The rapid development of 5G communication technologies have increased communication rates, reduced latency, and improved user’s experience, while also brought the problem of increased energy consumption and high electricity costs for 5G operators. Therefore, using existing resources and technologies to reduce operating costs has become an urgent challenge for 5G operators. At the same time, to ensure communication reliability, 5G base stations are usually equipped with internal standby battery storage to continue powering the base station equipment during utility power outages and maintain normal operations of the base station. However, with the improvement of power supply reliability of the power grid, the standby energy storage of 5G base stations is always in a float charging state, and the utilization rate is not high, resulting in the waste of resources. As the number of 5G base stations is very large, their internal idle backup energy storage can be used as a flexible resource to participate in power grid dispatching, optimizing grid operation while obtaining certain auxiliary service revenue, providing a possibility for 5G operators to reduce operating costs, enabling effective interaction between the power system and the communication system, and achieving win-win cooperation between the two sides. Firstly, considering the dispersed location of individual 5G base stations, their standby energy storage capacity is small, which is difficult to attract the dispatching interest of the power grid. However, the number of 5G base stations is huge, and through a reasonable management mode, large-scale base station energy storage can provide certain power and energy support for the power system and optimize the power system operation. Therefore, this paper introduces base station standby energy storage aggregator (BSA), which will participate in grid dispatching after aggregating the dispatchable potential of each base station standby energy storage, and sign a reasonable dispatching agreement with communication base stations to obtain direct control of base station standby energy storage. The BSA obtains the power consumption of base stations based on the distribution of users and communication reliability within the coverage area, and reserves backup capacity for base stations based on the utility reliability and real-time power consumption of base stations, and determines whether the energy storage of each base station participates in power grid dispatching on the basis of ensuring the communication reliability of base stations. Secondly, based on the analysis of user communication reliability, utility power supply reliability and power consumption characteristics of base stations, the calculation method of standby capacity of base station energy storage is proposed, and based on this, an overall dispatchable potential assessment model of base station energy storage considering the characteristics of base station location distribution, user communication reliability, base station power supply reliability, and base station backup storage energy and power constraints is established. Thirdly considering the maximization of social benefits, and taking the standard deviation of grid load fluctuation and aggregator' revenue as optimization objectives, a multi-objective optimization model for the participation of 5G base station energy storage aggregators in grid dispatch is established, and the proposed model is solved by an improved multi-objective particle swarm algorithm. Finally, the paper designs an example considering the density of communication users to verify the proposed method, and analyzes the impact of different zoning transmitting power and utility power reliability on the dispatchable capacity of base station standby energy storage. The results show that under the premise of setting the backup capacity to ensure the reliability of base station power supply, the base station energy storage still has considerable dispatching potential on the whole, and its response to power grid dispatch can smooth out the load fluctuation of the grid and reduce the peak-to-valley difference to achieve the goal of peak shaving and valley filling. © 2023 Chinese Machine Press. All rights reserved.
