OPFData: Large-scale datasets for AC optimal power flow with topological perturbations
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916292908285952 |
|---|---|
| author | Lovett, Sean Zgubic, Miha Liguori, Sofia Madjiheurem, Sephora Tomlinson, Hamish Elster, Sophie Apps, Chris Witherspoon, Sims Piloto, Luis |
| author_facet | Lovett, Sean Zgubic, Miha Liguori, Sofia Madjiheurem, Sephora Tomlinson, Hamish Elster, Sophie Apps, Chris Witherspoon, Sims Piloto, Luis |
| contents | Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07234 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OPFData: Large-scale datasets for AC optimal power flow with topological perturbations Lovett, Sean Zgubic, Miha Liguori, Sofia Madjiheurem, Sephora Tomlinson, Hamish Elster, Sophie Apps, Chris Witherspoon, Sims Piloto, Luis Machine Learning Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology. |
| title | OPFData: Large-scale datasets for AC optimal power flow with topological perturbations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.07234 |