A microgrid energy storage technology based on reinforcement learning

With the increasing penetration of renewable energy sources in power systems, their inherent intermittency and stochastic nature, combined with dynamic load demands and fluctuating real-time electricity prices, pose significant challenges to the stability and efficiency of traditional grids. Addressing the economic dispatch problem within Microgrids (MG), we integrated Deep Reinforcement Learning (DRL) with Quadratic Programming. This innovative approach aims to optimize decision-making processes through autonomous learning by intelligent agents while leveraging the strengths of quadratic programming to ensure the efficiency and feasibility of the solutions. Through in-depth simulation analysis using real-world datasets, the effectiveness and superiority of the proposed method have been validated. Additionally, comparative assessments with existing DRL models highlight its unique capabilities in tackling the complexities inherent in advanced energy management systems. The average reduction of system load fluctuation reaches $\mathbf{2 2 \%}$. The experimental results demonstrate that the proposed method is effective in both predictable and unpredictable settings.