Quantum-Accelerated Deep Reinforcement Learning for Frequency Regulation Enhancement

TL;DR

This paper introduces a novel quantum-enhanced deep reinforcement learning approach for frequency regulation in power systems, demonstrating improved robustness and reliability through simulation on a standard test system.

Contribution

It integrates quantum circuits with deep reinforcement learning agents to enhance frequency regulation control in power systems, a novel combination of quantum computing and control theory.

Findings

Achieves reliable performance across diverse conditions

Demonstrates robustness in IEEE 14-bus system simulations

Shows potential for near-term quantum device applications

Abstract

In modern power systems, frequency regulation is a fundamental prerequisite for ensuring system reliability and assessing the robustness of expansion projects. Conventional feedback control schemes, however, exhibit limited accuracy under varying operating conditions because their gains remain static. Consequently, deep reinforcement learning methods are increasingly employed to design adaptive controllers that can be generalized to diverse frequency control tasks. At the same time, recent advances in quantum computing provide avenues for embedding quantum capabilities into such critical applications. In particular, the potential of quantum algorithms can be more effectively explored and harnessed on near-term quantum devices by leveraging insights from active controller design. In this work, we incorporate a quantum circuit together with an ansatz into the operation of a deep deterministic policy gradient agent. The simulation results of the IEEE 14-bus test system demonstrate the potential of this integrated approach that can achieve reliable, robust performance across diverse real-world challenges.