Deep Reinforcement Learning for Intelligent Load Balancing in Smart Power Grids

Abstract

This study addresses the challenge of intelligent load balancing in modern power grids, which are increasingly characterized by renewable energy sources, electric vehicles, and decentralized generation. Traditional control mechanisms, often based on rule-based systems, fail to cope with the dynamic and stochastic nature of these grids. To overcome these limitations, we propose an innovative hierarchical reinforcement learning framework for load balancing, integrating Proximal Policy Optimization (PPO) within a dual-layer control architecture. This framework employs both local agent-based decision-making and a global critic network for system-wide optimization. The approach is designed to adapt to the temporal and spatial variability inherent in modern power grids, ensuring efficient load distribution and stability across various operating conditions. We introduce the Grid-aware Structured Embedding Network (GSEN), a novel model that enhances power grid state estimation by capturing multi-scale topological and temporal dependencies. GSEN integrates spectral graph convolutions and temporal attention mechanisms, providing robust, real-time predictions. The Stability-Aware Adaptive Inference Mechanism (SAIM) enhances the stability and adaptability of the model by dynamically adjusting inference pathways based on real-world grid conditions. Empirical evaluations demonstrate that the proposed framework outperforms traditional methods and state-of-the-art models, showing significant improvements in load balancing efficiency and energy dispatch precision. These findings underline the potential of reinforcement learning-based solutions to meet the growing complexity and demands of smart power grids, providing a scalable and adaptable solution for intelligent grid management.