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

Xincong Shi () and Chuangxin Guo
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Xincong Shi: College of Electrical Engineering, Zhejiang University, No. 38, Zheda Road, Hangzhou 310027, China
Chuangxin Guo: State Grid Shanxi Electric Power Company, No. 3, Harmony Garden Road, Jinyuan District, Taiyuan 030000, China

Energies, 2025, vol. 18, issue 8, 1-18

Abstract: 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.

Keywords: real-time dispatch; graph neural networks; behavioral cloning; grid expert strategy; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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