A Framework for Synthetic Power System Dynamics
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910369544404992 |
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| author | Büttner, Anna Plietzsch, Anton Anvari, Mehrnaz Hellmann, Frank |
| author_facet | Büttner, Anna Plietzsch, Anton Anvari, Mehrnaz Hellmann, Frank |
| contents | The paper is published in Chaos. Please refer to the Chaos version from now on.
Anna Büttner, Anton Plietzsch, Mehrnaz Anvari, Frank Hellmann; A framework for synthetic power system dynamics. Chaos 1 August 2023; 33 (8): 083120. https://doi.org/10.1063/5.0155971
Information on power grids is confidential and thus real data is often inaccessible. This necessitates the use of synthetic power grid models in research. So far the models used, for example, in machine learning had to be very simple and homogeneous to produce large ensembles of robust grids. We present a modular framework to generate synthetic power grids that considers the heterogeneity of real power grid dynamics but remains simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. We also include the major drivers of fluctuations on short-time scales. The synthetic grids generated are robust and show good synchronization under all evaluated scenarios, as should be expected for realistic power grids. This opens the door to future research that studies grids under severe stress due to extreme events which could lead to destabilization and black-outs. A software package that includes an efficient Julia implementation of the framework is released as a companion to the paper. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2303_06116 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Framework for Synthetic Power System Dynamics Büttner, Anna Plietzsch, Anton Anvari, Mehrnaz Hellmann, Frank Adaptation and Self-Organizing Systems The paper is published in Chaos. Please refer to the Chaos version from now on. Anna Büttner, Anton Plietzsch, Mehrnaz Anvari, Frank Hellmann; A framework for synthetic power system dynamics. Chaos 1 August 2023; 33 (8): 083120. https://doi.org/10.1063/5.0155971 Information on power grids is confidential and thus real data is often inaccessible. This necessitates the use of synthetic power grid models in research. So far the models used, for example, in machine learning had to be very simple and homogeneous to produce large ensembles of robust grids. We present a modular framework to generate synthetic power grids that considers the heterogeneity of real power grid dynamics but remains simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. We also include the major drivers of fluctuations on short-time scales. The synthetic grids generated are robust and show good synchronization under all evaluated scenarios, as should be expected for realistic power grids. This opens the door to future research that studies grids under severe stress due to extreme events which could lead to destabilization and black-outs. A software package that includes an efficient Julia implementation of the framework is released as a companion to the paper. |
| title | A Framework for Synthetic Power System Dynamics |
| topic | Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2303.06116 |