Data center integrated energy system for sustainability: Generalization, approaches, methods, techniques, and future perspectives

Yongzhen Wang; Yibo Han; Jun Shen; Kai Han; Jian Li; Wenjie Ji; Hailong Li; Rui Jing; Haochen Hua; Dan Wang; Juntao Han; Shuangquan Shao; Jiayu Lin; Jun Zhao; Quan Zhang; Yunfei Zhang; Haoran Zhang; Qiang Yang; Jinyue Yan

doi:10.59717/j.xinn-energy.2024.100014

The Innovation Energy > 2024 Vol. 1 > No. 1 > 100014

Next Article Previous Article

REVIEW Open Access

Cite PDF

Data center integrated energy system for sustainability: Generalization, approaches, methods, techniques, and future perspectives

1.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100083, China
2.
Innovation Center in Chongqing, Beijing Institute of Technology, Chongqing 401120, China
3.
Future Energy Center, Mälardalen University, Västeras 72123, Sweden
4.
College of Energy, Xiamen University, Xiamen 361005, China
5.
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
6.
Key Laboratory of the Ministry of Education on Smart Power Grids, Tianjin University, Tianjin 300072, China
7.
School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
8.
Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Tianjin University, Tianjin 300350, China
9.
College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
10.
School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China
11.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
12.
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China

Show all affliationsShow less

More Information

Corresponding authors: wyz80hou@bit.edu.cn (Y.W.); jshen@bit.edu.cn (J.S.); autosim@bit.edu.cn (K.H.); lijian2022@bit.edu.cn (J.L.); h.zhang@pku.edu.cn (H.Z.); jjyan@polyu.edu.hk (J.Y.)
Received Date: January 23, 2024

Accepted Date: February 22, 2024

Published Online: February 22, 2024

The Innovation Energy 1(1), https://doi.org/10.59717/j.xinn-energy.2024.100014 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

DownLoad: Full size image

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Data center merges integrated energy system by establishing a cohesive energy chain.
  
  Data center integrated energy system exemplifies the global optimization potentials.
  
  Innovative research between computing, power, and heating are reviewed.
  
  Generalization, approaches, methods, techniques, and future perspectives are investigated.

ABSTRACT

ABSTRACT

The increasing prominence of data centers (DCs) in the global digital economy has raised concerns about energy consumption and carbon emissions. Simultaneously, the rapid advancement of integrated energy systems (IES) has enabled DCs to efficiently harness clean energy and waste heat, contributing to sustainability. A concept of data center integrated energy system (DC-IES) is introduced in this paper, and its generalization, approaches, methods, techniques, and future perspectives are scrutinized on the utilization of renewable energy, recovery of waste heat, construction of producer and consumer and diversity of system architecture, as well as optimization solutions in DC-IES planning and design. The evaluation indicators and optimization objectives of DC-IES are also discussed from perspectives of energy, water, economy, environment and reliability. Furthermore, innovative models are proposed for DC-IES to actively engage in the interaction between computing networks and power grids as well as heating networks respectively, while also emphasizing their associated research through the technical principles, participating elements and benefit impacts. It reveals that the integration of non-interactive workloads in the computing network, along with the utilization of flexible resources from diesel generators and uninterruptible power supply, as well as the implementation of waste heat recovery technologies such as heat pumps and absorption refrigeration systems, will significantly enhance the planning and operation of DC-IES. Finally, a series of challenges and future perspectives related to the reliability and cost-effectiveness encountered in the optimization of DC-IES are revealed.
- Data center /
- integrated energy system /
- flexible interaction /
- waste heat recovery /
- heat pump

FullText(HTML)

