A Reinforcement Learning Based Method for Dynamic Scheduling and Energy Efficiency Optimization in Smart Grids

With the increasing penetration of renewable energy sources, modern smart grids face significant and unprecedented challenges in dynamic scheduling and energy efficiency optimization under highly uncertain operating conditions. Traditional optimization methods often struggle with severe computational bottlenecks and slow response times when deployed in high-dimensional, dynamic environments. To overcome these critical limitations, this study comprehensively investigates the extent to which reinforcement learning (RL) and deep reinforcement learning (DRL) approaches can effectively address these operational challenges. Through a rigorous empirical evaluation of three distinct case studies-economic dispatch under renewable uncertainty, multi-agent coordinated scheduling, and real-time voltage stability control-this research provides a robust framework for intelligent grid management. A mixed-methods approach, combining qualitative analysis of RL policy behavior with quantitative performance evaluation, was systematically employed using three publicly available datasets: the IEEE 118-bus test system, the PJM historical load dataset, and the Nordic32 test system. The comprehensive results reveal that RL-based methods, particularly the Deep Deterministic Policy Gradient and Multi-Agent Deep Deterministic Policy Gradient algorithms, achieve superior performance in complex dynamic scheduling tasks. These advanced techniques demonstrated remarkable cost reductions ranging from 12.5% to 41.1% compared to conventional baseline methods. However, the evaluated models also exhibit notable limitations in generalization across diverse operating scenarios and strict constraint satisfaction under extreme grid conditions. Ultimately, this study significantly contributes to the fundamental understanding of RL algorithms' potential and limitations in smart grid applications, offering valuable insights and strategic pathways for future improvements in real-time energy management systems.