Datasets:

neurips26-PSML
/

SIIB-Time

 - simulation
 task_categories:
 - tabular-regression
+---
+# Context, Purpose, and Motivation
+The increasing penetration of inverter-based resources (IBRs), e.g, renewable and energy storage systems, is fundamentally reshaping power grid dynamics. Unlike conventional resources, IBRs interact with the grid through power electronics operating at microsecond timescales, introducing ultrafast dynamic phenomena that conventional time-domain simulation methods, e.g., RMS techniques, fail to capture [1]. Electromagnetic transient (EMT) simulations can capture these fast dynamics but require integration time steps of 1–50 microseconds, making system-wide studies computationally intractable. This creates a critical bottleneck for stability analysis, contingency planning, and control design in modern power systems, as time-domain simulation has been a fundamental tool for analyzing system stability and dynamic performance [2]. Recent grid incidents, such as the April 2025 massive blackout in Spain and Portugal, underscore these limitations and the need for scalable analysis tools. Overcoming this computational barrier is crucial to the stable integration of renewable energy sources as mandated by climate change mitigation policies [3].
+This dataset was created to support research on machine learning (ML)-based surrogate modeling for power systems time-domain simulation. Specifically, the dataset is designed for the problem of simulation time step-invariance, wherein a model trained on coarse-resolution data can generalize to fine-resolution dynamics without retraining [4]. This is a nascent research direction with only a handful of publications to date, and no publicly available dataset exists that provides paired EMT and RMS simulation trajectories of an inverter-based system under both grid-forming (GFM) and grid-following (GFL) control modes across a large and diverse set of operational scenarios. This dataset fills that gap by providing: (1) high-fidelity EMT trajectories from PSCAD alongside RMS trajectories from MATLAB Simulink for the same scenarios, enabling cross-domain resolution studies; (2) coverage of both GFM and GFL control modes; and (3) 4,000 distinct operational scenarios spanning a wide range of disturbance types, load conditions, and power references, making it suitable for training and benchmarking data-driven surrogate models.
+Reuse of this dataset is most naturally suited to researchers working at the intersection of power systems and machine learning for surrogate modeling, using approaches such as neural operators and physics-informed learning, with power systems as the sole application domain. Researchers outside the power systems field may find the paired coarse/fine-resolution structure valuable as a benchmark for resolution-invariant time-domain simulation of physical systems. However, users should be aware that the system studied, a single inverter infinite bus (SIIB), is a canonical but simplified test case. Conclusions drawn from models trained on this dataset may not directly generalize to large-scale multi-machine or multi-inverter systems without further validation. The reuse of the dataset is unlikely to be impacted by changes in the social, political, or historical context. That said, this dataset supports research relevant to energy transition. Hence, the popularity of use may vary across space and time, as the social and political support for energy transition mandates changes.
+[1] https://ieeexplore.ieee.org/abstract/document/9286772/
+[2] https://ieeexplore.ieee.org/abstract/document/10213230
+[3] https://www.sciencedirect.com/science/article/pii/S1364032118305537
+[4] https://arxiv.org/abs/2510.09704