Data-Driven Optimal Power Flow: A Behavioral Systems Approach
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909814884401152 |
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| author | Otzen, Sebastian Wolf, Hannes M. H. Hans, Christian A. |
| author_facet | Otzen, Sebastian Wolf, Hannes M. H. Hans, Christian A. |
| contents | The increasing decentralization of power systems driven by a large number of renewable energy sources poses challenges in power flow optimization. Partially unknown power line properties can render model-based approaches unsuitable. With increasing deployment of sensors, data-driven methods rise as a promising alternative. They offer the flexibility to adapt to varying grid structures and unknown line properties. In this paper, we propose a novel data-driven representation of nonlinear power flow equations for radial grids based on Willems' Fundamental Lemma. The approach allows for direct integration of input/output data into power flow optimisation, enabling cost minimization and constraint enforcement without requiring explicit knowledge of the electrical properties or the topology of the grid. Moreover, we formulate a convex relaxation to ensure compatibility with state-of-the-art solvers. In a numerical case study, we demonstrate that the novel approach performs similar to state-of-the-art methods, without the need for an explicit system identification step. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Optimal Power Flow: A Behavioral Systems Approach Otzen, Sebastian Wolf, Hannes M. H. Hans, Christian A. Systems and Control The increasing decentralization of power systems driven by a large number of renewable energy sources poses challenges in power flow optimization. Partially unknown power line properties can render model-based approaches unsuitable. With increasing deployment of sensors, data-driven methods rise as a promising alternative. They offer the flexibility to adapt to varying grid structures and unknown line properties. In this paper, we propose a novel data-driven representation of nonlinear power flow equations for radial grids based on Willems' Fundamental Lemma. The approach allows for direct integration of input/output data into power flow optimisation, enabling cost minimization and constraint enforcement without requiring explicit knowledge of the electrical properties or the topology of the grid. Moreover, we formulate a convex relaxation to ensure compatibility with state-of-the-art solvers. In a numerical case study, we demonstrate that the novel approach performs similar to state-of-the-art methods, without the need for an explicit system identification step. |
| title | Data-Driven Optimal Power Flow: A Behavioral Systems Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.25120 |