| Name | Agent-based Simulation for Studying and Understanding Market Evolution |
| Acronym | ASSUME |
| Methodical Focus | Agent-Based modeling |
| Institution(s) | INATECH; Universität Freiburg, IISM; KIT Karlsruhe, Fraunhofer Institute for Systems and Innovation Research, Fraunhofer Institution for Energy Infrastructures and Geothermal Energy, FH Aachen |
| Author(s) (institution, working field, active time period) | Nick Harder; INATECH Freiburg, Kim Miskiw; IISM; KIT, Florian Maurer; FH Aachen, Manish Khanra; Fraunhofer ISI, Patil Parag; Fraunhofer IEG |
| Current contact person | Nick Harder |
| Contact (e-mail) | contact@assume-project.de |
| Website | https://assume-project.de/ |
| Logo |
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| Primary Purpose | Reinforcement learning Market design comparison |
| Primary Outputs | Timeseries data in CSV or Timescale DB Analysis Dashboard of different Scenario Runs reusable Reinforcement learning models |
| Support / Community / Forum | |
| Framework | mango-agents |
| Link to User Documentation | https://assume.readthedocs.io |
| Link to Developer/Code Documentation | https://assume.readthedocs.io |
| Documentation quality | good |
| Source of funding | - |
| Number of developers | less than 10 |
| Number of users | less than 10 |
| Open Source | |
| License | GNU Affero General Public License v3.0 |
| Source code available | |
| GitHub | |
| Access to source code | https://github.com/assume-framework/assume |
| Data provided | all data |
| Collaborative programming |
| GitHub Organisation |
| Modelling software | Python |
| Internal data processing software | |
| External optimizer | |
| Additional software | |
| GUI |
| Modeled energy sectors (final energy) |
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| Modeled demand sectors |
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| Modeled technologies: components for power generation or conversion |
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| Modeled technologies: components for transfer, infrastructure or grid |
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| Properties electrical grid |
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| Modeled technologies: components for storage |
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| User behaviour and demand side management | |||||||
| Changes in efficiency | |||||||
| Market models | - | ||||||
| Geographical coverage | |||||||
| Geographic (spatial) resolution |
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| Time resolution | - | ||||||
| Comment on geographic (spatial) resolution | |||||||
| Observation period | - | ||||||
| Additional dimensions (sector) |
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| Model class (optimisation) | - |
| Model class (simulation) | Agent-based, Bottom up |
| Other | |
| Short description of mathematical model class | |
| Mathematical objective | costs |
| Approach to uncertainty | Deterministic, Reinforcement Learning |
| Suited for many scenarios / monte-carlo | |
| typical computation time | less than an hour |
| Typical computation hardware | Intel Core i5-8265U, 16GB RAM |
| Technical data anchored in the model | - |
| Interfaces | Scenario Loaders: PyPSA, AMIRIS, CSV Outputs: SQLAlchemy, SQLite, PostgreSQL, MariaDB, CSV Visualization: Grafana, Python |
| Model file format | .py |
| Input data file format | .csv |
| Output data file format | .csv |
| Integration with other models | AMIRIS: https://doi.org/10.1109/EEM60825.2024.10609021 |
| Integration of other models | mango-agents: https://github.com/OFFIS-DAI/mango |
| Citation reference | - |
| Citation DOI | 10.1016/j.softx.2025.102176, https://doi.org/10.1016/j.egyai.2023.100295, https://doi.org/10.1186/s42162-023-00293-0, https://doi.org/10.1109/EEM60825.2024.10609021 |
| Reference Studies/Models | - |
| Example research questions | - |
| Model usage | KIT, University Freiburg, FH Aachen, Fraunhofer IEG, Fraunhofer ISI |
| Model validation |
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| Example research questions | - |
| further properties | |
| Model specific properties | - |