LAPSO: A Unified Optimization View for Learning-Augmented Power System Operations
This addresses the problem of fragmented design in power system operations for grid operators and researchers, offering a holistic integration approach that is incremental in combining existing methods.
The paper tackles the challenge of integrating machine learning with traditional model-based power system operations to improve economic, stable, and robust decisions amid high renewable energy penetration, proposing the LAPSO framework that unifies learning and optimization across tasks and demonstrates effectiveness through simulations and new algorithms like stability-constrained optimization.
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.