Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning

TL;DR

This paper explores a quantum-embedded deep reinforcement learning framework for dynamic security control in power grids, demonstrating promising results on a standard test system and highlighting future research directions.

Contribution

It introduces a novel quantum-embedded algorithm for power grid security control, integrating quantum computing concepts with deep reinforcement learning.

Findings

Quantum-embedded algorithm shows promising control performance.

Framework's dependability assessed on IEEE 39-bus system.

Identifies shortcomings and future development avenues.

Abstract

Dynamic security control (DSC) is considered a pivotal step for the future power grid, which is increasingly penetrated by inverter-based resources. However, the efficiency of such practices, whether governed by automatic generation control or virtual inertia scheduling, can be intractable due to the complexity of the problem and the need to solve the differentialalgebraic equation in a timely manner with the required accuracy. In this regard, the model-free deep reinforcement learning algorithm demonstrates reliable performance. In addition, the introduction of fault-tolerant and near-term quantum computing terminologies, i.e., noisy intermediate-scale quantum, opens avenues for improving the performance of model-free algorithms leveraging quantum capabilities. This paper provides an organized framework and assesses its dependability by evaluating the performance of a quantum-embedded algorithm on the DSC of the IEEE 39-bus test system. Hence, the obtained results demonstrate promising applications, along with shortcomings that can be addressed and further developed later.