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.

翻译：随着可再生能源发电量的增加，电网运行正变得日益复杂。近期一系列"学习运行电网"(L2RPN)竞赛推动了人工智能体在辅助调度员运行电网方面的应用。然而，动作空间的组合性质对传统优化器和学习型控制器均构成了挑战。动作空间分解——将决策分解为更小的子任务——是应对维度灾难的一种方法。本研究提出了一种用于动作空间分解的中央协调多智能体(CCMA)架构。该方法中，区域智能体提出动作建议，随后协调智能体选择最终动作。我们研究了CCMA架构的多种实现方案，并在不同实验设置下与多种L2RPN基线方法进行了基准测试。CCMA架构展现出比基线方法更高的样本效率和更优的最终性能。结果表明CCMA方法在更高维度的L2RPN场景及实际电网环境中具有广阔的应用前景。