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| | |source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main | | |source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main |
| | |logo=assume-project.png | | |logo=assume-project.png |
| − | |text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community. | + | |text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup and Reinforcement Learning. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community. |
| | |Primary outputs=electricity prices, power plant dispatch, cost and income | | |Primary outputs=electricity prices, power plant dispatch, cost and income |
| | |Support=OpenMod Forum, GitHub Issues | | |Support=OpenMod Forum, GitHub Issues |
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| | |Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium | | |Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium |
| | |Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind | | |Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind |
| − | |Transfer (Electricity)=Distribution, Transmission | + | |Transfer (Electricity)=Transmission |
| | |Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS | | |Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS |
| | |Storage (Gas)=No | | |Storage (Gas)=No |
Latest revision as of 12:53, 17 April 2024
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Agent-based Simulation for Studying and Understanding Market Evolution
by INATECH Freiburg
Authors:
Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
Contact:
Nick Harder
|
ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup and Reinforcement Learning. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
Based on Python, Pyomo. Using PostgreSQL for data processing.
Website / Documentation
Download
|
 Open Source
Affero General Public License v3 (AGPL-3.0)
Directly downloadable
Input data shipped
|
| Model Scope |
Model type and solution approach |
| Model class
|
German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model
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| Sectors
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All / Electricity
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| Technologies
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Renewables, Conventional Generation, CHP
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| Decisions
|
dispatch
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| Regions
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depending on input data
|
| Geographic Resolution
|
NUTS0 - NUTS3, for DE
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| Time resolution
|
15 Minute
|
| Network coverage
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transmission, distribution
|
|
| Model type
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Simulation, Agent-based
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|
|
depending on parameterization bidding behavior and market behavior can be defined.
bidding behavior:
- bid marginal cost
- complex bids
market behavior:
- pay as bid
- pay as clear
- redispatch
- nodal pricing
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| Variables
|
|
| Computation time
|
minutes
|
| Objective
|
Minimize cost, optimize dispatch per agent
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| Uncertainty modeling
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Deterministic
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| Suited for many scenarios / monte-carlo
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No
|
|
References
Scientific references
Zenodo
https://dx.doi.org/https://doi.org/10.5281/zenodo.8088760
Reports produced using the model
https://doi.org/10.1007/978-3-031-48652-4_10
https://doi.org/10.1016/j.egyai.2023.100295
Example research questions
What influence does the availability of different order types on a market have?
How can deep reinforcement learning for multiple markets be implemented in software?
What is the best way for demand-side management to be implemented in bidding agents?
How can different energy market designs be modelled in energy market simulations?
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