Quantum-Assisted Resilience Enhancement for Distribution Systems With Networked Microgrids Considering Full-Potential Failure Risks

Abstract

Effective pre- and post-disaster strategies are crucial for mitigating the impacts of extreme events and enhancing the resilience of networked microgrids (NMGs). However, traditional methods often fail to comprehensively and efficiently consider fault scenarios before disasters, and the inefficient challenge of addressing large-scale post-disaster recovery problems necessitate advanced computational approaches. This paper proposes a quantum-assisted resilience enhancement framework for power distribution systems with NMGs, fully accounting for potential failure risks. The main contributions include a two-stage quantum-assisted resilience enhancement framework that integrates quantum amplitude estimation (QAE) for quantifying failure risks, and generating scenarios, the quantum-encoded model establishment, and the proposed quantum surrogate absolute-value Lagrangian relaxation (Q-SAVLR) algorithm for achieving quantum-accelerated parallel optimization. Numerical tests on a modified IEEE system show that our approach reduces computation time by roughly 40%–75% relative to the classical solver, enabling faster repair resource deployment and more efficient resilience optimization for NMGs.