Case Study: China Tower & Huawei
As the deployment of 5G continues, the energy consumption of base stations increased significantly and the number of base stations soars. These lead to a sharp increase in
According to the energy consumption characteristics of the base station, a 5G base station energy consumption prediction model based on the LSTM network is constructed to provide data support for the subsequent BSES aggregation and collaborative scheduling.
Aiming at minimizing the base station (BS) energy consumption under low and medium load scenarios, the 3GPP recently completed a Release 18 study on energy saving techniques for 5G NR BSs . A broad range of techniques was evaluated in terms of the obtained network energy saving (NES) gain and their impact to the user-perceived throughput (UPT).
Reference revealed that the 5G base station energy storage could participate in demand response, and obtain certain benefits when it meets the basic power backup requirements.
In the optimal configuration of energy storage in 5G base stations, long-term planning and short-term operation of the energy storage are interconnected. Therefore, a two-layer optimization model was established to optimize the comprehensive benefits of energy storage planning and operation.
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