Annealing-based Quantum Computing for Combinatorial Optimal Power Flow

Thomas Morstyn

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This paper proposes the use of annealing-based quantum computing for solving combinatorial optimal power flow problems. Quantum annealers provide a physical computing platform which utilises quantum phase transitions to solve specific classes of combinatorial problems. These devices have seen rapid increases in scale and performance, and are now approaching the point where they could be valuable for industrial applications. This paper shows how an optimal power flow problem incorporating linear multiphase network modelling, discrete sources of energy flexibility, renewable generation placement/sizing and network upgrade decisions can be formulated as a quadratic unconstrained binary optimisation problem, which can be solved by quantum annealing. Case studies with these components integrated with the IEEE European Low Voltage Test Feeder are implemented using D-Wave Systems' 5,760 qubit Advantage quantum processing unit and hybrid quantum-classical solver.

Original languageEnglish
Pages (from-to)1093-1102
Number of pages1
JournalIEEE Transactions on Smart Grid
Issue number2
Early online date22 Aug 2022
Publication statusPublished - Mar 2023

Keywords / Materials (for Non-textual outputs)

  • Annealing
  • D-Wave
  • Distribution Network
  • Electric Vehicle
  • Investment
  • Lattices
  • Optimal Power Flow
  • Power System Planning
  • Program processors
  • Quantum Annealing
  • Quantum annealing
  • Quantum Computing. Smart Charging
  • Qubit
  • Renewable energy sources
  • power system planning
  • optimal power flow
  • smart charging
  • electric vehicle
  • quantum computing
  • quantum annealing
  • Distribution network


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