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RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks
Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents. Additionally, RL-ADN incorporates the Laurent power flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy. The effectiveness of RL-ADN is demonstrated using in different sizes of distribution networks, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. This enhancement is particularly beneficial from the increased diversity of training scenarios. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly.
- Versatile Benchmarking: Model diverse energy arbitrage tasks with full flexibility.
- Laurent Power Flow: Over 10 times faster computational speed compared to traditional methods.
- Seamless Transition: Designed for both simulated environments and real-world applications.
- Open-source: Easily accessible for modifications, customizations, and further research.
To install RL-ADN, simply run:
pip install RL-ADN
Or install from git
git clone https://github.com/shengrenhou/RL-ADN.git
cd your repository
pip install -e .
Or install locally, if you have downloaded the source code
cd to the path contains setup.py
python setup.py install
Version History [click to expand]
For more details, visit the File Structure and Tutorials pages.
Preparing the manuscript
Potential direction: 1. more realistic ESSs model.
MIT License
Disclaimer: We are sharing codes for academic purposes under the MIT education license. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.