The increasing share of uncertain renewable energy sources (RES) in power systems necessitates new efficient approaches for the two-stage stochastic multi-period AC optimal power flow (St-MP-OPF) optimization. The computational complexity of St-MP-OPF, particularly with AC constraints, grows exponentially with the number of uncertainty scenarios and the time horizon. This complexity poses significant challenges for large-scale transmission systems that require numerous scenarios to capture RES stochasticities. This paper introduces a scenario-based decomposition of the St-MP-OPF based on the alternating direction method of multipliers (ADMM). Additionally, this paper proposes a machine learning-accelerated ADMM approach (ADMM-ML), facilitating rapid and parallel computations of numerous scenarios with extended time horizons. Within this approach, a recurrent neural network approximates the ADMM sub-problem optimization and predicts wait-and-see decisions for uncertainty scenarios, while a master optimization determines here-and-now decisions. Additionally, we develop a hybrid approach that uses ML predictions to warm-start the ADMM algorithm, combining the computational efficiency of ML with the feasibility and optimality guarantees of optimization methods. The numerical results on the 118-bus and 1354-bus system show that the proposed ADMM-ML approach solves the St-MP-OPF with 3-4 orders of magnitude speed-ups, while the hybrid approach provides a balance between speed-ups and optimality.

Time domain simulation (TDS) is an important tool for assessing power system security under various disturbances. However, its computational cost limits the number of disturbances that can be assessed. The need for fast assessment of numerous disturbances has increased with the rapid integration of renewable energy sources. Machine learning (ML) methods have been explored to accelerate power system TDS, but these methods are studied in interpolation scenarios, where they predict outputs for inputs within the training data distribution. This work uses a state-of-the-art ML model to explore the extrapolation behavior of ML models for TDS. First, we highlight the importance of ML models’ extrapolation capacity for fast assessment of numerous diverse disturbances. Next, we demonstrate that extrapolation for discrete disturbances is more challenging than for continuous disturbances. Subsequently, we investigate how transfer learning (TL) may be used to improve the performance of ML models in TDS extrapolation scenarios. Finally, we outline the limitations of TL for power system TDS and suggest alternative approaches for developing ML models with better extrapolation performance in TDS applications.