Operator Learning for Power Systems Simulation

• URL: http://arxiv.org/abs/2510.09704v1
• Date: Thu, 09 Oct 2025 21:10:35 GMT
• Title: Operator Learning for Power Systems Simulation
• Authors: Matthew Schlegel, Matthew E. Taylor, Mostafa Farrokhabadi,
• Abstract summary: Operator learning is a family of machine learning methods that learn mappings between functions.<n>Time domain simulation is a crucial tool for studying and enhancing power system stability and dynamic performance.<n>This paper explores the concept of simulation time step-invariance, which enables models trained on coarse time steps to generalize to fine-resolution dynamics.
• Score: 9.317239577584411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time domain simulation, i.e., modeling the system's evolution over time, is a crucial tool for studying and enhancing power system stability and dynamic performance. However, these simulations become computationally intractable for renewable-penetrated grids, due to the small simulation time step required to capture renewable energy resources' ultra-fast dynamic phenomena in the range of 1-50 microseconds. This creates a critical need for solutions that are both fast and scalable, posing a major barrier for the stable integration of renewable energy resources and thus climate change mitigation. This paper explores operator learning, a family of machine learning methods that learn mappings between functions, as a surrogate model for these costly simulations. The paper investigates, for the first time, the fundamental concept of simulation time step-invariance, which enables models trained on coarse time steps to generalize to fine-resolution dynamics. Three operator learning methods are benchmarked on a simple test system that, while not incorporating practical complexities of renewable-penetrated grids, serves as a first proof-of-concept to demonstrate the viability of time step-invariance. Models are evaluated on (i) zero-shot super-resolution, where training is performed on a coarse simulation time step and inference is performed at super-resolution, and (ii) generalization between stable and unstable dynamic regimes. This work addresses a key challenge in the integration of renewable energy for the mitigation of climate change by benchmarking operator learning methods to model physical systems.

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