Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Parallel Hybrid Deep Reinforcement Learning for Real-time Energy Management of Microgrid
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School of Electric Power, South China University of Technology, Guangzhou, China

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This work was supported in part by the National Natural Science Foundation of China (No. 51977081) and the Natural Science Foundation of Guangdong Province (No. 2022A1515011193).

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    Abstract:

    This paper proposes a novel parallel hybrid deep reinforcement learning (DRL) approach to address the real-time energy management problem for microgrid (MG). As the proposed approach can directly approximate a discrete-continuous hybrid policy, it does not require the discretization of continuous actions like regular DRL approaches, which avoids accuracy degradation and the curse of dimensionality. In addition, a novel experience-sharing-based parallel technique is further developed for the proposed approach to accelerate the training speed and enhance the training robustness. Finally, a safety projection technique is introduced and incorporated into the proposed approach to improve the decision feasibility. Comparative numerical simulations with several existing MG real-time energy management approaches (i.e., myopic policy, model predictive control, and regular DRL approaches) demonstrate the effectiveness and superiority of the proposed approach.

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History
  • Received:June 24,2024
  • Revised:July 29,2024
  • Online: May 27,2025