# RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks

arXiv: https://arxiv.org/abs/2408.03685 · HTML: https://mlsanity.com/paper/2408.03685/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management](https://mlsanity.com/paper/2012.10504/) (2020)
- [Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems](https://mlsanity.com/paper/2103.07932/) (2021)
- [PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2111.05969/) (2021)
- [GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management](https://mlsanity.com/paper/2110.06396/) (2021)
- [Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm](https://mlsanity.com/paper/2305.05484/) (2023)
- [Design and implementation of an environment for Learning to Run a Power Network (L2RPN)](https://mlsanity.com/paper/2104.04080/) (2021)
- [Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2203.01292/) (2022)
- [Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling](https://mlsanity.com/paper/2208.00728/) (2022)
- [Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control](https://mlsanity.com/paper/2005.04869/) (2020)
- [Decentralized Safe Reinforcement Learning for Voltage Control](https://mlsanity.com/paper/2110.01126/) (2021)
- [A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch](https://mlsanity.com/paper/2307.14304/) (2023)
- [Tensor Power Flow Formulations for Multidimensional Analyses in Distribution Systems](https://mlsanity.com/paper/2403.04578/) (2024)
- [A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles](https://mlsanity.com/paper/2202.11514/) (2022)

## Built on by (most cited first, 4 of 4)

- [Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks](https://mlsanity.com/paper/2411.00995/) (2024)
- [Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints](https://mlsanity.com/paper/2510.12335/) (2025)
- [MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16063/) (2026)
- [Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks](https://mlsanity.com/paper/2603.26264/) (2026)

## Abstract (arXiv)

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.