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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.13265 |
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| _version_ | 1866918096380362752 |
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| author | Masoumi, Amin Korkali, Mert |
| author_facet | Masoumi, Amin Korkali, Mert |
| contents | This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive deep learning (DL) model with information gap decision theory (IGDT) to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of post-fault dynamics to proactively redispatch resources, ensuring the system's center of inertia remains stable under worst-case contingencies. Validated on the IEEE 39-bus system with 70% IBR penetration, the proposed approach prevents system collapse where a conventional VIS strategy fails, ensuring frequency stability at a cost increase of only 5%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning Masoumi, Amin Korkali, Mert Systems and Control This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive deep learning (DL) model with information gap decision theory (IGDT) to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of post-fault dynamics to proactively redispatch resources, ensuring the system's center of inertia remains stable under worst-case contingencies. Validated on the IEEE 39-bus system with 70% IBR penetration, the proposed approach prevents system collapse where a conventional VIS strategy fails, ensuring frequency stability at a cost increase of only 5%. |
| title | Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.13265 |