Learning-Augmented Power System Operations: A Unified Optimization View

With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness. Machine learning (ML) has emerged as a powerful tool for modeling complex system dynamics and uncertainty. However, standalone ML pipelines, including model selection, training, and validation, are often designed separately from the downstream optimization problems they influence, which can lead to suboptimal system-level decisions. To address this gap, this paper proposes \emph{Learning-Augmented Power System Operations} (LAPSO), a unified optimization-centered framework that treats ML as an explicit component of power-system operational decision-making. First, LAPSO provides generalized mathematical template covering both decision-independent predictors that parameterize downstream optimization and decision-dependent learned surrogates that enter optimization as auxiliary constraints. Second, it designs ML pipelines using optimization-aware criteria, including solution-quality, computational tractability, constraint satisfaction, and economic performance. We instantiate LAPSO on both stability-constrained optimization (SCO) and objective-based forecasting (OBF), and show how the framework provides actionable guidance for selecting learned components. We further extend the framework to a hybrid forecast--operation--control chain and use it to organize heterogeneous uncertainty sources. Finally, we release an open-source Python package, \texttt{lapso}, for modularly augmenting existing power-system optimization models with ML components. Code and datasets are available at: https://github.com/xuwkk/lapso_exp.