Machine Learning-Enhanced Quantum-Classical Benders Decomposition for Stochastic Unit Commitment

Document Type

Article

Publication Date

1-1-2026

Abstract

This paper proposes a learning-based quantum-classical Benders decomposition method for solving large-scale two-stage stochastic unit commitment (UC) problems. The approach integrates a long short-term memory (LSTM) network to approximate subproblem costs and a support vector machine (SVM) classifier to filter dominant Benders cuts, thereby reducing problem size, facilitating quantum computing implementation, and improving scalability. The master problem is reformulated into a quadratic unconstrained binary optimization (QUBO) problem, enabling its solution on quantum hardware. The key contributions are: (i) a quantum-classical decomposition framework tailored for stochastic UC, (ii) an LSTM-based subproblem proxy to facilitate iterations, (iii) an SVM-based cut filtering method to retain only dominant cuts, and (iv) a QUBO reformulation suitable for quantum solvers. Numerical experiments demonstrate that the proposed method significantly reduces computational overhead and quantum resource requirements compared with conventional approaches.

Publication Source (Journal or Book title)

IEEE Transactions on Power Systems

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