综合智慧能源2026，Vol.48Issue(8)：117-129,13.DOI:10.3969/j.issn.2097-0706.2026.08.010
计及算力与储冷灵活性的数据中心多能微网运行优化
Operational optimization of multi-energy microgrids for data centers considering flexibility of computing loads and cold energy storage
摘要
Abstract
To address the hourly temporal mismatch between wind and photovoltaic(PV)power output in industrial parks and the continuous computing and cooling demand of a data center,the complementary regulation mechanism of deferrable computing loads and a sensible-heat water-based cold energy storage system under monthly maximum demand constraints was investigated.A data center multi-energy microgrid model incorporating park-level wind and PV generation,the power grid,data center IT loads,a cooling system,and a sensible-heat water-based cold energy storage system was developed.On the computing side,the aggregate computing demand was divided into a nonshiftable component and arrivals of a shiftable component,and execution times were selected subject to service-window constraints,batch-wise task-conservation constraints,end-of-month queue-clearance constraints,and lower and upper limits on central processing unit utilization.On the cooling side,power models for the coolant distribution unit,facility water pump,computer-room air-handling unit,and chiller were established based on the IT heating load.Cold energy storage system was described using a state equation for cold energy storage,mutually exclusive charging and discharging constraints,capacity constraints,and end-of-period closure constraints.On the grid side,hourly mutual exclusivity between electricity purchase and sale,grid-export capacity,the time-of-use tariff for western Inner Mongolia,and the monthly peak-demand charge were explicitly considered.A three-level lexicographic objective was used to sequentially minimize electricity curtailment,net electricity cost,and ineffective adjustment.The case study used a meteorological series in typical May for Hohhot to model wind and PV output and dry-bulb-temperature-based free cooling.Dew-point and wet-bulb temperatures were used to verify the condensation boundary.The Google 2019 production-cluster trace and the measured Dell PowerEdge R740 SPECpower curve were used to construct a 1 MW equivalent IT load.The main scenario assumed a 20%share of flexible computing tasks,a maximum delay of 4 h,and a cold energy storage system with a rated duration of 2 h(rated charging and discharging cooling power of 651.77 kW and capacity of 1 303.53 kW·h).Baseline,computing-load-shifting-only,cold-energy-storage-only,rule-based-control,perfect-prediction joint optimization,and rolling-optimization scenarios were also established for comparison.The results showed that compared with the baseline scenario,computing-load shifting alone reduced monthly peak demand and net electricity cost by 15.33%and 11.38%,respectively,and cold energy storage alone reduced these values by 9.55%and 8.95%,respectively.Joint optimization reduced them by 17.83%and 16.13%,respectively,while increasing the local self-consumption rate of wind and PV power by 1.66 percentage points.Shapley decomposition showed that computing-load shifting and cold energy storage contributed 66.2%and 33.8%,respectively,to joint peak reduction,and 57.6%and 42.4%,respectively,to net electricity cost savings.Interaction effect analysis showed that the joint variations in grid-export capacity and electricity selling price had a significantly stronger interactive effect than the combined variations in the flexible computing task share and either the rated cold energy storage duration or the maximum delay.Computing-load shifting mainly performs inter-hour energy allocation,whereas cold energy storage mainly provides short-term cooling compensation around peak periods.Under the unified constraints of computing tasks,thermal energy,electric power,and monthly demand,the two systems can operate synergistically to simultaneously improve local consumption of wind and PV power,reduce monthly maximum demand,and lower net electricity cost.关键词
数据中心/需求响应/算力负荷时移/储冷/可再生能源消纳/多能微网/算电协同Key words
data center/demand response/computing-load shifting/cold energy storage/renewable energy consumption/multi-energy microgrid/computing-electricity coordination分类
能源科技引用本文复制引用
杜宇昊,严儒井,李孟兰..计及算力与储冷灵活性的数据中心多能微网运行优化[J].综合智慧能源,2026,48(8):117-129,13.基金项目
贵州省科技支撑计划项目(黔科合支撑[2025]一般021) （黔科合支撑[2025]一般021）
国家自然科学基金项目(52406227)Guizhou Provincial Science and Technology Support Program(Qiankehe-Support[2025]General 021) （52406227）
National Natural Science Foundation of China(52406227) （52406227）
