4.8 Article

Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park

期刊

APPLIED ENERGY
卷 311, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.118636

关键词

Multi-energy management; Industrial park; Multi-agent; Counterfactual baseline; Soft actor-critic; Attention mechanism

资金

  1. National Key Research and Development Program of China [2018YFB1702300]
  2. NSF of China [61731012, 62103265, 62122065, 92167205]

向作者/读者索取更多资源

This paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park, aiming to minimize energy costs while ensuring user demand. A novel multi-agent deep reinforcement learning algorithm is introduced to solve the problem and improve calculation speed, with the addition of an attention mechanism to enhance stability and exploration efficiency.
Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据