REFERENCES

[1]	Mytton, D., and Ashtine, M. (2022). Sources of data center energy estimates: A comprehensive review. Joule 6: 2032−2056. DOI: 10.1016/j.joule.2022.07.011. View in Article CrossRef Google Scholar Scopus
[2]	Cheng, S., Zhang, Y., Li, X., et al. (2022). Roadmap toward the metaverse: An AI perspective. The Innovation 3(5). DOI: 10.1016/j.xinn.2022.100293. View in Article Google Scholar
[3]	UNCTAD. (2021). Digital Economy Report 2021: Cross-border Data Flows and Development-For Whom the Data Flow (UN). View in Article Google Scholar
[4]	Zhou, Z., Shojafar, M., Alazab, M., et al. (2021). AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center. IEEE TGCN. 5: 658−669. DOI: 10.1109/TGCN.2021.3067309. View in Article CrossRef Google Scholar Scopus
[5]	Masanet, E., Shehabi, A., Lei, N., et al. (2020). Recalibrating global data center energy-use estimates. Science 367: 984−986. DOI: 10.1126/science.aba3758. View in Article CrossRef Google Scholar Scopus
[6]	Workshop, B., Scao, T.L., Fan, A., et al. (2022). Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv. DOI: 10.48550/arXiv.2211.05100. View in Article Google Scholar
[7]	Yang, J., Jin, H., Tang, R., et al. (2023). Harnessing the power of llms in practice: A survey on chatgpt and beyond. arXiv preprint arXiv. DOI: 10.48550/arXiv.2304.13712. View in Article Google Scholar
[8]	Andrae, A., and Edler, T. (2015). On Global Electricity Usage of Communication Technology: Trends to 2030. Challenges 6(1):117-157. DOI: 10.3390/challe6010117. View in Article Google Scholar
[9]	Trueman, C.(2019). Why data centres are the new frontier in the fight against climate change. https://www.computerworld.com/article/3431148/why-data-centres-are-the-new-frontier-in-the-fight-against-climate-change.html. View in Article Google Scholar
[10]	Wang, Y., Han, Y., Han, K., et al. (2023). Researches on data center integrated energy systems based on knowledge graph. Integr. Intelli. Energy 45: 1-10. DOI: 10. 3969/j. issn. 2097-0706. 2023. 07. 001. View in Article Google Scholar
[11]	Shuja, J., Gani, A., Shamshirband, S., et al. (2016). Sustainable Cloud Data Centers: A survey of enabling techniques and technologies. Renew. Sustain. Energy Rev. 62: 195−214. DOI: 10.1016/j.rser.2016.04.034. View in Article CrossRef Google Scholar Scopus
[12]	Coyne, B., Denny, E., and Fitiwi, D.Z. (2023). The benefits of low-carbon energy efficiency technology adoption for data centres. Energy Conver. Manage. X 20:100447. DOI: https://doi.org/10.1016/j.ecmx.2023.100447. View in Article Google Scholar
[13]	Shah, A.J., Carey, V.P., Bash, C.E., et al. (2008). Exergy analysis of data center thermal management systems. J. Heat Trans. Transac. ASME 130(2). 021401,DOI: 10.1115/1.2787024. View in Article Google Scholar
[14]	Iyengar, M., Schmidt, R.R., and Asme (2007). Analytical modeling of energy consumption and thermal performance of data center cooling systems - From the chip to the environment. IPACK 2007: PROCEEDINGS OF THE ASME INTERPACK CONFERENCE 2007, VOL 1. View in Article Google Scholar
[15]	Xu, L.J., Li, C.L., Li, L.Y., et al. (2014). A virtual data center deployment model based on the green cloud computing. 2014 IEEE/ACIS 13TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS). View in Article Google Scholar
[16]	Nakamura, M., Nakamura, R., Nishida, R., et al. (2012). Energy Efficient Cooling in Data Center Using Optimization Approach. 2012 PROCEEDINGS OF SICE ANNUAL CONFERENCE (SICE). View in Article Google Scholar
[17]	Wan, J., Zhou, J., and Gui, X. (2021). Sustainability analysis of green data centers with CCHP and waste heat reuse systems. IEEE Trans. Sustain. Comput. 6(1):155-167. 9028165,DOI: 10.1109/TSUSC.2020.2979473. View in Article Google Scholar
[18]	Jin, J.X., Zhou, Q., Yang, R.H., et al. (2022). A superconducting magnetic energy storage based current-type interline dynamic voltage restorer for transient power quality enhancement of composited data center and renewable energy source power system. J. Energy Storage 52: 105003. DOI. DOI: 10.1016/j.est.2022.105003. View in Article CrossRef Google Scholar Scopus
[19]	Chen, X., Jiang, S., Chen, Y., et al. (2022). Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center. Appl. Energy 310: 118602. DOI. DOI: 10.1016/j.apenergy.2022.118602. View in Article CrossRef Google Scholar Scopus
[20]	Li, Y., Huang, J., Liu, Y., et al. (2023). A Multicriteria Optimal Operation Framework for a Data Center Microgrid Considering Renewable Energy and Waste Heat Recovery: Use of Balanced Decision Making. IEEE Ind. Appl. Magaz. DOI: 10.1109/ICPS51807.2021.9416605. View in Article Google Scholar
[21]	Li, F., Xing, L., Su, W., et al. (2024). An idea to construct integrated energy systems of data center by combining CO2 heat pump and compressed CO2 energy storage. J. Energy Storage 75: 109581. DOI. DOI: 10.1016/j.est.2023.109581. View in Article CrossRef Google Scholar Scopus
[22]	Lamptey, N.B., Anka, S.K., Lee, K.H., et al. (2023). Comparative energy analysis of cooling energy performance between conventional and hybrid air source internet data center cooling system. Energy Build. 302(2011):113759. DOI: 10.1016/j.enbuild.2023.113759. View in Article Google Scholar
