Edit Model: MultiMod

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Basic Information

Full model name:	The name of the model in full, not the abbreviation.
Acronym:	The short name of the model, this should be the same as the page title.
Author institution:	The organization that hosts the project. Select the lead institution if more that one organization is involved.
Authors:	A comma-separated list of "Firstname Lastname" pairs. For example: John Doe, Jill Doe, Max Mustermann
Current contact persons:	A comma-separated list of "Firstname Lastname" pairs. For example: John Doe, Jill Doe, Max Mustermann
Contact email address:	The email address of one contact person.
Website:	The website URL including the "http://" part. Ideally a link to the landing page for the project and not the homepage of the institution hosting the project.
Source download link:	The download URL including the "http://" part.
Logo:	Upload file The logo used by the project, if any.
Short description:	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 endogenous substitution between fuels, infrastructure constraints and endogenous investment (e.g., pipeline capacity, power generation technologies), as well as market power by producers of fossil fuels in a unified framework. The mathematical framework of the MultiMod model is a dynamic Generalized Nash Equilibrium (GNE) derived from individual players' profit maximisation problems. The formulation is generic and flexible, so that the supply chain of any number of fossil and renewable fuels can be modelled. The framework includes seasonality and allows for a detailed infrastructure representation and a comprehensive transformation sector. Investment in infrastructure (transportation, seasonal storage, transformation) is determined endogenously in the model according to the respective player’s inter-temporal optimisation problem. Furthermore, substitution between different energy carriers on the final demand side is endogenous. Modelling co-production of fuels (e.g. crude oil and associated gas) is possible, as well as a flexible setup of transformation units (multiple inputs, multiple outputs). By formulating the model as an equilibrium problem derived from 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. For the model description paper, a database representing the global energy system was compiled and used for scenario analysis (Huppmann & Egging, 2014). New datasets or variations on the initial data base are currently under development within specific research projects: - Focus on US domestic conventional crude and shale oil infrastructure (lead: Johns Hopkins University) - Focus on Chinese coal policies (lead: Tsinghua University) - Focus on the global crude oil market and refinery investment (lead: DIW Berlin/TU Berlin) 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. The code package includes a number of auxiliary routines and algorithms that greatly facilitate the compilation of the data set as well as calibration of the model. Describe your project in a few short sentences.
Primary purpose:	Record the primary purpose of your project.
Primary outputs:	List the primary outputs you expect from your project.
Support:	TOFIX
Framework:	TOFIX
Link to user documentation:	Add a URL to the user documentation you offer.
Link to code documentation:	Add a URL to the developer documentation you offer.
Source of funding:	List your funding organizations, if appropriate.
Number of developers:	Add the number of developers working on the project.
Number of users:	Add the number of modelers using the project. Modelers can be developers too.

Openness

Open Source licensed?	Is the model published under an open source license? We regard licenses from OSI (opensource.org) and The Open Definition (opendefinition.org) as open source. Please contact us if you are using another license. If you leave this dialog unchecked, this implies you are planning to release your model under an open license in due course.
If so, what license?
Source code directly downloadable?	Yes means that the source code can be downloaded anonymously and that registration or manual inquiry is not required.
Link to source code:
Data provided?	None all some Indicate if some or all of the datasets necessary to run the model are shipped alongside the model.
Planned to open up in the future?	Check this dialog if the model and data are not fully open at present, but you plan to open these up in the future. This field is ignored if the open source license field is set to yes.

Software

Modelling software:	A comma-separated list of the languages and perhaps also the key libraries and/or development environments used for modelling. Do not include solvers, they are the subject of a separate question.
Data processing software:	A comma-separated list of the languages and software used for processing and visualizing data.
External optimizer:	A comma-separated list of the solvers used by the project. If non-proprietary solvers can be deployed, then these should be listed too.
Primary purpose:	TOFIX
Additional software required:	A comma-separated list of additional software and related dependencies that may be required to develop and/or run the model.
GUI:	Yes if a graphical user interface is offered.

