Therefore, this paper proposes a two-stage robust optimization (TSRO) model for 5G base stations, considering the scheduling potential of backup energy storage. At the day-ahead stage, the objective function is to minimize the comprehensive operational cost. . However, the uncertainty of distributed renewable energy and communication loads poses challenges to the safe operation of 5G base stations and the power grid. In this paper, firstly, an energy consumption prediction model based on long and short-term. . Therefore, BS power backup is in great need to keep the reliability of future mobile networks, especially for the macro BSs with large areas of network coverage and small ones serving mission-critical mobile and edge services (e., connected and automated vehicles [62]). To deploy backup batteries. . This work studies the optimization of battery resource configurations to cope with the duration uncertainty of base station interruption. We mainly consider the demand transfer and sleep mechanism of the base station and establish a two-stage stochastic programming model to minimize battery. . This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning. .
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