Stochastic quantum power flow for risk assessment in power systems

Brynjar Sævarsson*, Hjörtur Jóhannsson, Spyros Chatzivasileiadis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

his 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.
Original languageEnglish
Article number111409
JournalElectric Power Systems Research
Volume241
Number of pages8
ISSN0378-7796
DOIs
Publication statusPublished - 2025

Keywords

  • Power grids
  • Power system security
  • Quantum computing
  • Quantum power flow
  • Risk assessment
  • Quantum Monte Carlo

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