Flexible aggregation and coordinated scheduling strategy for renewable powered 5G communication base station clusters

被引：0

作者：

Zhang L. ^{[1
]}

Fan Y. ^{[1
,2
]}

Wang Y. ^{[1
]}

Zhou B. ^{[1
]}

Dou Q. ^{[1
]}

机构：

[1] College of Electrical and Information Engineering, Hunan University, Changsha

[2] Shanxi Transportation Technology Research & Development Co., Ltd., Taiyuan

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2024年 / 52卷 / 02期

基金：

中国国家自然科学基金;

关键词：

5G communication base station; distributed optimization; flexibility aggregation; Minkowski sum; power system peak shaving;

D O I：

10.19783/j.cnki.pspc.230685

中图分类号：

学科分类号：

摘要：

5G communication base stations are numerous and usually equipped with photovoltaic and energy storage and their power consumption is adjustable. It is a high-quality resource for flexible power regulation. This paper proposes a flexible resource aggregation method for multi-type renewable powered 5G communication base station clusters and a coordinated scheduling strategy for participating in power system peak shaving. First, the adjustable power consumption characteristics of multiple types of base stations under the sleep mechanism and the regulating capacity of energy storage considering the reserve power of the base station are analyzed. A flexibility space quantification model considering the impact of the uncertainty of solar power generation and base station communication load on the power feasible region of flexible resources for renewable powered 5G base stations is formulated based on the idea of limit scenarios. The flexible resource aggregation adjustable domain of a massive base station cluster is obtained using the Minkowski sum method to depict the spatio-temporal coupled energy trajectory of heterogeneous base station flexibility resources. Second, an optimal scheduling optimization model for base station flexibility resources participating in the electric energy market and auxiliary service market is established. Also a hierarchical distributed day-ahead peak shaving scheduling strategy based on the ADMM algorithm is proposed. The problem of large-scale base station cluster scheduling is decomposed into three sub-problems: unified collaborative peak shaving power response, aggregated power autonomous scheduling, and base station cluster power allocation for an iterative solution. Through the simulation test and comparative analysis, the proposed strategy can reduce the energy cost of communication base stations by 69.86%. This provides an effective means to improve the utilization of communication resources and the flexible regulation capability of the power system. © 2024 Power System Protection and Control Press. All rights reserved.

引用

页码：101 / 110

页数：9

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