ASSUME - Revision history

Florian Maurer at 12:53, 17 April 2024

2024-04-17T12:53:24Z

Florian Maurer at 12:04, 17 April 2024

2024-04-17T12:04:05Z

Florian Maurer at 12:01, 17 April 2024

2024-04-17T12:01:24Z

Florian Maurer at 12:01, 17 April 2024

2024-04-17T12:01:10Z

Florian Maurer at 11:56, 17 April 2024

2024-04-17T11:56:53Z

Florian Maurer: Add ASSUME project

2024-04-17T11:53:34Z

Add ASSUME project

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{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=pip install assume-framework
|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.
|Primary outputs= electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution, Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|example_research_questions=How can different energy market designs be modelled in
|Model validation=benchmark to entsoe
|Specific properties=reinforcement learning, RL, interoperability, market abstraction
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

@@ Line 53: / Line 53: @@
 |Additional dimensions (Other)=grid congestion
 |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, complex bids
-market behavior,
+* bid marginal cost
-pay as bid, pay as clear, redispatch, nodal pricing
+* complex bids
 |math_objective=Minimize cost, optimize dispatch per agent
 |deterministic=Deterministic
@@ Line 69: / Line 75: @@
 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

@@ Line 67: / Line 67: @@
 |citation_doi=https://doi.org/10.5281/zenodo.8088760
 |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?

@@ Line 9: / Line 9: @@
 |source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
 |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
 |Support=OpenMod Forum, GitHub Issues
@@ Line 35: / Line 35: @@
 |Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
 |Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
-|Transfer (Electricity)=Distribution, Transmission
+|Transfer (Electricity)=Transmission
 |Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
 |Storage (Gas)=No

@@ Line 1: / Line 1: @@
 {{Model
 |Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
 |Acronym=ASSUME
 |author_institution=INATECH Freiburg
@@ Line 7: / Line 7: @@
 |contact_email=contact@assume-project.de
 |website=https://assume-project.de/
-|source_download=pip install assume-framework
+|source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
 |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.
-|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
 |Framework=mango-agents
@@ Line 21: / Line 21: @@
 |license=Affero General Public License v3 (AGPL-3.0)
 |model_source_public=Yes
-|Link to source=https://github.com/assume-framework/assume/
+|Link to source=https://github.com/assume-framework/assume/releases
 |data_availability=all
 |open_future=No
@@ Line 28: / Line 28: @@
 |External optimizer=GLPK, CBC, Gurobi, C-Plex
 |GUI=No
 |model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
 |sectors=All / Electricity,
 |technologies=Renewables, Conventional Generation, CHP
 |Demand sectors=Households, Industry, Commercial sector
@@ Line 70: / Line 70: @@
 |citation_references=Zenodo
 |citation_doi=https://doi.org/10.5281/zenodo.8088760
 |example_research_questions=How can different energy market designs be modelled in
-|Model validation=benchmark to entsoe
+|Model validation=benchmark to entsoe, comparison of real dispatch
 |Specific properties=reinforcement learning, RL, interoperability, market abstraction
 |Integrated models=PyPSA, AMIRIS