A Voltage-Level Optimization Method for DC Remote Power Supply of 5G
The optimal voltage level for different supply distances is discussed, and the effectiveness of the model is verified through examples, providing valuable guidance for
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.
• The 5G base station energy consumption prediction model based on LSTM proposed in this paper takes into account the energy consumption characteristics of 5G base stations. The prediction results have high accuracy and provide data support for the subsequent research on BSES aggregation and optimal scheduling.
At the same time, the new equipment has altered the power load characteristics of base stations. In the 5G technology framework, the 5G base station comprises macro and micro variants. The micro base station serves indoor blind spots with minimal power consumption. The macro base station exhibits greater potential for demand response.
The 5G network and power system are coupled energetically by power feeders. Based on gNB-sleep actions and mode switching of their BESSs, 5G network can provide power support to the power system when the grid frequency deviation reaches the threshold.
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