Distributionally Robust Joint Chance-Constrained Optimal Power Flow using Relative Entropy
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915114044620800 |
|---|---|
| author | Brock, Eli Zhang, Haixiang Lavaei, Javad Sojoudi, Somayeh |
| author_facet | Brock, Eli Zhang, Haixiang Lavaei, Javad Sojoudi, Somayeh |
| contents | Designing robust algorithms for the optimal power flow (OPF) problem is critical for the control of large-scale power systems under uncertainty. The chance-constrained OPF (CCOPF) problem provides a natural formulation of the trade-off between the operating cost and the constraint satisfaction rate. In this work, we propose a new data-driven algorithm for the CCOPF problem, based on distributionally robust optimization (DRO). \revise{We show that the proposed reformulation of the distributionally robust chance constraints is exact, whereas other approaches in the CCOPF literature rely on conservative approximations. We establish out-of-sample robustness guarantees for the distributionally robust solution and prove that the solution is the most efficient among all approaches enjoying the same guarantees.} We apply the proposed algorithm to the the CCOPF problem and compare the performance of our approach with existing methods using simulations on IEEE benchmark power systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03543 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Joint Chance-Constrained Optimal Power Flow using Relative Entropy Brock, Eli Zhang, Haixiang Lavaei, Javad Sojoudi, Somayeh Optimization and Control Systems and Control Designing robust algorithms for the optimal power flow (OPF) problem is critical for the control of large-scale power systems under uncertainty. The chance-constrained OPF (CCOPF) problem provides a natural formulation of the trade-off between the operating cost and the constraint satisfaction rate. In this work, we propose a new data-driven algorithm for the CCOPF problem, based on distributionally robust optimization (DRO). \revise{We show that the proposed reformulation of the distributionally robust chance constraints is exact, whereas other approaches in the CCOPF literature rely on conservative approximations. We establish out-of-sample robustness guarantees for the distributionally robust solution and prove that the solution is the most efficient among all approaches enjoying the same guarantees.} We apply the proposed algorithm to the the CCOPF problem and compare the performance of our approach with existing methods using simulations on IEEE benchmark power systems. |
| title | Distributionally Robust Joint Chance-Constrained Optimal Power Flow using Relative Entropy |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2501.03543 |