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. R...
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266654682400123X |
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| author | Shengren Hou Shuyi Gao Weijie Xia Edgar Mauricio Salazar Duque Peter Palensky Pedro P. Vergara |
| author_facet | Shengren Hou Shuyi Gao Weijie Xia Edgar Mauricio Salazar Duque Peter Palensky Pedro P. Vergara |
| author_sort | Shengren Hou |
| collection | DOAJ |
| description | 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, achieving an average performance improvement of 21.43%, 1.08%, 2.76%, by augmenting five-year, one-year and three-month data, respectively. Additionally, RL-ADN incorporates the Tensor Power Flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy, maintaining voltage magnitude with an average error not exceeding 0.0001%. The effectiveness of RL-ADN is demonstrated using distribution networks with size varying, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. 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. |
| format | Article |
| id | doaj-art-adbbf11a0dcb480e8af93b366c2ac243 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-adbbf11a0dcb480e8af93b366c2ac2432025-01-27T04:22:19ZengElsevierEnergy and AI2666-54682025-01-0119100457RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networksShengren Hou0Shuyi Gao1Weijie Xia2Edgar Mauricio Salazar Duque3Peter Palensky4Pedro P. Vergara5Department of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, The NetherlandsDepartment of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, The NetherlandsDepartment of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, The NetherlandsEnergy Systems Systems Group, Eindhoven University of Technology, Eindhoven, 5612 AE, The NetherlandsDepartment of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, The NetherlandsDepartment of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, The Netherlands; Corresponding author.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, achieving an average performance improvement of 21.43%, 1.08%, 2.76%, by augmenting five-year, one-year and three-month data, respectively. Additionally, RL-ADN incorporates the Tensor Power Flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy, maintaining voltage magnitude with an average error not exceeding 0.0001%. The effectiveness of RL-ADN is demonstrated using distribution networks with size varying, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. 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.http://www.sciencedirect.com/science/article/pii/S266654682400123XDistribution networksBattery dispatchBattery optimizationMachine learningVoltage regulation |
| spellingShingle | Shengren Hou Shuyi Gao Weijie Xia Edgar Mauricio Salazar Duque Peter Palensky Pedro P. Vergara RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks Energy and AI Distribution networks Battery dispatch Battery optimization Machine learning Voltage regulation |
| title | RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks |
| title_full | RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks |
| title_fullStr | RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks |
| title_full_unstemmed | RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks |
| title_short | RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks |
| title_sort | rl adn a high performance deep reinforcement learning environment for optimal energy storage systems dispatch in active distribution networks |
| topic | Distribution networks Battery dispatch Battery optimization Machine learning Voltage regulation |
| url | http://www.sciencedirect.com/science/article/pii/S266654682400123X |
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