The aggregation of 5G base station virtual power plants (5G-VPPs) has become an essential approach to improve the utilization of 5G base stations' idle storage and enhance the market competitiveness of 5G-VPPs. However, most existing studies allocate the backup energy storage of 5G base stations in a fixed proportion, which neglects the impact of dynamic communication intensity of 5G base stations and dynamic 5G-VPP scheduling decisions, making it difficult to accommodate the dual evolving demands. In the optimal scheduling of 5G-VPPs, the dynamic bidirectional interaction of demands between 5G base stations and 5G-VPPs at each scheduling stage introduces tradeoffs in capacity allocation, while decisions from the previous stage affect the subsequent stage. The absence of dynamic allocation between 5G base stations and 5G-VPPs reduces resource configuration efficiency and hinders the maximization of benefits for both parties, highlighting a clear gap in current research. To fill this gap, this paper, for the first time, proposes a dynamic allocation strategy for backup energy storage, along with a communication intensity-based calculation method for determining the backup capacity of 5G base stations. Building on this, a bi-level optimization scheduling model is developed to achieve dynamic allocation of energy storage resources. Simulation results demonstrate: (1) compared to standalone virtual power plants, 5G-VPP revenue increases by 28.83%; (2) compared with the fixed backup energy storage allocation scheme, the dynamic backup energy storage allocation scheme improves the backup assurance capability of 5G base stations by 66.90%, significantly enhancing communication stability and reliability during power outages.
Hu et al. (Fri,) studied this question.