Quantum-Embedded Robust Optimization for Resilience-Constrained Unit Commitment

Abstract

Optimal resilience-constrained unit commitment (RCUC) enhances power system resilience during extreme weather events. As a remarkable disruptive methodology, the introduction of quantum computing (QC) can reduce scale-induced computational burdens and demonstrate enormous potential in solving combinatorial optimization problems, e.g., unit commitment. Considering the paramount importance of safety constraints and disaster uncertainty, this paper introduces a quantum-embedded robust optimization approach for RCUC. By leveraging duality theory, linearization, and decomposition techniques, RCUC is reformulated as a two-stage problem. A quantum alternating direction method of multipliers embedded column-and-constraint generation (QADMM-C&CG) algorithm is proposed, where the reconstructed quadratic unconstrained binary optimization model or Ising model can be integrated and solved by both universal and specialized quantum computers. The effectiveness and scalability of QADMM-C&CG algorithm are validated on the modified IEEE-39 system under spatiotemporal typhoon events. Comparative numerical experiments on quantum simulators and real quantum machine, alongside classical computing, highlight the potential advantages, current challenges and future directions of QC.