基于云-边-端协同的区域级风电场大数据中心数据管理框架及优化运行方法
Data Management Framework and Operation Optimization Method for Regional Wind Farms Big Data Center Based on Cloud-edge-end Collaboration
张扬帆 1王玙 1李阳 2沈小军 2梁恺1
作者信息
- 1. 国网冀北电力有限公司电力科学研究院,北京 100045
- 2. 同济大学电气工程系,上海 200092
- 折叠
摘要
区域级风电场大数据中心面临运行能效低、交互性差、资源重复建设等问题,难以满足数字化特征凸显的新型电力系统对数据实时计算、绿色计算的需求.该文系统性梳理了风电场大数据中心典型数字化业务的类别、算力敏感性特征和可调节潜力,构建了基于云-边-端协同技术的数据管理架构,从数据交互、业务执行、资源调度和质量治理4 个维度提出了大数据中心优化运行方法.算例仿真分析了所提架构在数据传输、存储和能耗等方面的性能,结果表明:相较于集中式架构,云-边-端协同可为大数据中心云服务器节约 350%的存储容量,降低21.04%的能耗,验证了所提架构在实际工程中应用的有效性和合理性.
Abstract
The big data center(BDC)of regional wind farms is faced with challenges such as proliferation of low opera-tional energy efficiency,poor interactivity,and duplication of resources,which makes it difficult to adapt to the demands of data processing for real-time,green computing in new power systems.This paper systematically sorted out the typical digital services and their sensitivity to computing power in wind farms.The spatial and temporal adjustable potentials of BDC loads are also analyzed.Then,a collaborative framework is constructed for data management,which decomposes wind farm big data management into three levels,including cloud,edge and end.The corresponding key technologies on data interaction,business execution,resource scheduling and quality governance are proposed.The experiments are per-formed to analyze the performance of the proposed architecture in terms of data transfer,storage and energy consumption.The results show that the proposed cloud-edge-end collaborative architecture can save 350%of storage capacity and re-duce 21.04%of server energy consumption for cloud servers in big data centers compared to traditional centralized architectures,which further verify the effectiveness and rationality of the proposed architecture.
关键词
区域级风电场/大数据中心/数字化业务/云-边-端协同/数据管理架构/优化运行Key words
regional wind farms/big data center/digitalization business/cloud-edge-end collaboration/data management framework/operation optimization引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据