Optimal Energy Management of Microgrid Based on Quantum Annealing Algorithm

The growing integration of wind and renewable energy into microgrids introduces critical operational challenges, including uncertainty in energy management, cost optimization complexities, and the maintenance of system stability. To address these challenges, this article proposes a quantum annealing (QA)-based optimization framework enhanced by a perturbation-driven solution generation strategy and an adaptive annealing mechanism. The improved QA algorithm is rigorously evaluated through six benchmark test functions, demonstrating a 19.2% increase in optimization accuracy and a 32.7% reduction in solution variability (measured by standard deviation) compared to conventional QA. For practical validation, the framework is applied to microgrid scheduling, where the enhanced QA achieves cost reductions of 21.5%, 18.3%, and 16.7% compared to particle swarm optimization, genetic algorithms, and classical simulated annealing, respectively. Notably, the proposed adaptive mechanism further reduces daily operational costs by 11.4% compared to the baseline QA, highlighting its effectiveness in balancing exploration–exploitation tradeoffs. These advancements underscore the potential of quantum-inspired algorithms in addressing high-dimensional, nonconvex optimization problems inherent to renewable energy systems, while providing a pathway toward scalable and interpretable solutions for real-world microgrid management.