[23]	Zhu, Y., Zhang, Q., Zeng, L., et al. (2023). An advanced control strategy for optimizing the operation state of chillers with cold storage technology in data center. Energy Build. 301:113684. DOI: https://doi.org/10.1016/j.enbuild.2023.113684. View in Article Google Scholar
[24]	Xiao, J.-W., Yang, Y.-B., Cui, S., et al. (2023). Cooperative online schedule of interconnected data center microgrids with shared energy storage. Energy 285: 129522. DOI. DOI: 10.1016/j.energy.2023.129522. View in Article CrossRef Google Scholar Scopus
[25]	Uptime-Institute (2022). Uptime Institute Global Data Center Survey 2022. https://uptimeinstitute.com/uptime_assets/6768eca6a75d792c8eeede827d76de0d0380dee6b5ced20fde45787dd3688bfe-2022-data-center-industry-survey-en.pdf. View in Article Google Scholar
[26]	Huang, P., Copertaro, B., Zhang, X.X., et al. (2020). A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating. Appl. Energy 258. DOI: 10.1016/j.apenergy.2019.114109. View in Article Google Scholar
[27]	Wang, J.J., Deng, H.D., Liu, Y., et al. (2023). Coordinated optimal scheduling of integrated energy system for data center based on computing load shifting. Energy 267: 126585. DOI: 10.1016/j.energy.2022.126585. View in Article Google Scholar
[28]	Lyu, J., Zhang, S., Cheng, H., et al. (2021). Optimal Sizing of Energy Station in the Multienergy System Integrated With Data Center. IEEE Trans. Ind. Appl. 57: 1222−1234. DOI: 10.1109/TIA.2021.3054607. View in Article CrossRef Google Scholar
[29]	Huan, J.J., Ding, Q.Y., Yu, T., et al. (2023). Multi-stage low-carbon planning of an integrated energy system considering demand response. Front. Energy Res. 11: 1259067. DOI: 10.3389/fenrg.2023.1259067. View in Article Google Scholar
[30]	Amin, S.M.M., Hossain, N., Lipu, M.S.H., et al. (2023). Development of a PV/Battery Micro-Grid for a Data Center in Bangladesh: Resilience and Sustainability Analysis. Sustainability 15(22): 15691. DOI: 10.3390/su152215691. View in Article Google Scholar
[31]	He, H., Shen, H., Hao, Q., et al. (2022). Online delay-guaranteed workload scheduling to minimize power cost in cloud data centers using renewable energy. J. Paral. Distri. Comput. 159: 51−64. DOI: 10.1016/j.jpdc.2021.09.002. View in Article CrossRef Google Scholar Scopus
[32]	Alipour, M., Deymi-Dashtebayaz, M., and Asadi, M. (2023). Investigation of energy, exergy, and economy of co-generation system of solar electricity and cooling using linear parabolic collector for a data center. Energy 279: 128076. DOI: 10.1016/j.energy.2023.128076. View in Article Google Scholar
[33]	Zhang, C., Han, Z., Dong, J., et al. (2024). A novel data center air conditioner and its application scheme balancing high-efficiency cooling and waste heat recovery: Environmental and economic analysis. Energy 291:130294. DOI: 10.1016/j.energy.2024.130294. View in Article Google Scholar
[34]	Li, C.Y., Zheng, K.D., Guo, H.Y., et al. (2023). Intra-day optimal power flow considering flexible workload scheduling of IDCs. Energy Rep. 9: 1149−1159. DOI: 10.1016/j.egyr.2023.04.139. View in Article CrossRef Google Scholar Scopus
[35]	Wang, Y.Z., Li, Z.H., Wen, F.S., et al. (2020). Energy Management for an Integrated Energy System with Data Centers Considering Carbon Trading. 2020 IEEE PESGM. DOI: 10.1109/PESGM41954.2020.9281980. View in Article Google Scholar
[36]	Wang, Y., Kang, L., Zhang, J., et al. (2021). Development history, typical form and future trend of integrated energy system. Acta Energiae Solaris Sin. 42: 84-95. (in Chinese). DOI: 10.19912/j.0254-0096.tynxb.2019-0763. View in Article Google Scholar
[37]	Gu, W., Wang, J., Lu, S., et al. (2017). Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings. Appl. Energy 199:234-246. DOI: 10.1016/j.apenergy.2017.05.004. View in Article Google Scholar
[38]	Mu, C., Ding, T., Qu, M., et al. (2020). Decentralized optimization operation for the multiple integrated energy systems with energy cascade utilization. Appl. Energy 280:115989. DOI: 10.1016/j.apenergy.2020.115989. View in Article Google Scholar
[39]	Wang, B., Ma, H., Wang, F., et al. (2023). An IoT-Enabled Stochastic Operation Management Framework for Smart Grids. IEEE Trans. Intelli. Transport. Sys. 24: 1025−1034. DOI: 10.1109/TITS.2022.3183327. View in Article CrossRef Google Scholar Scopus
[40]	Goiri, Í., Haque, M.E., Le, K., et al. (2015). Matching renewable energy supply and demand in green datacenters. Ad. Hoc. Networks 25:520-534. DOI: 10.1016/j.adhoc.2014.11.012. View in Article Google Scholar
[41]	Lei, H., Zhang, T., Liu, Y., et al. (2015). SGEESS: Smart green energy-efficient scheduling strategy with dynamic electricity price for data center. J. Sys. Software 108:23-38. DOI: https://doi.org/10.1016/j.jss.2015.06.026. View in Article Google Scholar
[42]	Mengmeng, Z., and Xiaoying, W. (2020). A Survey of Research on Datacenters Using Energy Storage Devices to Participate in Smart Grid Demand Response. 2020 IEEE ICPICS. DOI: 10.1109/ICPICS50287.2020.9202343. View in Article Google Scholar
[43]	Oró, E., Taddeo, P., and Salom, J. (2019). Waste heat recovery from urban air cooled data centres to increase energy efficiency of district heating networks. Sustain. Cities Soc. 45:522-542. DOI: 10.1016/j.scs.2018.12.012. View in Article Google Scholar