Coverage

Model class:	A short description indicating the class and/or scope of the model. For instance: Australian national electricity market, European transmission grid, distributed energy systems, smart grid simulation, web-based interactive, national planning, and so forth. Look at the data provided by existing projects for more hints.
Modeled sectors:	A comma-separated list of the economic sectors that are supported. For example: electricity, heat, gas, oil, mobility, household, carbon sequestration.
Modeled generation technologies:	Renewables Conventional Generation CHP Supported generation technologies. CHP and cogeneration are synomyms.
Demand sectors:	Households Industry Transport Commercial sector Other Supported demand sectors.
Modeled energy carriers (Gas):	Natural gas Biogas Hydrogen Supported energy carriers related to gaseous fuels.
Modeled energy carriers (Liquid):	Diesel Ethanol Petrol Supported energy carriers related to liquid fuels.
Modeled energy carriers (Solid):	Biomass Coal Lignite Uranium Supported energy carriers related to solid fuels.
Modeled energy carriers (Renewable):	Geothermal heat Hydro Sun Wind Supported renewable sources. Sun includes photovoltaic and solar thermal technologies.
Transport (Electricity):	Distribution Transmission Support for low-voltage distribution and high-voltage transmission.
Transport (Gas):	Distribution Transmission Support for low-pressure reticulation and high-pressure transmission.
Transport (Heat):	Distribution Transmission Support for district heat and similar.
Storage (Electricity):	Battery CAES Chemical Kinetic PHS Support for local and grid-based electricity storage. CAES is compressed air energy storage and PHS is pumped hydroelectric storage.
Storage (Gas):	Support for gas storage, either as a gas or liquid.
Storage (Heat):	Support for heat storage.
User behaviour:	A brief description of the representation of consumer behavior, perhaps using techniques from bounded rationality.
Market models:	A brief description of the representation of energy and carbon markets and their type.
Modeled decisions:	dispatch investment Dispatch decisions relate to system operations and investment decisions relate to plant commissioning and decommissioning.
Changes in efficiency:	Are plant efficiencies fixed or can they depend on the state of the environment and/or the plant in question?
Geographic regions covered:	A comma-separated list of regions covered by the model. Leave this blank if the model and data are not limited to a specific region. Examples include: USA, Canada, Germany, western Europe.
Geographic resolution:	The geographic resolution of the model. Examples include: country, federal states, NUTS 3 (a Eurostat designation), TSO regions, individual power stations.
Time resolution:	The timestep normally used by the model.
Network coverage:	transmission distribution AC load flow DC load flow net transfer capacities How are engineering networks represented for in your model? Multiple answers are possible. AC load flow refers to AC transmission and DC load flow refers to DC transmission.
Observation period:	Less than one month Less than one year More than one year The time horizon normally used by a model.
Additional dimensions (Ecological):	Can the model calculate and report greenhouse gas emissions, air pollution, water demand, and direct and indirect land use?
Additional dimensions (Economic):	Can the model calculate and report economic statistics, including: aggregate financial cost, net-present value, spot price time series?
Additional dimensions (Social):	Can the model estimate and report social statistics, such as energy sector employment?
Additional dimensions (Other):	Any other dimensions of significance.

Mathematical properties

Model type:	Optimization Simulation Other Agent-based Multiple answers are possible.
Short description of mathematical model type:	Generalized Nash Equilibrium (GNE) model formulated as a Mixed Complementarity Model (MCP) Provide a brief description of the mathematics behind the model. This may range from simple accounting through to intertemporal mathematical optimization.
Math objective:	A comma-separated list of supported system objectives. For example: financial cost, economic welfare, greenhouse gas emissions.
Approach to uncertainty:	How is uncertainty dealt with by your model? Answers might include: deterministic, stochastic, stochastic hydro inflows but otherwise perfect foresight, monte carlo methods, sensitivity analysis.
Suited for many scenarios / monte-carlo:	Is the model able to be used for large-scale scenario analysis with hundreds or thousands of different parameter sets being investigated?
Number of variables:	How many variables might a typical model have? Please enter a number without special letters or characters.
Supports Monte Carlo simulation:	Does the model support Monte Carlo methods?
Computation time minutes:	How long might a typical model take to solve in minutes? Please enter a number without special letters or characters.
Hardware used for computation time:	A brief description of the hardware used to establish runtime benchmarks.
Comments regarding computation time:	You can add a comment regarding the computation time. For example, you could explain under what circumstances the given run time time would be typical.

References

Citation reference:	A single publication which can be used to cite the model. For example: Doe, John (2014). "An electricity market model for Europe", Journal of Modeling Sciences, Vol 10, Issue 80. The DOI is given next.
Citation doi:	The DOI identifier for the given reference, if available.
References to reports produced using the model:	Currently used within EMF 31 (http://emf.stanford.edu) References to reports produced using the model.
Example research questions:	Scenarios regarding North American shale gas development, Russian supply disruption to Europe, evaluation of renewable support measures (feed-in tariffs vs. emission quota) Model variations (forks) used for other research projects by international partners (see short description for details) A selection of research questions that the model is capable of answering.
Larger scale usage:	TOFIX
Model validation:	List any procedures used to help ensure that the model is valid.
Comment on model validation:	Comment on procedures used to help ensure the model is valid.
Model specific properties:	List any properties that are special or unique to your model.

Model integration

Integrated models:	TOFIX
Interfaces:	List the interfaces that your model can offer to other models.
Integrating models:	TOFIX
Model input file format:	Briefly specify the data input format used by your model. For example: structured text, XML, CSV, JSON, pandas data structures, and Excel.
Model file format:	TOFIX
Model output file format:	Briefly specify the data output format used by your model.

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Edit Model: MultiMod

Basic Information

Openness

Software

Coverage

Mathematical properties

References

Model integration

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About

Help

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Search

Interaction

Toolbox

Namespaces

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Actions

Edit Model: MultiMod

Basic Information

Openness

Software

Coverage

Mathematical properties

References

Model integration

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