Quantum Optimization of Energy Consumption in Flexible Manufacturing Systems Integrating Renewable Power Sources

Abstract

As modern manufacturing transitions toward flexible and energy-adaptive production environments, optimizing energy consumption has become essential for achieving operational sustainability and aligning with global decarbonization goals. Traditional optimization techniques often struggle with the nonlinear, multi-objective, and stochastic nature of energy flows in flexible manufacturing systems, especially when integrating intermittent renewable energy sources such as solar and wind. This study proposes a quantum-enhanced optimization framework leveraging quantum annealing, variational quantum algorithms, and hybrid quantum–classical solvers to minimize energy usage while maintaining production throughput and scheduling feasibility. The approach formulates energy allocation, load balancing, and production scheduling as combinatorial optimization problems that benefit from quantum parallelism and high-dimensional state exploration. Simulations conducted on benchmark manufacturing models demonstrate that the quantum-enabled framework improves peak-load reduction, enhances renewable utilization rates, and reduces overall energy consumption by up to 22% compared to classical baselines. These results indicate the transformative potential of quantum optimization in enabling cleaner, more resilient, and sustainably powered manufacturing ecosystems.