Dual-layer Capacity Optimization for 5G Base Stations Microgrids Based on Improved Beluga Whale Algorithm
XING Qiaoqiao
LI Shidong
LIAO Qinyi
LI Yunxiang
Abstract：To alleviate the pressure of energy consumption and fully exploit the scheduling potential of the system,a dual-layer capacity optimization model for the″planning-operation″of a 5th generation mobile network(5G)base station microgrid based on renewable energy generation as well as a fast solving algorithm was proposed.On the planning layer,the model aimed to minimize the total life-cycle cost,while in the scheduling layer,it focused on minimizing the operational cost.The iterative optimization of the dual-layer model provided the optimal solution for capacity allocation and operational scheduling.Furthermore,the improved beluga whale optimization(IBWO)algorithm incorporated the inverse elite strategy,vertical-horizontal crossover strategy,and whirlwind foraging strategy to enhance global search capability and accelerate convergence.Finally,the energy consumption characteristics of 5G base stations were thoroughly analyzed,and a power aggregation model was constructed by distinguishing between sensitive loads requiring immediate processing and tolerant loads that could be delayed.Standby energy storage was scheduled to enhance regulatory flexibility.The results demonstrated that,compared to particle swarm optimization,grey wolf optimization,and traditional beluga whale optimization algorithms,the IBWO algorithm reduced daily operational costs by 26.20%,20.54%,and 20.30%,respectively,and annual comprehensive costs by 15.39%,12.87%,and 10.84%,respectively.This study played an important role in reducing the comprehensive cost of 5G base stations and improving the system scheduling capability.
Keywords：5G base stationscapacity optimizationenergy storage regulationdual-layer modelimproved beluga whale algorithm
Publication Date：2025-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 237-243,265 )