Self-Supervised Graph Neural Networks for Full-Scale Tertiary Voltage Control
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917382941835264 |
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| author | Donon, Balthazar Jamgotchian, Geoffroy Kulesza, Hugo Wehenkel, Louis |
| author_facet | Donon, Balthazar Jamgotchian, Geoffroy Kulesza, Hugo Wehenkel, Louis |
| contents | A growing portion of operators workload is dedicated to Tertiary Voltage Control (TVC), namely the regulation of voltages by means of adjusting a series of setpoints and connection status. TVC may be framed as a Mixed Integer Non Linear Program, but state-of-the-art optimization methods scale poorly to large systems, making them impractical for real-scale and real-time decision support. Observing that TVC does not require any optimality guarantee, we frame it as an Amortized Optimization problem, addressed by the self-supervised training of a Graph Neural Network (GNN) to minimize voltage violations. As a first step, we consider the specific use case of post-processing the forecasting pipeline used by the French TSO, where the trained GNN would serve as a TVC proxy. After being trained on one year of full-scale HV-EHV French power grid day-ahead forecasts, our model manages to significantly reduce the average number of voltage violations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03087 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Graph Neural Networks for Full-Scale Tertiary Voltage Control Donon, Balthazar Jamgotchian, Geoffroy Kulesza, Hugo Wehenkel, Louis Systems and Control A growing portion of operators workload is dedicated to Tertiary Voltage Control (TVC), namely the regulation of voltages by means of adjusting a series of setpoints and connection status. TVC may be framed as a Mixed Integer Non Linear Program, but state-of-the-art optimization methods scale poorly to large systems, making them impractical for real-scale and real-time decision support. Observing that TVC does not require any optimality guarantee, we frame it as an Amortized Optimization problem, addressed by the self-supervised training of a Graph Neural Network (GNN) to minimize voltage violations. As a first step, we consider the specific use case of post-processing the forecasting pipeline used by the French TSO, where the trained GNN would serve as a TVC proxy. After being trained on one year of full-scale HV-EHV French power grid day-ahead forecasts, our model manages to significantly reduce the average number of voltage violations. |
| title | Self-Supervised Graph Neural Networks for Full-Scale Tertiary Voltage Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.03087 |