RL-ADN

RL-ADN is a Python library for deep reinforcement learning research on energy storage dispatch in active distribution networks. It packages network data, environment logic, baseline optimization code, and fast Laurent power-flow utilities used in the accompanying research line.

Quickstart

Install runtime dependencies:

py -3 -m pip install -r requirements.txt

Install the development toolchain:

py -3 -m pip install -r requirements-dev.txt

Run the lightweight test suite:

py -3 -m pytest tests -q -m "not powerflow"

Some power-flow validation tests require pandapower. If it is not installed, those tests are skipped automatically.

First Import

from rl_adn import PowerNetEnv, make_env_config

config = make_env_config()
env = PowerNetEnv(config)
state = env.reset()

Run the script-style quickstart:

py -3 examples/quickstart_env.py

Repository Structure

• rl_adn/: package source code
• tests/: smoke and domain validation tests
• examples/: script-first quickstart plus notebooks
• docs/: Sphinx documentation sources

Highlights

• Flexible active distribution network environment modeling
• Laurent power flow solver for faster training-time simulation
• DRL algorithms and optimization baselines in the same repository
• Bundled network and time-series datasets for reproducible experiments

Background

The library was originally released alongside the RL-ADN research paper on optimal battery dispatch in distribution networks. The codex/develop branch is being used to harden the repository into a cleaner long-lived development branch for future extensions.

Recommended Learning Path

1. Run examples/quickstart_env.py for the minimal package-backed environment flow.
2. Open examples/Customize_env.ipynb to understand configuration customization.
3. Open the DDPG training notebook once the environment baseline is clear.

More Detail

For tutorial-style material, see the project wiki.