Hybrid Quantum-Classical Computing Model for Battery Scheduling

This study utilizes quantum computing resources to address the optimization problem of battery scheduling. Traditionally, such problems are formulated as Mixed-Integer Linear Programming (MILP) models and solved using classical algorithms such as branch-and-cut and Cutting Plane algorithms. However, these classical algorithms exhibits limitations when handling large-scale problems. To mitigate these limitations, decomposition techniques are typically implemented to handle large-scale complex MILP problems. Although decomposition technique increases the cababilities of classical computing resources to deal with large-scale probelems, they are still constrained by the physical limitation of classical hardware. In this paper, the utilization of both quantum and classical computing resource to solve MILP optimization problem is explored through proposing a hybrid quantum-classical battery scheduling model. The proposed battery scheduling model is decomposed into two operational layers: a time slot optimization layer (layer-1) and a charging/discharging power optimization layer (layer-2). In the first layer, the computational power of quantum resources is utilized to determine the optimal time slots for battery charging, discharging, or remaining idle. Once these time slots are determined, the second layer focuses on optimizing the amount of power to be charged or discharged. Bender’s decomposition technique is employed to implement the proposed model, and the results are compared with those obtained using classical algorithms.