Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
This addresses the problem of slow and poorly coordinated voltage control in power grids, offering a scalable solution for grid operators, though it is incremental as it builds on existing DRL methods.
The paper tackles voltage control in power grids by proposing a hierarchical deep reinforcement learning design that exploits area-wise division for scalability, achieving fast and adaptive control actions to meet voltage recovery criteria following disturbances.
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive selection of control actions such that the voltage recovery criterion can be met following disturbances. Existing voltage control techniques suffer from the issues of speed of operation, optimal coordination between different locations, and scalability. We exploit the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models. We employ an enhanced augmented random search algorithm that is tailored for the voltage control problem in a two-level architecture. We train area-wise decentralized RL agents to compute lower-level policies for the individual areas, and concurrently train a higher-level DRL agent that uses the updates of the lower-level policies to efficiently coordinate the control actions taken by the lower-level agents. Numerical experiments on the IEEE benchmark 39-bus model with 3 areas demonstrate the advantages and various intricacies of the proposed hierarchical approach.