ZHANG Liang,WANG Hao,WANG Dianbin,et al.Three-stage Migration Model of Multiple Data Centers for Green Power Accommodation and Computing Power Migration Profit[J].Automation of Electric Power Systems,2026,50(19):136-146.
ZHANG Liang,WANG Hao,WANG Dianbin,et al.Three-stage Migration Model of Multiple Data Centers for Green Power Accommodation and Computing Power Migration Profit[J].Automation of Electric Power Systems,2026,50(19):136-146.DOI: 10.7500/AEPS20251114003.
Three-stage Migration Model of Multiple Data Centers for Green Power Accommodation and Computing Power Migration Profit
In response to the coexistence problems arising from the rapid growth of computing power scale
including surge increment of data center loads
the local power shortages and green power curtailment
and the practical problems of the insufficient incentive for data center operators to participate in scheduling
this paper proposes a three-stage migration model of multiple data centers for green power accommodation and computing power migration profit. In the first stage
following the priority scheduling sequence of green power
the maximum flow algorithm is adopted to realize the capacity matching between green power and the power consumption of transferable load. In the second stage
a greedy algorithm is employed for secondary scheduling to further enhance economic efficiency. In the third stage
migration paths are adjusted again to support the coordinated accommodation of local and inter-regional green power. Simulation results demonstrate that the proposed model can simultaneously improve the revenue of data center operators and the economic benefits of power grids
alleviate the power supply pressure of individual data centers
and guarantee the compliance of service level agreements
realizing the win-win collaborative optimization among computing power
Ding Zhaohao , Cao Yujie , Zhang Sufang , et al . Coordinated operation for data center and power system in the context of energy Internet (Ⅰ): energy demand management model of data center [J]. Proceedings of the CSEE , 2022 , 42 ( 9 ): 3161 - 3176 .
Chu W X , Wang C C . A review on airflow management in data centers [J]. Applied Energy , 2019 , 240 : 84 - 119 .
China Industry Research Institute . Data center industry in China in 2025: the golden track of “new infrastructure”under the computing power revolution [EB/OL]. [ 2025-07-25 ]. https://www.chinairn.com/news/20250618/112204320.shtml https://www.chinairn.com/news/20250618/112204320.shtml .
Diouani S , Medromi H . How energy consumption in the cloud data center is calculated [C]// 2019 International Conference of Computer Science and Renewable Energies (ICCSRE) , 2019 .
Gao Ciwei , Cao Xiaojun , Yan Huaguang , et al . Energy management of data center and prospect for participation in demand side resource scheduling [J]. Automation of Electric Power Systems , 2017 , 41 ( 23 ): 1 - 7 .
Lin W W , Yu T H , Gao C Z , et al . A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers [J]. Information Sciences , 2021 , 547 : 1045 - 1065 .
Alan I , Arslan E , Kosar T . Energy-aware data transfer tuning [C]// 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing , 2014 : 626 - 634 .
Zapater M , Tuncer O , Ayala J L , et al . Leakage-aware cooling management for improving server energy efficiency [J]. IEEE Transactions on Parallel and Distributed Systems , 2015 , 26 ( 10 ): 2764 - 2777 .
Bai Y , Gu L J , Qi X . Comparative study of energy performance between chip and inlet temperature-aware workload allocation in air-cooled data center [J]. Energies , 2018 , 11 ( 3 ): 669 .
Ham S W , Kim M H , Choi B N , et al . Simplified server model to simulate data center cooling energy consumption [J]. Energy and Buildings , 2015 , 86 : 328 - 339 .
Arroba P , Risco-Martín J L , Zapater M , et al . Server power modeling for run-time energy optimization of cloud computing facilities [J]. Energy Procedia , 2014 , 62 : 401 - 410 .
Dou Y , Egdell R G , Law D S L , et al . An experimental and theoretical investigation of the electronic structure of CDO [J]. Journal of Physics: Condensed Matter , 1998 , 10 ( 38 ): 8447 .
Yu Lujie , Zhou Huan , Zhu Jiebei , et al . Thermodynamic model and variable temperature operation strategy for renewable energy consumption in data centers [J/OL]. Automation of Electric Power Systems [ 2025-04-14 ]. https://link.cnki.net/urlid/32.1180.TP.20250411.1121.004 https://link.cnki.net/urlid/32.1180.TP.20250411.1121.004 .
Zhu Jiebei , Yang Shunpo , Yu Lujie , et al . Modeling and variable temperature control for instant consumption of renewable energy in data center cooling systems [J]. Automation of Electric Power Systems , 2022 , 46 ( 20 ): 13 - 22 .
Zhang Hong , Zheng Guangyu , Lou Juan , et al . Regulation strategy of waste heat compensation system for data center considering thermo-electric load mismatch [J]. Automation of Electric Power Systems , 2025 , 49 ( 13 ): 156 - 165 .
Weng Q Z , Yang L Y , Yu Y H , et al . Beware of fragmentation: scheduling GPU-sharing workloads with fragmentation gradient descent [EB/OL].[ 2025-10-10 ]. https://www.usenix.org/conference/atc23/presentation/weng https://www.usenix.org/conference/atc23/presentation/weng .
He Y , Fan J Q , Lin J Y , et al . Two-stage robust planning of data center microgrid considering batch load flexibility and multi-energy complementarity [J]. Energy Conversion and Management: X , 2025 , 28 : 101266 .
Yang L F , Chen X Q , Fang X L , et al . Optimal coordinated management of integrated electricity-heat-computation systems in geographically distributed data centers [J]. Energy , 2025 , 332 : 137232 .
Hu Y Y , Yang J , Ruan X L , et al . Green optimization for micro data centers: task scheduling for a combined energy consumption strategy [J]. Applied Energy , 2025 , 393 : 126031 .
Zeng Bo , Liu Yixian , Zhang Weixiang , et al . A collaborative optimization approach for green data center multi-domain resource planning considering both endogenous and exogenous uncertainties [J]. Proceedings of the CSEE , 2024 , 44 ( 12 ): 4732 - 4747 .
Wu Yunyun , Fang Jiakun , Ai Xiaomeng , et al . Shared energy storage planning for data center alliance considering demand response [J]. Automation of Electric Power Systems , 2023 , 47 ( 7 ): 42 - 50 .
Cheung H , Wang S W , Zhuang C Q , et al . A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation [J]. Applied Energy , 2018 , 222 : 329 - 342 .
Liang Chen , Zeng Bo , Lei Leyi , et al . Integrated planning of data centers and distribution networks under the computing-electricity jointmarket: a multi-objective interval-stochastic optimization approach [J]. Power System Protection and Control , 2025 , 53 ( 16 ): 120 - 135 .
Zhang Liang , Wang Dianbin , Qi Jiajin , et al . Research on low-carbon demand response mechanism for electric vehicles based on dynamic carbon emission factors [J]. Power System Protection and Control , 2025 , 53 ( 17 ): 13 - 24 .
Qi Bing , Cao Wangzhang , Li Bin , et al . Bi-level optimization model of multiple data centers considering workload features and response characteristics [J]. Automation of Electric Power Systems , 2022 , 46 ( 21 ): 30 - 41 .
Ma Haotian , Hu Junjie , Tong Yuxuan . A two-stage robust planning approach for data center microgrids considering flexibility [J]. Proceedings of the CSEE , 2023 , 43 ( 19 ): 7396 - 7408 .