生成电力系统潮流与最优潮流数据的开源工具，支持真实多场景模拟。
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
- 融合真实负荷曲线与局部噪声，支持任意N-k拓扑扰动生成多样化数据
- 可生成超出运行极限的潮流样本，提升模型泛化能力
- 支持可变发电成本，适用于不同场景下的机器学习训练
我们提出 gridfm-datakit-v1，一个用于生成真实且多样化的潮流（PF）与最优潮流（OPF）数据集的 Python 库，以训练机器学习求解器。现有数据集和工具面临三大挑战：（1）缺乏真实随机负荷与拓扑扰动，导致场景多样性不足；（2）潮流数据仅限于满足 OPF 可行性点，限制了模型对越限情况（如线路过载、电压越限）的泛化能力；（3）最优潮流数据使用固定发电机成本函数，难以适应不同成本场景。gridfm-datakit 通过以下方式解决：（1）结合真实世界负荷曲线的全局缩放与局部噪声，并支持任意 N-k 拓扑扰动，生成高保真且多样的数据；（2）生成超出运行限值的潮流样本；（3）生成具有可变发电机成本的最优潮流数据。该工具可高效扩展至大规模电网（最高达 10,000 个母线）。本文对比了 OPFData、OPF-Learn、PGLearn 与 PFΔ。代码已开源在 GitHub（https://github.com/gridfm/gridfm-datakit），采用 Apache 2.0 许可证，可通过 `pip install gridfm-datakit` 安装。
原文摘要 · Abstract (English)
We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solvers. Existing datasets and libraries face three main challenges: (1) lack of realistic stochastic load and topology perturbations, limiting scenario diversity; (2) PF datasets are restricted to OPF-feasible points, hindering generalization of ML solvers to cases that violate operating limits (e.g., branch overloads or voltage violations); and (3) OPF datasets use fixed generator cost functions, limiting generalization across varying costs. gridfm-datakit addresses these challenges by: (1) combining global load scaling from real-world profiles with localized noise and supporting arbitrary N-k topology perturbations to create diverse yet realistic datasets; (2) generating PF samples beyond operating limits; and (3) producing OPF data with varying generator costs. It also scales efficiently to large grids (up to 10,000 buses). Comparisons with OPFData, OPF-Learn, PGLearn, and PF$Δ$ are provided. Available on GitHub at https://github.com/gridfm/gridfm-datakit under Apache 2.0 and via `pip install gridfm-datakit`.
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