The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique

deep reinforcement learning (DRL) attracts attention in the control of power grids. However

DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach

bridging planning and DRL

to the task of power line flow regulation. First

we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second

we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition

we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization

a state-of-the-art deep reinforcement learning (DRL) approach

improving efficiency by 26.16% and 6.86% on both systems.