Stochastic Quantum Power Flow for Risk Assessment in Power Systems

TL;DR

This paper presents a pioneering quantum computing framework for stochastic power flow analysis, utilizing quantum states and Quantum Monte Carlo sampling to efficiently evaluate risks like line overloads in uncertain power systems.

Contribution

It introduces the first quantum approach for stochastic power flow analysis, reducing sample complexity and enabling scalable risk assessment in power systems with high uncertainty.

Findings

Quantum method reduces sample size compared to classical Monte Carlo.

Demonstrates computational speedups on test systems.

Maintains accuracy while improving efficiency.

Abstract

This paper introduces the first quantum computing framework for Stochastic Quantum Power Flow (SQPF) analysis in power systems. The proposed method leverages quantum states to encode power flow distributions, enabling the use of Quantum Monte Carlo (QMC) sampling to efficiently assess the probability of line overloads. Our approach significantly reduces the required sample size compared to traditional Monte Carlo methods, making it particularly suited for risk assessments in scenarios involving high uncertainty, such as renewable energy integration. We validate the method on two test systems, demonstrating the computational advantage of quantum algorithms in reducing sample complexity while maintaining accuracy. This work represents a foundational step toward scalable quantum power flow analysis, with potential applications in future power system operations and planning. The results show promising computational speedups, underscoring the potential of quantum computing in addressing the increasing uncertainty in modern power grids.