RAMBO

RAMBO数据集由丹麦技术大学风能与能源系统系创建，旨在通过双层优化方法生成最大化代表性的OPF数据集。该数据集包含100个样本，通过模拟测试在30、57和118总线的PGLib测试案例中收集。RAMBO数据集通过优化距离来选择OPF解决方案，以更有效地接近系统的边界。此数据集适用于机器学习应用，特别是在可再生能源主导的系统中，用于改进操作决策和增强网络性能。

The RAMBO dataset was developed by the Department of Wind Energy and Energy Systems at the Technical University of Denmark. Its core objective is to generate maximally representative Optimal Power Flow (OPF) datasets using a bi-level optimization methodology. Comprising 100 samples, the dataset is collected from PGLib test cases with 30, 57, and 118 buses via simulation experiments. The RAMBO dataset selects OPF solutions by optimizing distance metrics to more efficiently approach the system's operational boundaries. This dataset is tailored for machine learning applications, particularly in renewable energy-dominated power systems, to advance operational decision-making and enhance network performance.