Optimal Power Flow

Optimal Power Flow (OPF) is a crucial optimization problem in power systems, aiming to efficiently and reliably distribute electricity while meeting demand and adhering to operational constraints. Current research heavily emphasizes developing faster and more robust solutions, particularly for large-scale grids with high renewable energy penetration, using machine learning models such as graph neural networks (GNNs) and deep reinforcement learning (DRL), often incorporating techniques like self-supervised learning and active sampling to improve accuracy and efficiency. These advancements are significant because they promise substantial cost savings, reduced emissions, and improved grid stability and resilience in the face of increasing complexity and uncertainty.