考虑负载时空转移特性的数据中心集群算电协同优化调度OA
Computing-power Synergistic Scheduling of Internet Data Centre Clusters Considering Spatio-temporal Load Transfer Characteristics
随着城市中高耗能互联网数据中心(internet data center,IDC)接入数量的不断增多,如何搭建科学合理的算电同互动调度模式,在满足IDC高可靠用电需求的前提下充分发挥其灵活可调特性,助力自身节能降耗与电网安全稳定运行,是当前社会各界重点关注的问题.然而,当前算电协同研究在刻画算力网络可调特性以及设计算电协同调度交易机制方面存在不足.为此,文章提出考虑负载时空转移特性的IDC集群算电协同优化调度方法.首先,提出计及设备运行耦合特性的IDC集群资源可调特性建模方法,系统刻画了工作负载、空调和储能3类灵活性资源的时空调节特性;然后,提出融合需求响应与调频服务的IDC集群参与电网协同调度模式.一方面,利用配电系统运营商(distribution system operator,DSO)实时下发的配电节点边际电价(distribution locational marginal price,DLMP)引导 IDC 集群聚合商(data center aggregator,DCA)制定集群内算力负载迁移策略;另一方面,聚合集群中IDC闲置备用储能容量参与电力系统调频交易,发挥储能双向快速调节效益;接着,构建DCA-DSO协同优化的双层模型.最后,算例分析表明所提方法在提升调频收益、降低购电成本及促进绿电消纳方面具有显著效果,验证了模型与方法的有效性和工程适用性.
With the growing number of high-energy Internet Data Centers(IDCs)being connected in cities,a key challenge is to design a scientifically grounded computing-power synergistic scheduling paradigm that ensures highly reliable electricity supply for IDCs while fully exploiting their flexibility to reduce energy use and support secure,stable grid operation.Yet existing studies remain insufficient both in characterizing the adjustable features of computing networks and in designing synergistic scheduling and market transaction mechanisms.To address this gap,this paper proposes an optimal computing-power synergistic scheduling method for IDC clusters that explicitly considers spatiotemporal load transfer.First,we develop a flexibility-modeling approach for IDC-cluster resources that accounts for equipment-level operational couplings and provides a systematic,quantitative description of the spatiotemporal regulation capabilities of three classes of flexible resources:IT workloads,cooling/air-conditioning systems,and energy storage.Second,we propose a coordination scheme for IDC-cluster participation in grid operations that integrates demand response and frequency-regulation services.On one side,real-time Distribution Locational Marginal Prices(DLMPs)published by the Distribution System Operator(DSO)guide the Data Center Aggregator(DCA)in planning intra-cluster compute-workload migration;on the other,idle reserve storage capacity across the cluster is aggregated to participate in frequency-regulation transactions,leveraging storage's fast,bidirectional ramping capability.We then construct a bi-level DCA-DSO collaborative optimization model.Finally,case studies demonstrate significant gains in frequency-regulation revenues,reductions in electricity procurement costs,and improvements in green-power utilization,thereby validating the effectiveness and engineering applicability of the proposed model and method.
向梓旸;黄淳驿;李康平;梁钢;王涛;李雅洁
上海交通大学 电气工程学院,上海市闵行区 200240上海交通大学 电气工程学院,上海市闵行区 200240上海交通大学 国家电投智慧能源创新学院,上海市闵行区 200240国网新疆电力有限公司信息通信公司,新疆维吾尔自治区 乌鲁木齐 830000国网新疆电力有限公司信息通信公司,新疆维吾尔自治区 乌鲁木齐 830000国网新疆电力有限公司信息通信公司,新疆维吾尔自治区 乌鲁木齐 830000
信息技术与安全科学
数据中心算电协同时空负载均衡可调能力估计调频辅助服务市场投标调度
internet data centerscomputing-power synergistic operationspatiotemporal load balancingflexibility estimationfrequency regulation ancillary services marketbidding and scheduling
《电力信息与通信技术》 2026 (3)
33-44,中插1-中插2,14
国家自然科学基金项目(52407126)上海市科委科技创新行动计划(25692113000).
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