Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks

TL;DR

This paper introduces a robust deep reinforcement learning method for inverter-based volt-var control in distribution networks with limited measurements, using a conservative critic and surrogate rewards to improve voltage regulation and power loss minimization.

Contribution

It proposes a novel robust DRL approach with a conservative critic and surrogate rewards to handle partial observability in volt-var control, enhancing stability and performance.

Findings

Effective in scenarios with limited measurements, including less than 10% bus voltages.

Improves voltage profile and reduces power loss across the network.

Demonstrates robustness through extensive simulations.

Abstract

Inverter-based volt-var control is studied in this paper. One key issue in DRL-based approaches is the limited measurement deployment in active distribution networks, which leads to problems of a partially observable state and unknown reward. To address those problems, this paper proposes a robust DRL approach with a conservative critic and a surrogate reward. The conservative critic utilizes the quantile regression technology to estimate conservative state-action value function based on the partially observable state, which helps to train a robust policy; the surrogate rewards of power loss and voltage violation are designed that can be calculated from the limited measurements. The proposed approach optimizes the power loss of the whole network and the voltage profile of buses with measurable voltages while indirectly improving the voltage profile of other buses. Extensive simulations verify the effectiveness of the robust DRL approach in different limited measurement conditions, even when only the active power injection of the root bus and less than 10% of bus voltages are measurable.