【作者】 王晴；

【导师】 刘友波；

【作者基本信息】 四川大学 ， 电气工程（专业学位）， 2021， 硕士

【摘要】 随着“大云物移智”技术的快速推进,新型基础设施建设战略发展,代表数据流的5G网络与互联网数据中心(internet data centers,IDCs)基础设施建设逐步加快。5G网络能耗所产生的巨额电费使5G网络运营商的运营成本急剧增加,且绿色节能通信网络的构建关乎5G网络能否可持续发展;地理位置上分散的互联网数据中心是大数据平台的物理基础,同时由于5G网络发展,数据计算量激增,数据中心的运营电费成本激增。数据新基建的能耗问题已成为数据网络运营商与电网公司不容忽视的难题。在数据流爆炸的背景下,用5G网络与电容量巨大的IDCs可作为需求响应,成为智能电网的重要互动资源。利用5G网络、IDCs网络与城市高压配电网之间动态关联特性,使数据负荷在数据网络中的迁移代替电能在电网中的转移,实现弹性转供。研究5G网络与IDCs网络在需求响应方面应用,并构建传统负荷转供方法与数据网络运营商需求响应方法协同管控城市电网的双层优化模型,将城市电网静态容量转变为动态容量,解决城市电网阻塞问题与需求响应中电网公司与数据网络运营商2个独立主体运行效益问题。我国5G网络、IDCs网络运营商通常是数据网络运营商一个主体,且5G基站边缘计算容量较小,5G基站计算负荷仍基本位于地理位置上分布的数据中心,本文将数据流负荷集中于IDCs,构造IDCs网络运营商参与需求响应的模型。首先,构建电网公司与IDCs网络运营商双层协同优化模型,上层模型目标函数为电网公司阻塞管理成本最小、下层模型目标函数为IDCs网络运营商参与需求响应的收益最大,通过基于优惠券激励的需求响应决策模型,协调二者运行效益。其次,基于Benders分解思想构建算法的整体框架与流程,对模型进行求解。最后,通过算例验证:当IDCs网络运营商所运营的数据网络达到一定规模时,协同优化模型阻塞管理效果优于传统负荷转供模型,在解决阻塞问题的同时,既能降低电网公司阻塞管理成本也能保障IDCs网络运营商参与需求响应收益。同时,本文探究能源互联网背景下,5G网络能耗管控技术、分布式清洁能源系统、储能系统与需求响应在5G网络能耗管控中如何应用。提炼存在的学术与工程问题,梳理下一步研究思路,并进行相关研究内容的总结及展望。更多还原

【Abstract】 With the rapid development of the technology of “Big data,cloud computing,Internet of things,mobile Internet,artificial intelligence”,technology and the development of new infrastructure construction strategies,the infrastructure construction of 5G networks and Internet data centers(IDCs)representing data streams is gradually accelerating.The huge electricity bills generated by 5G network energy consumption have sharply increased the operating costs of 5G network operators,and the construction of green energy-saving communication networks is related to the sustainable development of 5G networks;geographically dispersed Internet data centers are the physics of big data platforms At the same time,due to the development of 5G networks,the amount of data calculation has increased sharply,and the operating electricity cost of data centers has increased sharply.The energy consumption of new data infrastructure has become a difficult problem that data network operators and power grid companies cannot ignore.In the context of the explosion of data streams,5G networks and IDCs with huge electric capacity can be used as demand responses and become important interactive resources for smart grids.Utilizing the dynamic correlation characteristics between the 5G network,IDCs network and the urban high-voltage distribution network,the migration of data loads in the data network replaces the transfer of electric energy in the power grid,realizing flexible transfer.Study the application of 5G network and IDCs network in demand response,and build a two-layer optimization model for cooperating with the traditional load transfer method and data network operator demand response method to control the urban power grid,transform the static capacity of the urban power grid into dynamic capacity,and solve the urban power grid Congestion and demand response are two independent entities operating efficiency issues of power grid companies and data network operators.Chinese 5G network and IDCs network operators are usually the main body of data network operators,and the edge computing capacity of 5G base stations is small,and the computing load of 5G base stations is still basically located in geographically distributed data centers.This article focuses on the data stream load on IDCs.Construct a model of IDCs network operators participating in demand response.First,construct a two-tier collaborative optimization model for grid companies and IDCs network operators.The objective function of the upper model is that the grid company’s congestion management cost is the smallest,and the objective function of the lower model is that the IDCs network operator has the greatest benefit from participating in demand response.Demand response decision-making model to coordinate the operational benefits of the two.Secondly,build the overall framework and process of the algorithm based on the Benders decomposition idea,and solve the model.Finally,an example is verified: when the data network operated by the IDCs network operator reaches a certain scale,the congestion management effect of the collaborative optimization model is better than that of the traditional load transfer model,which can reduce the congestion management of the grid company while solving the congestion problem.Cost can also guarantee the benefits of IDCs network operators participating in demand response.At the same time,this article explores how 5G network energy consumption management and control technology,distributed clean energy systems,energy storage systems and demand response are applied in 5G network energy consumption management and control under the background of the energy Internet.Refine the existing academic and engineering issues,sort out the next research ideas,and summarize and prospect the relevant research content.更多还原