LAPSO: A Unified Optimization View for Learning-Augmented Power System Operations

--With the high penetration of renewables, traditional model-based power system operation is challenged to deliver economic, stable, and robust decisions. Machine learning has emerged as a powerful modeling tool for capturing complex dynamics to address these challenges. However, its separate design often lacks systematic integration with existing methods. T o fill the gap, this paper proposes a holistic framework of Learning-Augmented Power System Operations (LAPSO, pronounced as Lap-So). Adopting a native optimization perspective, LAPSO is centered on the operation stage and aims to break the boundary between temporally siloed power system tasks, such as forecast, operation and control, while unifying the objectives of machine learning and model-based optimizations at both training and inference stages. Systematic analysis and simulations demonstrate the effectiveness of applying LAPSO in designing new integrated algorithms, such as stability-constrained optimization (SCO) and objective-based forecasting (OBF), while enabling end-to-end tracing of different sources of uncertainties. In addition, a dedicated Python package-lapso is introduced to automatically augment existing power system optimization models with learnable components. All code and data are available at https://github.com/xuwkk/lapso_exp. Index T erms --Power system operation, machine learning, objective-based forecasting, stability-constrained optimization. A. Background and Motivation Power system decision-making consists of sequentially connected tasks, including modeling/forecasting, operation, and control (See Figure 1(a).) With the decarbonization need, traditional model-based approaches face significant challenges. For example, the increasing uncertainty associated with renewable generation undermines the reliability of deterministic forecasting and power system operation (PSO) [2]. Meanwhile, the declining share of inertia from synchronous generators (SGs) can cause grid instability [3].