综合智慧能源2026，Vol.48Issue(9)：96-106,11.DOI:10.3969/j.issn.2097-0706.2026.09.008
新疆绿色算力园区源网荷储算协同容量优化
Coordinated capacity optimization of source-grid-load-storage-computing systems in green computing parks in Xinjiang
摘要
Abstract
Against the backdrop of the"East Data,West Computing"project,the development of new power systems,and the rapid development of large-scale wind and photovoltaic bases in Xinjiang,green computing parks are becoming important platforms for local renewable energy consumption,low-carbon computing infrastructure,and the development of integrated intelligent energy systems.To address the significant seasonal differences in wind and photovoltaic output in Xinjiang,the continuous nature of computing load,the variation of power usage effectiveness(PUE)in data centers with ambient temperature,the effects of low and high temperatures on energy storage efficiency,and the difficulty of traditional capacity planning in characterizing flexible computing tasks,a source-grid-load-storage-computing capacity optimization configuration method for green computing parks in Xinjiang was proposed.Wind power capacity,photovoltaic capacity,energy storage power,energy storage capacity,and data center IT capacity were used as decision variables to establish an annualized net cost model that included annualized investment costs,purchased electricity costs,carbon emission costs,wind and photovoltaic curtailment penalties,power quality penalties,and computing service revenue.The model used time-series data of electric,cooling,gas,and heating loads and wind and photovoltaic generation as basic inputs.Through the Xinjiang-specific regional characteristic quantification module,seasonal corrections to wind and photovoltaic output,dynamic PUE,temperature efficiency correction of energy storage,time-of-use electricity prices,carbon emission factors,grid-connected power constraints,and green electricity supply ratio constraints were introduced.An outer-layer capacity search strategy consisting of coarse-grid screening,local refinement search,and Pareto constraint screening was adopted.Under the given capacity scheme,hourly operational simulations were performed for representative days in four seasons in the inner layer,and rigid IT loads,flexible computing tasks,energy storage charging and discharging,purchased electricity,and curtailed electricity were jointly evaluated.Green computing parks supporting renewable energy bases in Hami,Changji,and other areas were used as case studies.The coordinated optimization scheme included 70 MW of rated wind power installed capacity,20 MW of rated photovoltaic installed capacity,6 MW of rated energy storage power,16 MW·h of rated energy storage capacity,and 12 MW of rated data center IT capacity,with a rigid IT load of 7.2 MW and an equivalent average power of 3.6 MW for flexible computing tasks.The results showed that,compared with the baseline scheme without coordination,the coordinated optimization scheme reduced the annualized net cost by 37.98%,increased the green electricity supply ratio to 93.24%,controlled the wind and photovoltaic curtailment rate at 3.32%,and reduced carbon emissions by 89.44%.The findings indicate that coordinated source-grid-load-storage-computing configuration can transform Xinjiang's wind and photovoltaic resource advantages into stable green computing supply advantages while achieving a favorable balance among economic performance,low-carbon operation,and renewable energy consumption.关键词
源网荷储算/容量优化/绿色算力/新能源消纳/综合智慧能源系统/算电协同Key words
source-grid-load-storage-computing system/capacity optimization/green computing/renewable energy consumption/integrated intelligent energy system/computing-electricity coordination分类
能源科技引用本文复制引用
樊小朝,王永真,张志豪,王子龙,史瑞静,孙剑桥,王维庆,何山,程志江,程静..新疆绿色算力园区源网荷储算协同容量优化[J].综合智慧能源,2026,48(9):96-106,11.基金项目
国家自然科学基金项目(72595833) National Natural Science Foundation of China(72595833) （72595833）
