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| Line 53: |
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| | |Additional dimensions (Other)=grid congestion | | |Additional dimensions (Other)=grid congestion |
| | |math_modeltype=Simulation, Agent-based | | |math_modeltype=Simulation, Agent-based |
| − | |math_modeltype_shortdesc=depending on parameterization bidding behavior and market behavior can be defined | + | |math_modeltype_shortdesc=depending on parameterization bidding behavior and market behavior can be defined, |
| | | | |
| − | bidding behavior: | + | bidding behavior, |
| − | - bid marginal cost
| + | bid marginal cost, complex bids |
| − | - complex bids
| + | |
| | | | |
| − | market behavior: | + | market behavior, |
| − | - pay as bid
| + | pay as bid, pay as clear, redispatch, nodal pricing |
| − | - pay as clear
| + | |
| − | - redispatch
| + | |
| − | - nodal pricing
| + | |
| | |math_objective=Minimize cost, optimize dispatch per agent | | |math_objective=Minimize cost, optimize dispatch per agent |
| | |deterministic=Deterministic | | |deterministic=Deterministic |
| Line 70: |
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| | |citation_references=Zenodo | | |citation_references=Zenodo |
| | |citation_doi=https://doi.org/10.5281/zenodo.8088760 | | |citation_doi=https://doi.org/10.5281/zenodo.8088760 |
| − | |example_research_questions=How can different energy market designs be modelled in | + | |report_references=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? |
| | |Model validation=benchmark to entsoe, comparison of real dispatch | | |Model validation=benchmark to entsoe, comparison of real dispatch |
| − | |Specific properties=reinforcement learning, RL, interoperability, market abstraction | + | |Specific properties=reinforcement learning, RL, interoperability, market abstraction, multiple markets |
| | |Integrated models=PyPSA, AMIRIS | | |Integrated models=PyPSA, AMIRIS |
| | |Interfaces=CSV, PostgreSQL | | |Interfaces=CSV, PostgreSQL |
Revision as of 12:01, 17 April 2024
|
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. 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
|
| Sectors
|
All / Electricity
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| Technologies
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Renewables, Conventional Generation, CHP
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| Decisions
|
dispatch
|
| Regions
|
depending on input data
|
| Geographic Resolution
|
NUTS0 - NUTS3, for DE
|
| Time resolution
|
15 Minute
|
| Network coverage
|
transmission, distribution
|
|
| Model type
|
Simulation, Agent-based
|
|
|
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
|
| Variables
|
|
| Computation time
|
minutes
|
| Objective
|
Minimize cost, optimize dispatch per agent
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| Uncertainty modeling
|
Deterministic
|
| Suited for many scenarios / monte-carlo
|
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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