Harnessing urban 5G base station mega-clusters for grid flexibility: A coordinated optimization framework

An urban-level 5G communication network composed of densely distributed 5G base stations (BSs) can provide significant flexibility to support power grid operations. Although existing research has undertaken preliminary explorations, such approaches heavily rely on privacy-involved communication traffic data to model the behavior of 5G BSs. It renders them unsuitable for dispatching 5G BS mega-clusters, thereby failing to enable flexible interaction between urban-level communication networks and power grids. To this end, this paper proposed a novel insight to form synergic operation of 5G network and distribution network with limited information. Firstly, the communication traffic condition of urban 5G network is estimated by applying crowd heatmaps derived from public database. Secondly, by integrating the dynamic sleeping strategy of 5G BSs considering spatial-temporally coupling characteristics, the urban 5G network operation optimization model is constructed to accurately capture operational flexibility. Thirdly, a novel synergic operation framework is formulated, which adopts the distribution location marginal price capable of sensitively reflecting the nodal differentiated operation requirements of the distribution network to spontaneously guide the orderly operation of 5G networks. Case studies validate the efficiency of the proposed method in leveraging the flexibility of urban 5G network to improve the economy and stability of the distribution network. • A novel urban 5G traffic simulation method using crowd heatmaps and BS data from public databases is proposed. • A novel energy optimization model is developed to reflect the traffic transfer and diffusion effects among 5G BS clusters. • A dynamic interaction framework is established between the distribution system operator and the mobile network operator. • DLMP signals are employed to attract 5G BS clusters to participate in local demand response proactively.