Abstract
The rapid deployment of Fifth-generation base stations (5G BSs) in urban communities has led to rising electricity costs for mobile network operators. Meanwhile, distributed photovoltaic power plants (PVs) provide a promising solution to offset energy expenses and reduce renewable energy curtailment. This study proposes a hybrid quantum-classical two-stage stochastic programming approach for the co-planning of BSs and PVs in urban communities. In the first stage, warm-start quantum annealing is employed to determine BS deployment locations and capacities. In the second stage, data envelopment analysis (DEA) is used to evaluate and improve the operational performance of the integrated BS-PV system. Case study results show that the proposed method reduces total planning costs to one-third compared to traditional experience-based strategies, enhances PV utilization by 12.53%, reduces electricity costs by 51.04%, and achieves over 5.4 times improvement in computational efficiency. These results demonstrate not only technical advantages but also practical value in supporting cost-effective and low-carbon urban infrastructure planning.