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. To 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.

本研究针对可再生能源高渗透率带来的传统电力系统运营问题，提出了一种学习增强电力系统操作的整体框架（LAPSO）。该框架从优化的角度出发，通过统一机器学习和基于模型的优化目标，旨在消除电力系统任务之间的时间界限，以提升电力系统的经济性、稳定性和可靠性。研究表明，LAPSO能有效设计新的综合算法，并引入了自动增强现有电力系统优化模型的Python工具包。

LAPSO：用于学习增强电力系统操作的统一优化视角