综合智慧能源2026，Vol.48Issue(8)：96-106,11.DOI:10.3969/j.issn.2097-0706.2026.08.008
新疆绿色算力园区多时间尺度低碳调度
Multi-time scale low-carbon dispatch for Xinjiang green computing parks
摘要
Abstract
Green computing power parks integrated with renewable energy bases in Xinjiang face coordinated scheduling issues,which are caused by fluctuations in wind and photovoltaic power output,computing task deadlines,inter-temporal coupling of energy storage,and variations in the carbon emission intensity from purchased electricity.To address these issues,a multi-time scale low-carbon scheduling method covering day-ahead,intraday,and real-time operation was proposed.Unlike conventional approaches that represent flexible computing power as continuously shiftable loads,a task queue model was established,incorporating the task release time,deadline,energy demand,upper limit of single-task power,upper limit of aggregate computing power,and unfinished energy,so as to characterize the temporal shiftability of computing loads.The energy usage effectiveness varying with ambient temperature was adopted to represent cooling and auxiliary energy consumption in data centers.Constraints on energy storage state-of-charge transitions,mutual exclusivity of charging and discharging,capacity at the point of common coupling,and purchased-power ramping were established.All costs and losses were converted to Chinese yuan,and a mean-conditional value-at-risk mixed integer linear optimization model was developed.At the day-ahead level,a baseline scheme with 96 time intervals was formed based on the prediction information for the next 24 h.At the intra-day level,wind and photovoltaic power,electricity prices,carbon emission factors,energy storage states,and remaining tasks were updated every 4 h,followed by rolling optimization.At the real-time level,task,energy storage,and purchased power were adjusted every 15 min according to actual operating conditions.A parameterized typical day for a green computing power park associated with a wind and photovoltaic power base in Xinjiang was used as a case study,with four schemes considered:fixed tasks without energy storage,task queue optimization,fixed tasks with energy storage,and multi-timescale risk coordination.The results showed that,compared with the fixed-task scheme without energy storage,the proposed scheme reduced operating cost by 61.36%,decreased the curtailment rate from 9.92%to 0,and increased renewable energy utilization from 90.08%to 100%.After equivalent investment was included,the comprehensive cost was 38 709.43 yuan,representing a 4.53%reduction compared with the baseline scheme.Across 400 independent out-of-sample scenarios,the CVaR95 values of cost,carbon emissions,and peak power at the point of common coupling under the risk-based scheme decreased by 1.62%,3.55%,and 1.68%,respectively.However,the single maximum peak value increased slightly,indicating that the proposed method improves the average tail risk in the most adverse scenarios,but does not guarantee superiority in every extreme scenario.The results indicate that coordination between flexible task queues and energy storage can enhance the capacity for local supply from renewable energy,indirectly achieve carbon emission reduction,and balance the economic operation of the park,peak shaving of the distribution network,and the quality of computing services.关键词
绿色算力园区/柔性任务队列/多时间尺度调度/碳排放因子/储能/条件风险价值/算电协同Key words
green computing park/flexible task queue/multi-time-scale dispatch/carbon emission factor/energy storage/conditional value-at-risk/computing-electricity coordination分类
能源科技引用本文复制引用
张志豪,王永真,樊小朝,王子龙,史瑞静,孙剑桥,王维庆,何山,程志江,程静..新疆绿色算力园区多时间尺度低碳调度[J].综合智慧能源,2026,48(8):96-106,11.基金项目
国家自然科学基金项目(72595835,52266018)National Natural Science Foundation of China(72595835,(52266018) （72595835,52266018）
