ASSUME - Agent-Based Electricity Markets Simulation Toolbox
Project description
ASSUME: Agent-Based Electricity Markets Simulation Toolbox
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.
Introduction
A unique feature of the ASSUME toolbox is its integration of Deep Reinforcement Learning methods into the behavioral strategies of market agents. The model offers various predefined agent representations for both the demand and generation sides, which can be used as plug-and-play modules, simplifying the reinforcement of learning strategies. This setup enables research into new market designs and dynamics in energy markets.
Documentation
Installation
You can install ASSUME using pip. Choose the appropriate installation method based on your needs:
Using pip
To install the core package:
pip install assume-framework
To install with testing capabilities:
pip install assume-framework[test]
Timescale Database and Grafana Dashboards
If you want to benefit from a supported database and integrated Grafana dashboards for scenario analysis, you can use the provided Docker Compose file.
Follow these steps:
- Clone the repository and navigate to its directory:
git clone https://github.com/assume-framework/assume.git
cd assume
- Start the database and Grafana using the following command:
docker-compose up -d
This will launch a container for TimescaleDB and Grafana with preconfigured dashboards for analysis. You can access the Grafana dashboards at http://localhost:3000
.
Using Learning Capabilities
If you intend to use the reinforcement learning capabilities of ASSUME and train your agents, make sure to install Torch. Detailed installation instructions can be found here.
Trying out the Examples
To explore the provided examples, follow these steps:
- Clone the repository and navigate to its directory:
git clone https://github.com/assume-framework/assume.git
cd assume
- Quick Start:
There are three ways to run a simulation:
- Local:
python examples/examples.py
- Using the provided Docker setup:
If you have installed Docker and set up the Docker Compose file previously, you can select 'timescale' in examples.py
before running the simulation. This will save the simulation results in a Timescale database, and you can access the Dashboard at http://localhost:3000
.
- Using the CLI to run simulations:
assume -s example_01b -db "postgresql://assume:assume@localhost:5432/assume"
For additional CLI options, run assume -h
.
Development
If you're contributing to the development of ASSUME, follow these steps:
- Install pre-commit:
pip install pre-commit
pre-commit install
To run pre-commit checks directly, use:
pre-commit run --all-files
Creating Documentation
First, create an environment that includes the documentation dependencies:
conda env create -f environment_docs.yaml
To generate or update the automatically created docs in docs/source/assume*
, run:
sphinx-apidoc -o docs/source -Fa assume
To create and serve the documentation locally, use:
make -C docs html && python -m http.server --directory docs/build/html
Contributors and Funding
The project is developed by a collaborative team of researchers from INATECH at the University of Freiburg, IISM at Karlsruhe Institute of Technology, Fraunhofer Institute for Systems and Innovation Research, Fraunhofer Institution for Energy Infrastructures and Geothermal Energy, and FH Aachen - University of Applied Sciences. Each contributor brings valuable expertise in electricity market modeling, deep reinforcement learning, demand side flexibility, and infrastructure modeling.
ASSUME is funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK). We are grateful for their support in making this project possible.
License
Copyright 2022-2023 ASSUME developers.
ASSUME is licensed under the GNU Affero General Public License v3.0. This license is a strong copyleft license that requires that any derivative work be licensed under the same terms as the original work. It is approved by the Open Source Initiative.
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