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Microgrids, as decentralized energy systems, play a pivotal role in enhancing energy efficiency, reliability, and sustainability. Managing energy flow in microgrids presents significant challenges due to their dynamic nature, the integration of renewable energy sources, and fluctuating load demands. This research presents a comprehensive framework utilizing Deep Reinforcement Learning (DRL) to optimize energy management in microgrids. Unlike traditional approaches, our proposed system leverages advanced DRL algorithms including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradient (DDPG) to learn adaptive policies directly from environmental interactions. Our framework models the microgrid environment as a Markov Decision Process (MDP), incorporating energy generation units, storage systems, and diverse load profiles. Through extensive simulations and real-world data analysis, we demonstrate that our DRL-based approach achieves 23% lower operational costs, 31% higher renewable energy utilization, and 27% improved system resilience compared to conventional methods. The results establish the transformative potential of DRL in enabling autonomous, efficient, and sustainable microgrid operations.

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Energy Management in Microgrids Using Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Tlotlollo Sidwell Hlalele,
  • Zenghui Wang

摘要

Microgrids, as decentralized energy systems, play a pivotal role in enhancing energy efficiency, reliability, and sustainability. Managing energy flow in microgrids presents significant challenges due to their dynamic nature, the integration of renewable energy sources, and fluctuating load demands. This research presents a comprehensive framework utilizing Deep Reinforcement Learning (DRL) to optimize energy management in microgrids. Unlike traditional approaches, our proposed system leverages advanced DRL algorithms including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradient (DDPG) to learn adaptive policies directly from environmental interactions. Our framework models the microgrid environment as a Markov Decision Process (MDP), incorporating energy generation units, storage systems, and diverse load profiles. Through extensive simulations and real-world data analysis, we demonstrate that our DRL-based approach achieves 23% lower operational costs, 31% higher renewable energy utilization, and 27% improved system resilience compared to conventional methods. The results establish the transformative potential of DRL in enabling autonomous, efficient, and sustainable microgrid operations.