[44]	Davies, G.F., Maidment, G.G., and Tozer, R.M. (2016). Using data centres for combined heating and cooling: An investigation for London. Appl. Thermal Eng. 94:296-304. DOI: 10.1016/j.applthermaleng.2015.09.111. View in Article Google Scholar
[45]	Manu, S., and Chandrashekar, T.K. (2017). On-chip waste heat-driven absorption cooling system for sustainable data center environment: simulation. Int. J. Sustain. Eng. 11: 1−16. DOI: 10.1080/19397038.2017.1390006. View in Article CrossRef Google Scholar Scopus
[46]	Wilde, T., Ott, M., Auweter, A., et al. (2017). CooLMUC-2: A supercomputing cluster with heat recovery for adsorption cooling. 2017 33rd SEMI-THERM. DOI: 10.1109/SEMI-THERM.2017.7896917. View in Article Google Scholar
[47]	Zhao, J., Chang, H., Luo, X., et al. (2022). Dynamic analysis of a CCHP system based on fuel cells integrated with methanol-reforming and dehumidification for data centers. Appl. Energy 309: 118496. DOI: 10.1016/j.apenergy.2021.118496. View in Article Google Scholar
[48]	Tian, M., Zhang, H., and Wu, H. (2021). Energy management for data centre microgrids considering co-optimisation of workloads and waste heat. IET Energy Sys. Integrat. DOI: 10.1049/esi2.12044. View in Article Google Scholar
[49]	Liu, J., Xu, Z., Wu, J., et al. (2021). Optimal Planning of Internet Data Centers Decarbonized by Hydrogen-Water-Based Energy Systems. IEEE CASE. DOI: 10.1109/CASE49439.2021.9551571. View in Article Google Scholar
[50]	Cai, S., Zou, Y., Luo, X., et al. (2021). Investigations of a novel proton exchange membrane fuel cell-driven combined cooling and power system in data center applications. Energy Conver. Manag. 250: 114906. DOI: 10.1016/j.enconman.2021.114906. View in Article Google Scholar
[51]	Sustainability in Tencent Cloud. (2024). https://www.tencentcloud.com/global-infrastructure/sustainability##. View in Article Google Scholar
[52]	Korhonen, S. (2018). Energy Efficiency of Modern Datacenter. https://api.semanticscholar.org/CorpusID:116244546. View in Article Google Scholar
[53]	Liu, J., Tang, Z., Yu, M., et al. (2024). Robust expansion planning model of integrated energy system with energy hubs integrated. Electr. Power Sys. Res. 226:109947. DOI: https://doi.org/10.1016/j.jpgr.2023.109947. View in Article Google Scholar
[54]	Babagheibi, M., Sahebi, A., Jadid, S., et al. (2023). An Integrated Design of Heat and Power Market for Energy Hubs Considering the Security Constraints of the System. Sustain. Cities Soc. 96:104616. DOI: https://doi.org/10.1016/j.scs.2023.104616. View in Article Google Scholar
[55]	Liu, Z.Y., Yu, H., Liu, R., et al. (2020). Configuration Optimization Model for Data-Center-Park-Integrated Energy Systems under Economic, Reliability, and Environmental Considerations. Energies 13(2): 448. DOI: 10.3390/en13020448. View in Article Google Scholar
[56]	Yuan, H., Feng, K., Li, W., et al. (2022). Multi-objective optimization of virtual energy hub plant integrated with data center and plug-in electric vehicles under a mixed robust-stochastic model. J. Cleaner Product. 363: 132365. DOI: 10.1016/j.jclepro.2022.132365. View in Article Google Scholar
[57]	Keskin, I., and Soykan, G. (2022). Optimal cost management of the CCHP based data center with district heating and district cooling integration in the presence of different energy tariffs. Energy Conver. Manag. 254:115211. DOI: 10.1016/j.enconman.2022.115211. View in Article Google Scholar
[58]	Tian, Q., Guo, Q., Nojavan, S., et al. (2021). Robust optimal energy management of data center equipped with multi-energy conversion technologies. J. Cleaner Product. 329:129616. DOI: 10.1016/j.jclepro.2021.129616. View in Article Google Scholar
[59]	Liu, Q., Chen, S., Chen, M., et al. (2018). Energy Management for Internet Data Centers Considering the Coordinating Optimization of Workload and CCHP System. 2nd IEEE EI. 2018 - Proceedings. View in Article Google Scholar
[60]	Liu, C., Meng, C., Jing, R., et al. (2019). Multi-objective Optimization Design and Scheduling of Integrated Energy System in A-level Data Center. Auto. Electric Power Sys. 43: 136−142. DOI: 10.7500/AEPS20180731007. View in Article CrossRef Google Scholar Scopus
[61]	Guo, C., Luo, F., Cai, Z., et al. (2021). Integrated planning of internet data centers and battery energy storage systems in smart grids. Appl. Energy 281:116093. DOI: 10.1016/j.apenergy.2020.116093. View in Article Google Scholar
[62]	Zhou, D., Yan, S., Huang, D., et al. (2022). Modeling and simulation of the hydrogen blended gas-electricity integrated energy system and influence analysis of hydrogen blending modes. Energy 239:121629. DOI: 10.1016/j.energy.2021.121629. View in Article Google Scholar
[63]	Mei, F., Zhang, J., Lu, J., et al. (2021). Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations. Energy 219:119629. DOI: 10.1016/j.energy.2020.119629. View in Article Google Scholar
[64]	Shaker Al-Sayyab, A.K., Navarro-Esbri, J., and Mota-Babiloni, A. (2022). Energy, exergy, and environmental (3E) analysis of a compound ejector-heat pump with low GWP refrigerants for simultaneous data center cooling and district heating. Int. J. Refrig. 133: 61−72. DOI: 10.1016/j.ijrefrig.2021.09.036. View in Article CrossRef Google Scholar Scopus
