|
|
| 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
| + | 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: |
Line 66: |
| | |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 |
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?
