Efficient Policy Adaptation for Voltage Control Under Unknown Topology Changes

TL;DR

This paper presents a topology-aware online policy optimization method for voltage control that efficiently detects topology changes and updates sensitivities, significantly improving regulation performance under system reconfigurations.

Contribution

It introduces a data-driven, sparsity-exploiting framework for fast sensitivity updates and policy adaptation in voltage control under unknown topology changes.

Findings

Achieves over 90% line identification accuracy with only 15 data points.

Significantly outperforms non-adaptive and regression-based adaptive policies.

Effective on IEEE 13-bus and SCE 56-bus systems.

Abstract

Reinforcement learning (RL) has shown great potential for designing voltage control policies, but their performance often degrades under changing system conditions such as topology reconfigurations and load variations. We introduce a topology-aware online policy optimization framework that leverages data-driven estimation of voltage-reactive power sensitivities to achieve efficient policy adaptation. Exploiting the sparsity of topology-switching events, where only a few lines change at a time, our method efficiently detects topology changes and identifies the affected lines and parameters, enabling fast and accurate sensitivity updates without recomputing the full sensitivity matrix. The estimated sensitivity is subsequently used for online policy optimization of a pre-trained neural-network-based RL controller. Simulations on both the IEEE 13-bus and SCE 56-bus systems demonstrate over 90 percent line identification accuracy, using only 15 data points. The proposed method also significantly improves voltage regulation performance compared with non-adaptive policies and adaptive policies that rely on regression-based online optimization methods for sensitivity estimation.