[65]	Norani, M., and Deymi-Dashtebayaz, M. (2022). Energy, exergy and exergoeconomic optimization of a proposed CCHP configuration under two different operating scenarios in a data center: Case study. J. Cleaner Product. 342: 130971. DOI: 10.1016/j.jclepro.2022.130971. View in Article Google Scholar
[66]	Gong, X., Zhang, Z., Gan, S., et al. (2020). A review on evaluation metrics of thermal performance in data centers. Build. Environ. 177:106907. DOI: 10.1016/j.buildenv.2020.106907. View in Article Google Scholar
[67]	Shao, X., Zhang, Z., Song, P., et al. (2022). A review of energy efficiency evaluation metrics for data centers. Energy Build. 271:112308. DOI: 10.1016/j.enbuild.2022.112308. View in Article Google Scholar
[68]	Huawei. (2023). On the Top 10 Trends of Data Center Facilities. https://digitalpower.huawei.com/attachments/index/e8c4f4fd705a4d48ad229502e3eb668b.pdf. View in Article Google Scholar
[69]	Li, J., Jurasz, J., Li, H., et al. (2020). A new indicator for a fair comparison on the energy performance of data centers. Appl. Energy 276:115497. DOI: 10.1016/j.apenergy.2020.115497. View in Article Google Scholar
[70]	Wang, D., Xie, C., Wu, R., et al. (2021). Optimal Energy Scheduling for Data Center with Energy Nets including CCHP and Demand Response. IEEE Access 9:6137-6151. DOI: 10.1109/ACCESS.2020.3049066. View in Article Google Scholar
[71]	Lin, H., Peng, Y., Xia, Y., et al. (2020). Summary of my country's data center energy consumption evaluation method standards. Energy China 42: 36-39. (in Chinese). View in Article Google Scholar
[72]	Zhang, J., and Pan, J. (2021). Research on the standard compilation of "Evaluation Method for Comprehensive Utilization of Data Center Energy". Energy China 43:60-66+86. (in Chinese). View in Article Google Scholar
[73]	Chen, X.Y., Zhang, M.S., Jiang, S., et al. (2023). Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage. Energy 263:125622. DOI: 10.1016/j.energy.2022.125622. View in Article Google Scholar
[74]	Chen, S., Li, P., Ji, H., et al. (2021). Operational flexibility of active distribution networks with the potential from data centers. Appl. Energy 293:116935. DOI: 10.1016/j.apenergy.2021.116935. View in Article Google Scholar
[75]	Li, J., Yang, Z., Li, H., et al. (2021). Optimal schemes and benefits of recovering waste heat from data center for district heating by CO₂transcritical heat pumps. Energy Conver. Manage. 245: 114591. DOI: 10.1016/j.enconman.2021.114591. View in Article Google Scholar
[76]	Chen, Y., Zhang, L., Xu, P., et al. (2021). Electricity demand response schemes in China: Pilot study and future outlook. Energy 224:120042. DOI: 10.1016/j.energy.2021.120042. View in Article Google Scholar
[77]	Luo, Z., and Lu, X. (2023). Characteristics and prospect of geothermal industry in China under the “dual carbon” target. Energy Geo. 4(4):100199. DOI: 10.1016/j.engeos.2023.100199. View in Article Google Scholar
[78]	Wahlroos, M., Parssinen, M., Rinne, S., et al. (2018). Future views on waste heat utilization - Case of data centers in Northern Europe. Renew. Sustain. Energy Rev. 82: 1749−1764. DOI: 10.1016/j.rser.2017.10.058. View in Article CrossRef Google Scholar
[79]	Chen, M., Gao, C., Song, M., et al. (2020). Internet data centers participating in demand response: A comprehensive review. Renew. Sustain. Energy Rev. 117: 109466. DOI: 10.1016/j.rser.2019.109466. View in Article Google Scholar
[80]	Chen, M., Gao, C., Guo, Q., et al. (2022). Modeling and Coordinated Optimization for Spatiotemporal Load Regulation Potentials of Internet Data Centers: Motivation and Architecture. Proceed. CSEE 42: 6945-6958. (in Chinese). View in Article Google Scholar
[81]	Mamun, A., Narayanan, I., Wang, D., et al. (2016). Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage. J. Energy Storage 7:258-269. DOI: 10.1016/j.est.2016.08.002. View in Article Google Scholar
[82]	Sutaria, J., and Rönnberg, S.K. (2023). Propagation of Supraharmonics in a Data Center With Different Operating Modes of UPS. IEEE Access 11: 36823−36833. DOI: 10.1109/ACCESS.2023.3266092. View in Article CrossRef Google Scholar Scopus
[83]	Zhou, K.L., Fei, Z.E., and Lu, X.H. (2023). Optimal Energy Management of Internet Data Center With Distributed Energy Resources. IEEE Trans. Cloud Comput. 11: 2285−2295. DOI: 10.1109/TCC.2022.3196655. View in Article CrossRef Google Scholar
[84]	Tao, Y., Qiu, J., Lai, S., et al. (2022). Market-Based Resource Allocation of Distributed Cloud Computing Services: Virtual Energy Storage Systems. IEEE Int.Things J. 9: 22811−22821. DOI: 10.1109/JIOT.2022.3184750. View in Article CrossRef Google Scholar Scopus
[85]	Zhang, M., Guan, J., Tu, Y., et al. (2021). Highly efficient conversion of surplus electricity to hydrogen energy via polysulfides redox. The Innovation 2(3):100144. DOI: 10.1016/j.xinn.2021.100144. View in Article Google Scholar
[86]	Almalaq, A., Albadran, S., Alghadhban, A., et al. (2022). An Effective Hybrid-Energy Framework for Grid Vulnerability Alleviation under Cyber-Stealthy Intrusions. Mathematics 10(14): 2510. DOI: 10.3390/math10142510. View in Article Google Scholar
