Deep Reinforcement Learning-Based Energy Management in Hybrid Renewable Energy Systems

Abstract

The increasing integration of renewable energy resources into modern power systems has created significant challenges in achieving efficient energy management, operational stability, and optimal utilization of distributed energy resources. This study presents a deep reinforcement learning-based energy management framework for hybrid renewable energy systems to improve power distribution efficiency, energy utilization, and system reliability under dynamic operating conditions. The proposed framework integrates solar photovoltaic systems, wind energy generation, battery energy storage, and intelligent control mechanisms with deep reinforcement learning algorithms to enable adaptive and autonomous energy management. A data-driven methodology was implemented in which energy generation patterns, load demand variations, battery state of charge, and environmental conditions were continuously monitored and analyzed using deep learning and reinforcement learning models. The developed control system employed reward-based optimization strategies to dynamically regulate energy allocation, storage utilization, and load balancing for maximizing overall system performance. Simulation and analytical results demonstrated substantial improvements in energy efficiency, renewable energy utilization, operational cost reduction, and power supply stability compared with conventional rule-based energy management approaches. The proposed framework also minimized energy wastage, reduced dependency on nonrenewable backup sources, and enhanced battery lifecycle performance through intelligent charging and discharging control. Furthermore, the adaptive learning capability enabled efficient response to fluctuating renewable generation and varying load demands in real time. The study concludes that deep reinforcement learning-based energy management provides an effective and scalable solution for optimizing hybrid renewable energy systems by enabling intelligent decision-making, adaptive control, and sustainable power management. The findings contribute to the advancement of smart energy infrastructures and resilient renewable energy integration technologies for future sustainable power systems.