用中心协调多智能体架构提升电网拓扑控制效率与性能
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control
- 将决策分解为区域智能体提议+中心协调选择的两级结构
- 在多个实验中样本效率更高，最终表现优于基线方法
- 适合复杂电网场景，对真实系统部署有潜在应用价值
由于可再生能源发电增加，电网运行日益复杂。近年来的L2RPN竞赛推动了人工智能代理在电网调度中的应用。然而，动作空间的组合特性给传统优化器和学习控制器带来挑战。通过动作空间分解，将决策拆分为更小的子任务，是缓解维度灾难的一种方法。本文提出一种中心协调多智能体（CCMA）架构实现动作空间分解：区域智能体提出动作，协调智能体最终选定。我们在不同实验设置下评估多种CCMA实现，并与多个L2RPN基线方法对比。结果表明，该架构具有更高的样本效率和更优的最终性能，显示出在更高维度的L2RPN及真实电网场景中进一步应用的巨大潜力。
原文摘要 · Abstract (English)
Power grid operation is becoming more complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have encouraged the use of artificial agents to assist human dispatchers in operating power grids. However, the combinatorial nature of the action space poses a challenge to both conventional optimizers and learned controllers. Action space factorization, which breaks down decision-making into smaller sub-tasks, is one approach to tackle the curse of dimensionality. In this study, we propose a centrally coordinated multi-agent (CCMA) architecture for action space factorization. In this approach, regional agents propose actions and subsequently a coordinating agent selects the final action. We investigate several implementations of the CCMA architecture, and benchmark in different experimental settings against various L2RPN baseline approaches. The CCMA architecture exhibits higher sample efficiency and superior final performance than the baseline approaches. The results suggest high potential of the CCMA approach for further application in higher-dimensional L2RPN as well as real-world power grid settings.
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