ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913153635319808 |
|---|---|
| author | Shen, Chao Guo, Zihan Wan, Xu Yang, Zhenghao Zhang, Yifan Huang, Wengi Song, Jie Zhang, Zongyan Sun, Mingyang |
| author_facet | Shen, Chao Guo, Zihan Wan, Xu Yang, Zhenghao Zhang, Yifan Huang, Wengi Song, Jie Zhang, Zongyan Sun, Mingyang |
| contents | Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise-intensive, near-real-time modeling workflows. Large Language Models (LLMs) provide a promising avenue for automating this process by translating natural-language (NL) operational requirements into executable optimization models via semantic reasoning and code synthesis. Yet existing LLM datasets and benchmarks for optimization modeling primarily target coarse-grained cross-domain generalization, offering limited, rigorous evaluation in power-system settings, particularly for Optimal Power Flow (OPF). We therefore introduce \textbf{ProOPF-D} and \textbf{ProOPF-B}, a dataset and benchmark for professional-grade OPF modeling: ProOPF-D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF-B provides 121 expert-annotated test cases with ground-truth code, enabling end-to-end evaluation under both concrete and abstract OPF modeling regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03070 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling Shen, Chao Guo, Zihan Wan, Xu Yang, Zhenghao Zhang, Yifan Huang, Wengi Song, Jie Zhang, Zongyan Sun, Mingyang Systems and Control Software Engineering Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise-intensive, near-real-time modeling workflows. Large Language Models (LLMs) provide a promising avenue for automating this process by translating natural-language (NL) operational requirements into executable optimization models via semantic reasoning and code synthesis. Yet existing LLM datasets and benchmarks for optimization modeling primarily target coarse-grained cross-domain generalization, offering limited, rigorous evaluation in power-system settings, particularly for Optimal Power Flow (OPF). We therefore introduce \textbf{ProOPF-D} and \textbf{ProOPF-B}, a dataset and benchmark for professional-grade OPF modeling: ProOPF-D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF-B provides 121 expert-annotated test cases with ground-truth code, enabling end-to-end evaluation under both concrete and abstract OPF modeling regimes. |
| title | ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling |
| topic | Systems and Control Software Engineering |
| url | https://arxiv.org/abs/2602.03070 |