Novel Real-Time Power System Scheduling Based on Behavioral Cloning of a Grid Expert Strategy with Integrated Graph Neural Networks

Amidst the large-scale integration of renewable energy, power grid operations are increasingly characterized by higher levels of uncertainty, challenging the system’s safety and stability. Traditional model-driven dispatch methods are computationally intensive, and recent Reinforcement Learning (RL) techniques struggle with slow training times due to high-dimensional state spaces, while the inability to fully utilize the system’s topology information affects scheduling accuracy. This paper introduces a novel Behavioral Cloning of Grid Expert Strategy with Integrated Graph Neural Networks (GES-GNNBC) method for efficient and highly accurate real-time dispatch. The approach integrates grid expert strategies with graph theory-based modeling and Behavioral Cloning (BC), capturing the topological information of the power grid through Graph Neural Networks (GNN) to improve scheduling accuracy. Tested on a modified IEEE 33-bus model rich in renewable sources, GES-GNNBC outperforms both traditional and RL methods in stability and efficiency of computing optimization schemes and power balance strategies, markedly improving dispatch decision-making speed and effectiveness.