|
|
| (4 intermediate revisions by one user not shown) |
| Line 1: |
Line 1: |
| | {{Model | | {{Model |
| − | |Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution | + | |Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution |
| | |Acronym=ASSUME | | |Acronym=ASSUME |
| | |author_institution=INATECH Freiburg | | |author_institution=INATECH Freiburg |
| Line 7: |
Line 7: |
| | |contact_email=contact@assume-project.de | | |contact_email=contact@assume-project.de |
| | |website=https://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 | | |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 |
| | |Framework=mango-agents | | |Framework=mango-agents |
| Line 21: |
Line 21: |
| | |license=Affero General Public License v3 (AGPL-3.0) | | |license=Affero General Public License v3 (AGPL-3.0) |
| | |model_source_public=Yes | | |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 | | |data_availability=all |
| | |open_future=No | | |open_future=No |
| Line 28: |
Line 28: |
| | |External optimizer=GLPK, CBC, Gurobi, C-Plex | | |External optimizer=GLPK, CBC, Gurobi, C-Plex |
| | |GUI=No | | |GUI=No |
| − | |model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model, | + | |model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model, |
| − | |sectors=All / Electricity, | + | |sectors=All / Electricity, |
| | |technologies=Renewables, Conventional Generation, CHP | | |technologies=Renewables, Conventional Generation, CHP |
| | |Demand sectors=Households, Industry, Commercial sector | | |Demand sectors=Households, Industry, Commercial sector |
| Line 35: |
Line 35: |
| | |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 |
| Line 53: |
Line 53: |
| | |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
| + | |
| − | - complex bids
| + | * bid marginal cost |
| | + | * complex bids |
| | | | |
| | market behavior: | | market behavior: |
| − | - pay as bid
| + | |
| − | - pay as clear
| + | * pay as bid |
| − | - redispatch
| + | * pay as clear |
| − | - nodal pricing
| + | * 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: |
Line 72: |
| | |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 |
| − | |Model validation=benchmark to entsoe | + | |
| − | |Specific properties=reinforcement learning, RL, interoperability, market abstraction | + | 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 |
| | + | |Specific properties=reinforcement learning, RL, interoperability, market abstraction, multiple markets |
| | |Integrated models=PyPSA, AMIRIS | | |Integrated models=PyPSA, AMIRIS |
| | |Interfaces=CSV, PostgreSQL | | |Interfaces=CSV, PostgreSQL |
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?
