ProOPF: 面向专业级电力系统优化建模的大语言模型基准测试与改进
ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling
AI总结：
针对电力系统优化建模中自动化翻译自然语言需求为可执行优化模型的问题，本文提出专业级最优潮流数据集ProOPF-D和基准ProOPF-B，用于评估和改进大语言模型在专业级OPF建模中的表现。
摘要：
可再生能源渗透率的增长给电力系统运行带来了显著的不确定性，需要频繁调整调度目标和约束，挑战了依赖专业知识的近实时建模工作流程。大语言模型（LLMs）通过语义推理和代码合成，将自然语言（NL）运行需求翻译为可执行优化模型，为自动化这一过程提供了有前景的途径。然而，现有的面向优化建模的LLM数据集和基准主要针对粗粒度的跨领域泛化，在电力系统场景中，特别是最优潮流（OPF）方面，提供的严格评估有限。因此，我们引入了 extbf{ProOPF-D}和 extbf{ProOPF-B}，一个面向专业级OPF建模的数据集和基准：ProOPF-D包含12K个实例，将NL请求与参数调整和标准OPF的结构扩展配对，并附带可执行实现；ProOPF-B提供了121个专家标注的测试用例及真实代码，能够在具体和抽象的OPF建模机制下进行端到端评估。
英文摘要：
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.
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