Reforming Quantum Microgrid Formation

This letter introduces a novel compact and lossless quantum microgrid formation (qMGF) approach to achieve efficient operational optimization of the power system and improvement of resilience. This is achieved through lossless reformulation to ensure that the results are equivalent to those produced by the classical MGF by exploiting graph-theory-empowered quadratic unconstrained binary optimization (QUBO) that avoids the need for redundant encoding of continuous variables. Additionally, the qMGF approach utilizes a compact formulation that requires significantly fewer qubits compared to other quantum methods thereby enabling a high-accuracy and low-complexity deployment of qMGF on near-term quantum computers. Case studies on real quantum processing units (QPUs) empirically demonstrated that qMGF can achieve the same high accuracy as classic results with a significantly reduced number of qubits.

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Publication Details

Journal
IEEE Transactions on Power Systems
Published
2025-01-09
DOI
https://doi.org/10.1109/tpwrs.2025.3527817
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21
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article

Reforming Quantum Microgrid Formation

Mikhail A. Bragin, Y. Shamash, Chaofan Lin, Peng Zhang
1 citations
IEEE Transactions on Power Systems
Microgrid Control and Optimization
1.21
article

Reforming Quantum Microgrid Formation

Mikhail A. Bragin, Y. Shamash, Chaofan Lin, Peng Zhang
article en
1 citations

Abstract

This letter introduces a novel compact and lossless quantum microgrid formation (qMGF) approach to achieve efficient operational optimization of the power system and improvement of resilience. This is achieved through lossless reformulation to ensure that the results are equivalent to those produced by the classical MGF by exploiting graph-theory-empowered quadratic unconstrained binary optimization (QUBO) that avoids the need for redundant encoding of continuous variables. Additionally, the qMGF approach utilizes a compact formulation that requires significantly fewer qubits compared to other quantum methods thereby enabling a high-accuracy and low-complexity deployment of qMGF on near-term quantum computers. Case studies on real quantum processing units (QPUs) empirically demonstrated that qMGF can achieve the same high accuracy as classic results with a significantly reduced number of qubits.

IEEE Transactions on Power SystemsVol. 40(2)
Stony Brook University (US), Edison International (United States) (US)
Openalex Percentile: Top 26%
Microgrid Control and Optimization
1.21
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