A Two-Stage Green Energy Dispatch Scheme for Microgrid using Deep Reinforcement Learning | IEEE Journals & Magazine | IEEE Xplore

A Two-Stage Green Energy Dispatch Scheme for Microgrid using Deep Reinforcement Learning


Abstract:

The integration of renewable energy resources in microgrid productively contributes to reducing the emission of greenhouse gases, but inherently increases the complexity ...Show More

Abstract:

The integration of renewable energy resources in microgrid productively contributes to reducing the emission of greenhouse gases, but inherently increases the complexity of energy management. Capable of rapid-response characteristic, the deep reinforcement learning (DRL) algorithm could be applied to provide real-time energy scheduling. However, due to the limitation of restricted training data and ignoring of the impact on the environment, most DRL-based schemes fail to get comprehensive solutions. To overcome this, we proposed a two-stage scheme, namely GAN-DDPG energy dispatch scheme, which utilizes the benefits of both the generative adversarial networks (GAN) and an enhanced deep deterministic policy gradient algorithm, namely CE-DDPG algorithm. In the first stage, a trained GAN is used to generate sufficient training data for the training process of the CE-DDPG algorithm. Then, the microgrid controller could invoke the trained CE-DDPG algorithm to obtain a real-time scheduling with efficient carbon emissions reductions. Different from the traditional DRL algorithm, a novel reward function is proposed in the CE-DDPG algorithm, promoting the scheduling of the energy storage system (ESS) with more correct actions. Numerical simulations demonstrated that the proposed GAN-DDPG scheme could reduce the cumulative cost up to 35% with less carbon emissions of 23% compared to existing schemes.
Page(s): 1 - 1
Date of Publication: 11 April 2025
Electronic ISSN: 2473-2400

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