Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909983499616256 |
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| author | Hassouna, Mohamed Holzhüter, Clara Lytaev, Pawel Thomas, Josephine Sick, Bernhard Scholz, Christoph |
| author_facet | Hassouna, Mohamed Holzhüter, Clara Lytaev, Pawel Thomas, Josephine Sick, Bernhard Scholz, Christoph |
| contents | The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyses how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Reinforcement Learning for Power Grids: A Comprehensive Survey Hassouna, Mohamed Holzhüter, Clara Lytaev, Pawel Thomas, Josephine Sick, Bernhard Scholz, Christoph Machine Learning The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyses how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications. |
| title | Graph Reinforcement Learning for Power Grids: A Comprehensive Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.04522 |