Quantum Computing for Analyzing Microgrid Systems With Uncertainties
Joseph Maxwell Lange, Jianing Chen, Yan Li, Liang Du · 2025
Modern power systems face substantial uncertainty due to the high penetration of renewable energy resources, whose diverse dynamic behaviors further complicate operational management. This paper presents a quantum Monte Carlo (QMC) framework that leverages quantum amplitude estimation to deliver a quadratic speedup over classical Monte Carlo methods for probabilistic disturbance analysis. The QMC methodology is presented in detail, covering system-state encoding into quantum registers, amplitude estimation, and extensions to joint normal probability density functions. Feasibility is demonstrated on a representative microgrid case study, in which voltage expectation values weighted by a probabilistic distribution of load characteristics are computed. A subsequent discussion outlines how the QMC approach can be generalized to arbitrary load distributions and expectation metrics. Given the rapid evolution of quantum hardware, this approach promises to become a powerful tool for uncertainty quantification, an essential capability for enabling robust planning and operation of power systems under stochastic conditions.