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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
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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.
Based on Python, Pyomo. Using PostgreSQL for data processing.
Website / Documentation
Download
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 Open Source
Affero General Public License v3 (AGPL-3.0)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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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
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dispatch
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| Regions
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depending on input data
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| Geographic Resolution
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NUTS0 - NUTS3, for DE
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| Time resolution
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15 Minute
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| Network coverage
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transmission, distribution
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| 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
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| Computation time
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minutes
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| Objective
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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
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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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