基于改进骨干粒子群算法的光储一体化5G基站优化调度模型

Optimal scheduling model for photovoltaic-energy storage integrated 5G base stations based on an improved bare bones particle swarm optimization algorithm

随着能源互联网技术的发展,在5G基站中配置分布式光伏和储能电池将成为通信基站能耗高和用电成本大的潜在解决方案。本文提出一种基于自适应搜索中心的改进骨干粒子群算法的光储一体化基站日前优化调度策略,综合考虑光储一体化基站系统的日运维成本和储能电池老化,制定出各电力单元逐时的能量调度计划,保证5G基站的经济稳定可靠运行。

With the development of energy internet technology,configuring photovoltaic systems and energy storage batteries in 5G base stations will become a potential solution for high energy consumption and high electricity costs in communication base stations.This study proposes an improved backbone particle swarm optimization algorithm based on an adaptive search center for the day-ahead optimization scheduling strategy of a photovoltaic-energy storage integrated 5G base station.Taking into account the daily operation and maintenance costs of the integrated optical storage base station system and the aging of energy storage batteries,an objective function is established to develop hourly energy scheduling plans for each power unit,ensuring the economy,and reliability of the 5G base station.