Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning
Abstract
Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL) is applied to learn an optimal policy for making joint energy dispatch (ED) and unit commitment (UC) decisions in an isolated MG, with the aim for reducing the total power generation cost on the premise of ensuring the supply-demand balance. In order to overcome the challenge of discrete-continuous hybrid action space due to joint ED and UC, we propose a DRL algorithm, i.e., the hybrid action finite-horizon DDPG (HAFH-DDPG), that seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and deep deterministic policy gradient (DDPG), based on a finite-horizon dynamic programming (DP) framework. Moreover, a diesel generator (DG) selection strategy is presented to support a simplified action space for reducing the computation complexity of this algorithm. Finally, the effectiveness of our proposed algorithm is verified through comparison with several baseline algorithms by experiments with real-world data set.
Keywords
Cite
@article{arxiv.2206.01663,
title = {Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning},
author = {Jiaju Qi and Lei Lei and Kan Zheng and Simon X. Yang},
journal= {arXiv preprint arXiv:2206.01663},
year = {2023}
}
Comments
The title, authors, and experimental results of this paper have all been extensively updated. Since the title has been modified and the version is no longer updated, the new paper has been submitted on arxiv