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.
@article{arxiv.2602.03070,
title = {ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling},
author = {Chao Shen and Zihan Guo and Xu Wan and Zhenghao Yang and Yifan Zhang and Wengi Huang and Jie Song and Zongyan Zhang and Mingyang Sun},
journal= {arXiv preprint arXiv:2602.03070},
year = {2026}
}