Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

Abstract

With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

Graphical abstract

Keywords

model predictive control / interconnected data center / multi-timescale / optimized scheduling / distributed power supply / landscape uncertainty

References

[1] Wang H, Huang J W, Lin X J. . Proactive demand response for data centers: A win-win solution. IEEE Transactions on Smart Grid, 2016, 7(3): 1584–1596

[2] Wang W, Abdolrashidi A, Yu N P. . Frequency regulation service provision in data center with computational flexibility. Applied Energy, 2019, 251: 113304

[3] Chen T Y, Zhang Y, Wang X. . Robust workload and energy management for sustainable data centers. IEEE Journal on Selected Areas in Communications, 2016, 34(3): 651–664

[4] Ebrahimi K, Jones G F, Fleischer A S. Thermo-economic analysis of steady state waste heat recovery in data centers using absorption refrigeration. Applied Energy, 2015, 139: 384–397

[5] Han O Z, Ding T, Zhang X S. . A shared energy storage business model for data center clusters considering renewable energy uncertainties. Renewable Energy, 2023, 202: 1273–90

[6] Landré D, Nicod J M, Varnier C. Optimal standalone data center renewable power supply using an offline optimization approach. Sustainable Computing-Informatics & Systems, 2022, 34: 100627

[7] Cao X Y, Zhang J S, Poor H V. Data center demand response with on-site renewable generation: A bargaining approach. IEEE/ACM Transactions on Networking, 2018, 26(6): 2707–2720

[8] Chen Z, Wu L, Li Z. Electric demand response management for distributed large-scale internet data centers. IEEE Transactions on Smart Grid, 2014, 5(2): 651–661

[9] Chen M, Gao C W, Shahidehpour M. . Internet data center load modeling for demand response considering the coupling of multiple regulation methods. IEEE Transactions on Smart Grid, 2021, 12(3): 2060–2076

[10] Lasemi M A, Alizadeh S, Assili M. . Energy cost optimization of globally distributed Internet Data Centers by copula-based multidimensional correlation modeling. Energy Reports, 2023, 9: 631–644

[11] Oró E, Depoorter V, Garcia A. . Energy efficiency and renewable energy integration in data centres. Strategies and modelling review. Renewable & Sustainable Energy Reviews, 2015, 42: 429–445

[12] Cheung H, Wang S W, Zhuang C Q. . A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation. Applied Energy, 2018, 222: 329–342

[13] Nadjahi C, Louahlia H, Lemasson S. A review of thermal management and innovative cooling strategies for data center. Sustainable Computing-Informatics & Systems, 2018, 19: 14–28

[14] Oró E, Codina M, Salom J. Energy model optimization for thermal energy storage system integration in data centres. Journal of Energy Storage, 2016, 8: 129–41

[15] Zhao Q, Xiong C C, Yu C. . A new energy-aware task scheduling method for data-intensive applications in the cloud. Journal of Network and Computer Applications, 2016, 59: 14–27

[16] Yuan H, Bi J, Zhou M C. Spatial task scheduling for cost minimization in distributed green cloud data centers. IEEE Transactions on Automation Science and Engineering, 2019, 16(2): 729–740

[17] Yuan H, Bi J, Zhou M C. Spatiotemporal task scheduling for heterogeneous delay-tolerant applications in distributed green data centers. IEEE Transactions on Automation Science and Engineering, 2019, 16(4): 1686–1697

[18] CioaraTAnghel IAntalM, . Data center optimization methodology to maximize the usage of locally produced renewable energy. In: Proceedings of the 2015 Sustainable Internet and ICT for Sustainability, Madrid, Spain, 2015

[19] Chen T Y, Zhang Y, Wang X. . Robust workload and energy management for sustainable data centers. IEEE Journal on Selected Areas in Communications, 2016, 34(3): 651–664

[20] WangPXie L Y. LU Y, . Day-ahead emission-aware resource planning for data center considering energy storage and batch workloads. In: Proceedings of the IEEE Conference on Energy Internet and Energy System Integration, Beijing, China, 2017

[21] LiuZHuangB HuX, . Blockchain-based renewable energy trading using information entropy theory. IEEE Transactions on Network Science and Engineering, 2023

[22] Jawad M, Qureshi M B, Khan M U S. . A robust optimization technique for energy cost minimization of cloud data centers. IEEE Transactions on Cloud Computing, 2021, 9(2): 447–460

[23] Zhang H F, Xu T, Wu H. . Risk-based stochastic day-ahead operation for data centre virtual power plants. IET Renewable Power Generation, 2019, 13(10): 1660–1669

[24] Ding Z H, Cao Y J, Xie L Y. . Integrated stochastic energy management for data center microgrid considering waste heat recovery. IEEE Transactions on Industry Applications, 2019, 55(3): 2198–2207

[25] Ding Z H, Xie L Y, Lu Y. . Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management. IEEE Transactions on Industry Applications, 2018, 54(6): 5599–5608

[26] PaulDZhong W DBoseS K. Energy efficient scheduling in data centers. In: Proceedings of the 2015 IEEE International Conference on Communications, London, UK, 2015

[27] WuYXueX LeL, . Real-time energy management of large-scale data centers: A model predictive control approach. In: Proceedings of the 2020 IEEE Sustainable Power and Energy Conference, Chengdu, China, 2020

[28] Zhu Y X, Wang J Y, Bi K T. . Energy optimal dispatch of the data center microgrid based on stochastic model predictive control. Frontiers in Energy Research, 2022, 10: 863292

[29] WangHShen H YWiederP, . A data center interconnects calculus. In: 26th IEEE/ACM International Symposium on Quality of Service, Banff, Canada, 2018

[30] Wang H, Ai Q, Wu J. . Bi-level distributed optimization for microgrid clusters based on alternating direction method of multipliers. Power System Technology, 2018, 42(6): 1718–1727

Competing interests

The authors declare that they have no competing interests.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11708-023-0912-6 and is accessible for authorized users.

RIGHTS & PERMISSIONS

2023 Higher Education Press