Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks
Abstract As the adoption of electric vehicles (EVs) accelerates, addressing the challenges of large-scale, city-wide optimization becomes critical in ensuring efficient use of charging infrastructure and maintaining electrical grid stability. This study introduces EV-GNN, a novel graph-based solutio...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00457-8 |
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| _version_ | 1849768642545188864 |
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| author | Stavros Orfanoudakis Valentin Robu E. Mauricio Salazar Peter Palensky Pedro P. Vergara |
| author_facet | Stavros Orfanoudakis Valentin Robu E. Mauricio Salazar Peter Palensky Pedro P. Vergara |
| author_sort | Stavros Orfanoudakis |
| collection | DOAJ |
| description | Abstract As the adoption of electric vehicles (EVs) accelerates, addressing the challenges of large-scale, city-wide optimization becomes critical in ensuring efficient use of charging infrastructure and maintaining electrical grid stability. This study introduces EV-GNN, a novel graph-based solution that addresses scalability challenges and captures uncertainties in EV behavior from a Charging Point Operator’s (CPO) perspective. We prove that EV-GNN enhances classic Reinforcement Learning (RL) algorithms’ scalability and sample efficiency by combining an end-to-end Graph Neural Network (GNN) architecture with RL and employing a branch pruning technique. We further demonstrate that the proposed architecture’s flexibility allows it to be combined with most state-of-the-art deep RL algorithms to solve a wide range of problems, including those with continuous, multi-discrete, and discrete action spaces. Extensive experimental evaluations show that EV-GNN significantly outperforms state-of-the-art RL algorithms in scalability and generalization across diverse EV charging scenarios, delivering notable improvements in both small- and large-scale problems. |
| format | Article |
| id | doaj-art-33c69aa306424b8abd6eae4edb4e5ea8 |
| institution | DOAJ |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-33c69aa306424b8abd6eae4edb4e5ea82025-08-20T03:03:42ZengNature PortfolioCommunications Engineering2731-33952025-07-014111210.1038/s44172-025-00457-8Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networksStavros Orfanoudakis0Valentin Robu1E. Mauricio Salazar2Peter Palensky3Pedro P. Vergara4Intelligent Electrical Power Grids, Delft University of TechnologyIntelligent & Autonomous Systems Group, Centrum Wiskunde & Informatica (CWI)Electrical Energy Systems, Eindhoven University of Technology (TU/e)Intelligent Electrical Power Grids, Delft University of TechnologyIntelligent Electrical Power Grids, Delft University of TechnologyAbstract As the adoption of electric vehicles (EVs) accelerates, addressing the challenges of large-scale, city-wide optimization becomes critical in ensuring efficient use of charging infrastructure and maintaining electrical grid stability. This study introduces EV-GNN, a novel graph-based solution that addresses scalability challenges and captures uncertainties in EV behavior from a Charging Point Operator’s (CPO) perspective. We prove that EV-GNN enhances classic Reinforcement Learning (RL) algorithms’ scalability and sample efficiency by combining an end-to-end Graph Neural Network (GNN) architecture with RL and employing a branch pruning technique. We further demonstrate that the proposed architecture’s flexibility allows it to be combined with most state-of-the-art deep RL algorithms to solve a wide range of problems, including those with continuous, multi-discrete, and discrete action spaces. Extensive experimental evaluations show that EV-GNN significantly outperforms state-of-the-art RL algorithms in scalability and generalization across diverse EV charging scenarios, delivering notable improvements in both small- and large-scale problems.https://doi.org/10.1038/s44172-025-00457-8 |
| spellingShingle | Stavros Orfanoudakis Valentin Robu E. Mauricio Salazar Peter Palensky Pedro P. Vergara Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks Communications Engineering |
| title | Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks |
| title_full | Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks |
| title_fullStr | Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks |
| title_full_unstemmed | Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks |
| title_short | Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks |
| title_sort | scalable reinforcement learning for large scale coordination of electric vehicles using graph neural networks |
| url | https://doi.org/10.1038/s44172-025-00457-8 |
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