Quantum Computing for Analyzing Microgrid Systems With Uncertainties

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.