Abstract
Abstract
In the context of this study, we leverage a hierarchical deep reinforcement learning algorithm to address challenges in the integration of renewable energy sources within smart grid environments. The primary focus is on enhancing the efficiency of large-scale renewable energy integration while ensuring grid stability and reliability. The algorithm builds on the principles of hierarchical deep reinforcement learning, aiming to optimize energy utilization, reduce operational costs, and decrease reliance on conventional energy sources within the smart grid framework. Rigorous experimentation in authentic smart grid settings validates the efficacy of the proposed algorithm, demonstrating notable improvements in grid performance and increased utilization of renewable energy sources. The mathematical outcomes underscore the algorithm’s superior performance across diverse conditions. This research contributes a practical solution for seamlessly incorporating renewable energy sources into smart grids, providing valuable insights for the optimization and sustainability of future smart grid systems. Future research directions may involve further refinement of the algorithm to adapt to evolving electricity network environments, fostering broader applications of renewable energy technologies in power systems.