Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

TL;DR

This paper introduces a decentralized multi-agent deep reinforcement learning framework for energy management in multi-energy industrial parks, effectively reducing costs amidst renewable energy uncertainties and complex energy interactions.

Contribution

It develops a novel multi-agent deep reinforcement learning algorithm with attention mechanisms and a new reward design, improving scalability and efficiency for industrial energy management.

Findings

The proposed algorithm reduces energy costs across various demand scenarios.

It demonstrates high scalability and robustness in managing multi-energy systems.

Numerical results confirm improved policy stability and exploration efficiency.

Abstract

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.