[87]	Errapotu, S.M., Li, H., Yu, R., et al. (2020). Clock Auction Inspired Privacy Preserving Emergency Demand Response in Colocation Data Centers. IEEE Trans. Depend. Secure Comput. 17: 691−702. DOI: 10.1109/TDSC.2018.2875732. View in Article CrossRef Google Scholar
[88]	Sun, Q., Wu, C., Li, Z., et al. (2016). Colocation Demand Response: Joint Online Mechanisms for Individual Utility and Social Welfare Maximization. IEEE J. Selected Areas Commun. 34(12): 3978−3992. DOI: 10.1109/JSAC.2016.2611918. View in Article CrossRef Google Scholar Scopus
[89]	Cao, Y., and Zhang, S. (2023). Facilitating the provision of load flexibility to the power system by data centers: A hybrid research method applied to China. Utilities Policy 84:101636. DOI: 10.1016/j.jup.2023.101636. View in Article Google Scholar
[90]	Cortez, E., Bonde, A., Muzio, A., et al. (2017). Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. SOSP'17: 153-167. DOI: 10.1145/3132747.3132772. View in Article Google Scholar
[91]	Li, Y., Wang, X., Luo, P., et al. (2019). Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization. Energies 12(8). DOI: 10.3390/en12081494. View in Article Google Scholar
[92]	Wang, D., Zhang, S., Cheng, H., et al. (2023). Coordinated Planning of Multi-regional Energy Stations Considering Spatio-Temporal Adjustment of Energy Consumption in Data Centers. Auto. Electric Power Sys. 47: 77-85. (in Chinese). View in Article Google Scholar
[93]	Chen, M., Gao, C., Song, M., et al. (2020). Internet data centers participating in demand response: A comprehensive review. Renew. Sustain. Energy Rev. 117: 109466. DOI: 10.1016/j.rser.2019.109466. View in Article Google Scholar
[94]	Waisberg, E., Ong, J., Masalkhi, M., et al. (2023). Google’s AI chatbot “Bard”: a side-by-side comparison with ChatGPT and its utilization in ophthalmology. Eye. DOI: 10.1038/s41433-023-02760-0. View in Article Google Scholar
[95]	Abdullah, M., Madain, A., and Jararweh, Y. (2022). ChatGPT: Fundamentals, Applications and Social Impacts. SNAMS. DOI: 10.1109/SNAMS58071.2022.10062688. View in Article Google Scholar
[96]	Wierman, A., Liu, Z., Liu, I., et al. (2014). Opportunities and challenges for data center demand response. Int. Green Comput. Conf. DOI: 10.1109/IGCC.2014.7039172. View in Article Google Scholar
[97]	Shoreh, M.H., Siano, P., Shafie-khah, M., et al. (2016). A survey of industrial applications of Demand Response. Electric Power Sys. Res. 141:31-49. DOI: 10.1016/j.jpgr.2016.07.008. View in Article Google Scholar
[98]	Al Kez, D., Foley, A.M., Ahmed, F.W., et al. (2021). Potential of data centers for fast frequency response services in synchronously isolated power systems. Renew. Sustain. Energy Rev. 151:111547. DOI: 10.1016/j.rser.021.111547. View in Article Google Scholar
[99]	Yang, T., Li, Y., Pen, H., et al. (2017). Tie-line Power Fluctuation Smoothing Algorithm Based on Data Center Demand Response. Proceed. CSEE 37: 5529-5540+5830. (in Chinese). View in Article Google Scholar
[100]	Wang, F., Harindintwali, J.D., Yuan, Z., et al. (2021). Technologies and perspectives for achieving carbon neutrality. The Innovation 2(4):100180. DOI: 10.1016/j.xinn.2021.100180. View in Article Google Scholar
[101]	Toosi, A.N., Qu, C., de Assuncao, M.D., et al. (2017). Renewable-aware geographical load balancing of web applications for sustainable data centers. J. Network Comput. Appl. 83: 155−168. DOI: 10.1016/j.jnca.2017.01.036. View in Article CrossRef Google Scholar Scopus
[102]	Wang, Y., Zhang, L., Song, Y., et al. (2024). State-of-the-art review on evaluation indicators of integrated intelligent energy from different perspectives. Renew. Sustain. Energy Rev. 189:113835. DOI: 10.1016/j.rser.2023.113835. View in Article Google Scholar
[103]	Hu, Y., Feng, Z., and Song, W. (2023). Study on performance of a water-source gas engine-driven heat pump system for combined cooling and heating supply. Thermal Sci. Eng. Prog. 39:101726. DOI: 10.1016/j.tsep.2023.101726. View in Article Google Scholar
[104]	Wang, Y., Hu, C., Wu, B., et al. (2020). Matching Characteristic Research of Building Renewable Energy System Based on Virtual Energy Storage of Air Conditioning Load. Energies 13(5):1269. DOI: 10.3390/en13051269. View in Article Google Scholar
[105]	Lan, Z., Jiang, C., Gu, J., et al. (2022). Optimal Dispatch and Demand Response Strategies of Data Centers for Promoting Accommodation of Renewable Energy Generation and Reducing Carbon Emission. Electr. Power Construc. 43:1-9. (in Chinese). View in Article Google Scholar
[106]	Wahlroos, M., Pärssinen, M., Rinne, S., et al. (2018). Future views on waste heat utilization – Case of data centers in Northern Europe. Renew. Sustain. Energy Rev. 82:1749-1764. DOI: 10.1016/j.rser.2017.10.058. View in Article Google Scholar
[107]	Ebrahimi, K., Jones, G.F., and Fleischer, A.S. (2014). A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities. Renew. Sustain. Energy Rev. 31:622-638. DOI: 10.1016/j.rser.2013.12.007. View in Article Google Scholar
[108]	Zhang, Q., Meng, Z., Hong, X., et al. (2021). A survey on data center cooling systems: Technology, power consumption modeling and control strategy optimization. J. Sys. Archi. 119:102253. DOI: 10.1016/j.sysarc.2021.102253. View in Article Google Scholar
