Optimal energy scheduling for microgrid based on GAIL with Wasserstein distance

Abstract

Owing to the volatility and intermittency of renewable energy generation units in microgrids, effective energy scheduling methods are essential for efficient renewable energy utilization and stable microgrid operation. In recent years, microgrid energy optimization scheduling based on deep reinforcement learning (DRL) has made significant progress. With the development of the microgrid, the drawbacks of the traditional DRL agent, such as long training time and poor convergence effect, are gradually revealed. This paper proposes a generative adversarial imitation learning method with Wasserstein distance for optimal energy scheduling in the microgrid. This method combines a proximal policy optimization algorithm to optimize energy scheduling and reduce microgrid operating costs. First, the agent adaptively learns the action exploration process by imitating expert trajectories. Second, based on the generative adversarial theory, a discriminator network is added, and the Wasserstein distance is introduced into the discriminator network to distinguish between the generative and expert strategies. This feedback assists in updating the neural network parameters. Finally, the effectiveness of the proposed method is verified through an arithmetic example analysis.