  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. RL-ADN is available at:
https://github.com/ShengrenHou/RL-ADN and
https://github.com/distributionnetworksTUDelft/RL-ADN.