[109]	Nadjahi, C., Louahlia, H., and Lemasson, S. (2018). A review of thermal management and innovative cooling strategies for data center. Sustain. Comput. Inform. Sys. 19:14-28. DOI: 10.1016/j.suscom.2018.05.002. View in Article Google Scholar
[110]	Nandhini, R., Sivaprakash, B., and Rajamohan, N. (2022). Waste heat recovery at low temperature from heat pumps, power cycles and integrated systems – Review on system performance and environmental perspectives. Sustain. Energy Techn. Assess. 52:102214. DOI: 10.1016/j.seta.2022.102214. View in Article Google Scholar
[111]	Yuan, X., Liang, Y., Hu, X., et al. (2023). Waste heat recoveries in data centers: A review. Renew. Sustain. Energy Rev. 188:113777. DOI: 10.1016/j.rser.2023.113777. View in Article Google Scholar
[112]	Deymi-Dashtebayaz, M., and Norani, M. (2021). Sustainability assessment and emergy analysis of employing the CCHP system under two different scenarios in a data center. Renew. Sustain. Energy Rev. 150:111511. DOI: 10.1016/j.rser.2021.111511. View in Article Google Scholar
[113]	Al-Sayyab, A.K.S., Mota-Babiloni, A., and Navarro-Esbrí, J. (2023). Renewable and waste heat applications for heating, cooling, and power generation based on advanced configurations. Energy Conver. Manag. 291:117253. DOI: 10.1016/j.enconman.2023.117253. View in Article Google Scholar
[114]	Ziviani, D., Beyene, A., and Venturini, M. (2014). Advances and challenges in ORC systems modeling for low grade thermal energy recovery. Appl. Energy 121:79-95. DOI: 10.1016/j.apenergy.2014.01.074. View in Article Google Scholar
[115]	Rahbar, K., Mahmoud, S., Al-Dadah, R.K., et al. (2017). Review of organic Rankine cycle for small-scale applications. Energy Conver. Manag. 134:135-155. DOI: 10.1016/j.enconman.2016.12.023. View in Article Google Scholar
[116]	Ebrahimi, K., Jones, G.F., and Fleischer, A.S. (2017). The viability of ultra low temperature waste heat recovery using organic Rankine cycle in dual loop data center applications. Appl. Thermal Eng. 126: 393−406. DOI: 10.1016/j.applthermaleng.2017.07.001. View in Article CrossRef Google Scholar Scopus
[117]	Marshall, Z.M., and Duquette, J. (2022). A techno-economic evaluation of low global warming potential heat pump assisted organic Rankine cycle systems for data center waste heat recovery. Energy 242: 122528. DOI: 10.1016/j.energy.2021.122528. View in Article Google Scholar
[118]	Ljungqvist, H.M., Mattsson, L., Risberg, M., et al. (2021). Data center heated greenhouses, a matter for enhanced food self-sufficiency in sub-arctic regions. Energy 215:119169. DOI: https://doi.org/10.1016/j.energy.2020.119169. View in Article Google Scholar
[119]	Judge, P. (2022). EcoDataCenter to reuse heat in fish farms and greenhouses. https://www.datacenterdynamics.com/en/news/ecodatacenter-to-reuse-heat-in-fish-farms-and-greenhouses/. View in Article Google Scholar
[120]	Hanley, S. (2023). Waste Heat From Data Centers Can Bolster District Heat Systems. https://cleantechnica.com/2022/12/29/waste-heat-from-data-centers-can-bolster-district-heat-systems/. View in Article Google Scholar
[121]	Jang, Y., Lee, D., Kim, J., et al. (2022). Performance characteristics of a waste-heat recovery water-source heat pump system designed for data centers and residential area in the heating dominated region. J. Build. Eng. 62: 105416. DOI: 10.1016/j.jobe.2022.105416. View in Article Google Scholar
[122]	Deymi-Dashtebayaz, M., and Valipour-Namanlo, S. (2019). Thermoeconomic and environmental feasibility of waste heat recovery of a data center using air source heat pump. J. Cleaner Product. 219: 117−126. DOI: 10.1016/j.jclepro.2019.02.061. View in Article CrossRef Google Scholar Scopus
[123]	Montagud-Montalvá, C., Navarro-Peris, E., Gómez-Navarro, T., et al. (2023). Recovery of waste heat from data centres for decarbonisation of university campuses in a Mediterranean climate. Energy Conver. Manag. 290:117212. DOI: 10.1016/j.enconman.2023.117212. View in Article Google Scholar
[124]	Burton, G., and DatacenterDynamics. (2020). AWS Dublin data center to contribute to new district heating scheme. https://www.datacenterdynamics.com/en/news/aws-dublin-data-center-contribute-new-district-heating-scheme/. View in Article Google Scholar
[125]	Anand, S., Gupta, A., and Tyagi, S.K. (2013). Simulation studies of refrigeration cycles: A review. Renew. Sustain. Energy Rev. 17:260-277. DOI: 10.1016/j.rser.2012.09.021. View in Article Google Scholar
[126]	Nikbakhti, R., Wang, X., Hussein, A.K., et al. (2020). Absorption cooling systems – Review of various techniques for energy performance enhancement. Alex. Eng. J. 59(2):707-738. DOI: 10.1016/j.aej.2020.01.036. View in Article Google Scholar
[127]	Xu, Z.Y., and Wang, R.Z. (2016). Absorption refrigeration cycles: Categorized based on the cycle construction. International J. Refrig. 62:114-136. DOI: 10.1016/j.ijrefrig.2015.10.007. View in Article Google Scholar
[128]	Mohtaram, S., Omidi, M., Lin, J., et al. (2019). Exergy analysis of a multi mixture working fluid absorption refrigeration cycle. Case Studies Thermal Eng. 15:100540. DOI: 10.1016/j.csite.2019.100540. View in Article Google Scholar
