Alex Tan, Karthik Natarajan

In the field of Computer Science, Artificial Intelligence and Distributed Systems, combinatorial optimization of multi-period microgrid power scheduling becomes computationally intractable on classical computing architectures. This empirical investigation systematically examines Variational Quantum Optimization Algorithms for Large-Scale Smart Grid Microgrid Power Dispatch through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing parameterized Quantum Approximate Optimization Algorithm (QAOA) circuits compiled on superconducting quantum simulators, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the hybrid quantum-classical optimization workflow solved 64-node unit commitment instances with a 0.98 approximation ratio. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for smart grid utility operators and quantum algorithm researchers. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.