综合智慧能源2026，Vol.48Issue(9)：84-95,12.DOI:10.3969/j.issn.2097-0706.2026.09.007
考虑算力负载时空迁移特性的多数据中心低碳经济调度
Low-carbon economic scheduling for multiple data centers considering spatiotemporal migration characteristics of computing loads
摘要
Abstract
In the context of the"East Data,West Computing"strategy,the spatiotemporal flexibility of computing loads across multiple data centers has not been fully utilized,and fixed carbon quotas fail to match the carbon emission characteristics of different regions and typical days,resulting in suboptimal economic and low-carbon performance of the system.For a multi-data-center microgrid system,a data center energy consumption model considering differences in regional climate,electricity prices,and renewable energy endowments was developed.Computing loads were divided into interactive and batch loads.Interactive loads had to be processed locally and immediately during their arrival periods,while batch loads had spatiotemporal adjustability.On this basis,a four-dimensional computing load migration model was constructed with the source data center,arrival period,destination data center,and execution period as its dimensions.The execution time and location of batch loads were jointly determined within the same optimization framework,thereby reflecting the coupling relationships among regional electricity prices,renewable energy output,and link transmission conditions.With the objective of minimizing total system cost,a mixed-integer linear programming model considering electricity purchase,operation and maintenance,carbon trading,and load migration costs was established and solved using CPLEX called via YALMIP.Three progressive scenarios—no workload migration,temporal migration only,and coordinated spatiotemporal migration—were compared to reveal the incremental effects of computing load migration on system economic performance,carbon emissions,and renewable energy consumption.In addition,under the premise of keeping the total annual carbon quotas constant,an adaptive typical-day carbon quota allocation method based on pre-scheduling carbon emission pressure prediction was proposed and compared with a fixed baseline allocation scheme.Case study results showed that,compared with the no-load-migration scenario,coordinated spatiotemporal migration reduced total system cost and carbon emissions by 9.41%and 11.50%,respectively.The direction of load migration responded to differences in regional electricity prices and renewable energy output.Daytime loads were transferred mainly to data centers with abundant photovoltaic resources,whereas nighttime loads were transferred mainly to those with abundant wind resources.The photovoltaic and wind power consumption rates reached 99.84%and 100%,respectively.Under the condition that the annual total carbon quota remained unchanged,the typical-day adaptive allocation reduced annual carbon trading cost and total system cost by 6.19%and 0.25%,respectively.Coordinated spatiotemporal migration can effectively match computing loads with low-cost,low-carbon electricity resources,achieving coordinated optimization of computing power and electricity.The adaptive quota allocation can improve the matching relationship between quotas and typical-day emission pressure.It reduces carbon trading compliance costs without tightening annual carbon emission constraints,thereby providing a practical pathway for low-carbon operation of geographically distributed data centers.关键词
多数据中心/算电协同/算力负载时空迁移/低碳经济调度/碳交易/碳配额分配/可再生能源消纳Key words
multiple data centers/computing-electricity coordination/spatiotemporal migration of computing workloads/low-carbon economic scheduling/carbon trading/carbon quota allocation/renewable energy consumption分类
能源科技引用本文复制引用
付利卓,王成福,鲁浩,蔡继增..考虑算力负载时空迁移特性的多数据中心低碳经济调度[J].综合智慧能源,2026,48(9):84-95,12.基金项目
国家自然科学基金项目(52377108) National Natural Science Foundation of China(52377108) （52377108）
