Hierarchical regulation strategy based on dynamic clustering for economic optimization of large-scale 5G base stations

Yunfei Mu, Xinyang Jiang, Xiaoyan Ma, Jiarui Zhang, Hongjie Jia, Xiaolong Jin and Boren Yao

Applied Energy, 2025, vol. 377, issue PD, No S0306261924020889

Abstract: Utilizing the backup energy storage potential of 5G base stations (BSs) for economic regulation is an essential strategy to provide flexibility to the power grid and reduce operational costs. However, the dimensionality of the decision variables for centralized regulation of large-scale BSs is substantial, thereby increasing the computational complexity. Furthermore, the traditional clustering method, which could enhance solution speed, fails to account for the spatiotemporal dynamics of the regulation potential induced by the tidal effect and the sleep mechanism of 5G BSs. This limitation affects the accuracy of regulation and the utilization of the BSs' regulable potential. Therefore, a hierarchical regulation strategy based on dynamic clustering for economic optimization of large-scale 5G BSs is proposed, where BSs are regulated at two levels: cluster and individual. Focusing on the changes in 5G BSs' regulation potential, a dynamic clustering method based on K-means is proposed, which considers the regulable capacity and geographical location of BSs over time and space, thereby reducing the computational scale. The method accounts for changes in the regulable capacity to modify clusters and dynamically aggregates them for modeling. Furthermore, the clustering regulation economic optimization model and in-cluster power allocation control model are established respectively at the cluster and individual levels to solve the corresponding regulation schemes. Due to the interaction between the clustering and regulation in the overall strategy, the optimal clustering and regulation scheme are determined through the iteration of dynamic clustering, clustering regulation and in-cluster allocation. The simulation with 2916 BSs in a test area is conducted. The results show that the computation time of the proposed strategy is reduced to 2.34 % of the centralized regulation. The maximum error of the regulable capacity and regulation scheme decrease by 21.93 % and 9.32 %. It demonstrates that the proposed strategy enhances the speed of large-scale 5G BSs regulation while ensuring the accuracy of regulation and utilization of the regulable potential.

Keywords: 5G base station; Backup energy storage; Regulable capacity; Dynamic clustering; Hierarchical regulation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124705

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