/files/common_files/deliverables/WP6/Case%20study%20report%20on%20the%20role%20for%20carbon%20capture%2C%20transport%20and%20storage%20in%20electricity%20and%20industry%20in%20the%20future.pdf
The energy system and resource market model "MultiMod" is a large-scale representation of the supply and demand of fossil fuels and renewable energy sources. It captures in a unified framework important energy market features such as endogenous substitution between fuels, infrastructure constraints, and endogenous investment, as well as market power by producers of fossil fuels.
This model was developed within the BMBF-project RESOURCES, in collaboration with NTNU Trondheim. It is updated in the EU Horizon 2020 project SET-Nav. The mathematical formulation of the MultiMod model is a dynamic Generalized Nash Equilibrium (GNE) derived from individual players' profit maximization problems. The formulation is generic and flexible, so that the supply chain of any number of fossil and renewable fuels can be modeled. The framework includes seasonality and allows for detailed infrastructure representation and a comprehensive transformation sector (power generation, refinery sector). Investment in infrastructure (transportation, seasonal storage, transformation) is determined endogenously in the model according to the respective player’s inter-temporal optimization problem. Furthermore, substitution between different energy carriers on the final demand side is endogenous. By formulating the model as an equilibrium problem with different player types based on non-cooperative game theory, the model can incorporate Cournot market power by individual suppliers as well as distinct discount rates by various players concerning their investment. The current framework is an open-loop perfect foresight model. A stochastic version of the model is under development at NTNU Trondheim. This will allow for consideration of uncertainty and distinct risk profiles for individual players along the supply chain, including investment by consumers in energy efficiency.
The model is formulated and solved as a Mixed Complementarity Problem (MCP) and implemented in GAMS, using MS Access and MS Excel for data processing and output reports. Initially, a database representing the global energy system was compiled and used for scenario analysis (Huppmann & Egging, 2014). Other datasets or variations of the initial data base are have later been developed within specific research projects:
Huppmann, D., Egging, R. (2014). Market Power, Fuel Substitution and Infrastructure: A Large-Scale Equilibrium Model of Global Energy Markets. DIW Berlin.
Also published in Energy 75: 483-500, 2014. DOI: 10.1016/j.energy.2014.08.004
R. Egging, D. Huppmann, 2012. Investigating a CO2 tax and a nuclear phase out with a multi-fuel market equilibrium model, IEEE Conference Proceedings, Ninth International Conference on the European Energy Market (EEM), pp.1-8.
Su, Z., Egging, R., Huppmann, D. & Tomasgard, A. 2015. A Multi-stage Multi-Horizon Stochastic Equilibrium Model of Multi-Fuel Energy Markets.
CenSES Working paper 2/2015
L. Langer, D. Huppmann, and F. Holz. Lifting the US crude oil export ban: A numerical partial-equilibrium analysis.
DIW Discussion Paper 1548, 2016
Also published in Energy Policy 97: 258-266. DOI: http://dx.doi.org/10.1016/j.enpol.2016.07.040
Yeh, S., Y. Cai, D. Huppman, P. Bernstein, S. Tuladhar and H. G. Huntington (2016). "North American natural gas and energy markets in transition: insights from global models."
Energy Economics 60: 405-415. DOI: http://dx.doi.org/10.1016/j.eneco.2016.08.021
F. Feijoo, D. Huppmann, L. Sakiyama, and S. Siddiqui. North American natural gas model: Impact of cross-border trade with Mexico.
DIW Discussion Paper 1553, 2016
Also published in Energy 112: 1084-1095. DOI: http://dx.doi.org/10.1016/j.energy.2016.06.133
O. Oke, D. Huppmann, M. Marshall, R. Poulton, and S. Siddiqui. Mitigating environmental and public-safety risks of United States crude-by-rail transport.
DIW Discussion Paper 1575, 2016
The online calculation tool "Atomi-MOD" calculates the economic profitability of the (hypothetical) construction of new Generation III/III+ nuclear power plants described in the DIW Berlin Weekly Report 30/2019 (Ben Wealer et al., “High-priced and dangerous: nuclear power is not an option for the climate-friendly energy mix,” DIW Weekly Report, no. 30 (2019): 512–520) and in more detail in the DIW Discussion Paper 1833 (Ben Wealer et al., “Economics of Nuclear Power Plant Investment – Monte Carlo Simulations of Generation III/III+ Investment Projects,” DIW Discussion Papers, no. 1833 (2019)). All the relevant informations for the result are provided in a simplified calculation Excel tool (called "Atomi-MOD"). In addition, an implementation of the model in the Matlab software is provided for further analyses, with which a large number of variants can be calculated, e.g. by Monte Carlo simulation. A private investment in a Generation III nuclear power plant, as currently under construction at various locations in Europe, will lead to considerable economic losses in the order of several billion €, the electricity production costs are far above the expected wholesale prices. The analysis confirms the consensus in the scientific literature and in energy industry practice that the construction of nuclear power plants is not economically viable even if environmental costs, decommissioning, and final disposal of nuclear waste are neglected.
Ben Wealer et al., “High-priced and dangerous: nuclear power is not an option for the climate-friendly energy mix"
DIW Berlin Weekly Report 30/2019
Ben Wealer et al., “Economics of Nuclear Power Plant Investment – Monte Carlo Simulations of Generation III/III+ Investment Projects,”
DIW Discussion Paper 1833 (2019)