[129]	Wu, X., Xu, S., and Jiang, M. (2018). Development of bubble absorption refrigeration technology: A review. Renew. Sustain. Energy Rev. 82:3468-3482. DOI: 10.1016/j.rser.2017.10.109. View in Article Google Scholar
[130]	Ebrahimi, K., Jones, G.F., and Fleischer, S. (2015). Thermo-economic analysis of steady state waste heat recovery in data centers using absorption refrigeration. Appl. Energy 139: 384−397. DOI: 10.1016/j.apenergy.2014.10.067. View in Article CrossRef Google Scholar Scopus
[131]	Gupta, R., and Puri, I.K. (2021). Waste heat recovery in a data center with an adsorption chiller: Technical and economic analysis. Energy Conver. Manag. 245: 114576. DOI: 10.1016/j.enconman.2021.114576. View in Article Google Scholar
[132]	Han, B., Li, W., Li, M., et al. (2020). Study on Libr/H₂O absorption cooling system based on enhanced geothermal system for data center. Energy Rep. 6: 1090−1098. DOI: 10.1016/j.egyr.2020.11.072. View in Article CrossRef Google Scholar
[133]	Pan, Q., Peng, J., and Wang, R. (2021). Application analysis of adsorption refrigeration system for solar and data center waste heat utilization. Energy Conver. Manage. 228: 113564. DOI: 10.1016/j.enconman.2020.113564. View in Article Google Scholar
[134]	Pan, Q., Peng, J., and Wang, R. (2019). Experimental study of an adsorption chiller for extra low temperature waste heat utilization. Appl. Thermal Eng. 163:114341. DOI: 10.1016/j.applthermaleng.2019.114341. View in Article Google Scholar
[135]	Fridgen, G., Keller, R., Thimmel, M., et al. (2017). Shifting load through space–The economics of spatial demand side management using distributed data centers. Energy Policy 109:400-413. DOI: 10.1016/j.enpol.2017.07.018. View in Article Google Scholar
[136]	Flanagan, C., and Alliance, S.D.I. (2013). A data center perspective on demand response. https://www.datacenterdynamics.com/en/news/a-data-center-perspective-on-demand-response/. View in Article Google Scholar
[137]	Yao, J., Lu, P., Gong, L., et al. (2015). On Fast and Coordinated Data Backup in Geo-Distributed Optical Inter-Datacenter Networks. J. Lightwave Techn. 33(14): 3005−3015. DOI: 10.1109/JLT.2015.2425303. View in Article CrossRef Google Scholar Scopus
[138]	Zhou, A.C., Xiao, Y., Gong, Y., et al. (2019). Privacy Regulation Aware Process Mapping in Geo-Distributed Cloud Data Centers. IEEE Tran. Paral. Distr. Sys. 30: 1872−1888. DOI: 10.1109/TPDS.2019.2896894. View in Article CrossRef Google Scholar Scopus
[139]	Energie-Agentur, D. (2022). https://transition-china.org/wp-content/uploads/2022/05/0419Data_Centre_CN_Final.pdf. View in Article Google Scholar
[140]	Chatterjee, S., Misra, S., and Khan, S.U. (2015). Optimal data center scheduling for quality of service management in sensor-cloud. IEEE Tran. Cloud Comput. 7: 89−101. DOI. DOI: 0.1109/TCC.2015.2487973. View in Article CrossRef Google Scholar
[141]	Luo, G., and Teng, J.X. (2018). Bureaucracy Short-Circuits Distributed-Power Trading Plan. https://www.caixinglobal.com/2018-01-08/bureaucracy-short-circuits-distributed-power-trading-plan-101195072.html. View in Article Google Scholar
[142]	Linde, M. (2019). Economic and Environmental Benefits of the ORC and the Willingness to Invest. https://liu.diva-portal.org/smash/get/diva2:1412470/FULLTEXT01.pdf. View in Article Google Scholar
[143]	Pärssinen, M., Wahlroos, M., Manner, J., et al. (2019). Waste heat from data centers: An investment analysis. Sustain. Cities Soc. 44:428-444. DOI: 10.1016/j.scs.2018.10.023. View in Article Google Scholar
[144]	Kou, X., Wang, R., Du, S., et al. (2024). Heat pump assists in energy transition: Challenges and approaches. DeCarbon 3:100033. DOI: 10.1016/j.decarb.2023.100033. View in Article Google Scholar
[145]	Liu, X. (2024). Research on collaborative scheduling of internet data center and regional integrated energy system based on electricity-heat-water coupling. Energy 292:130462. DOI: 10.1016/j.energy.2024.130462. View in Article Google Scholar
[146]	Li, W., Qian, T., Zhang, Y., et al. (2023). Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty. Appl. Energy 336:120787. DOI: 10.1016/j.apenergy.2023.120787. View in Article Google Scholar
[147]	Ren, X., Han, Z., Ma, J., et al. (2024). Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers. Energy 288:129679. DOI: 10.1016/j.energy.2023.129679. View in Article Google Scholar
[148]	Wang, Z., Zhang, J., Liu, J., et al. (2024). Data association load uncertainty and risk aversion in electricity markets with data center participation in the demand response. Energy Reports 11:483-497. DOI: 10.1016/j.egyr.2023.12.012. View in Article Google Scholar
[149]	Cioara, T., Anghel, I., Antal, M., et al. (2015). Data center optimization methodology to maximize the usage of locally produced renewable energy. SustainIT. DOI: 10.1109/SustainIT.2015.7101363. View in Article Google Scholar
[150]	Cioara, T., Anghel, I., Salomie, I., et al. (2019). Exploiting data centres energy flexibility in smart cities: Business scenarios. Information Sciences 476:392-412. DOI: 10.1016/j.ins.2018.07.010. View in Article Google Scholar
[151]	Ham, S.-W., Kim, M.-H., Choi, B.-N.,et al. (2015). Simplified server model to simulate data center cooling energy consumption. Energy Build. 86:328-339. DOI: 10.1016/j.enbuild.2014.10.058. View in Article Google Scholar
[152]	Wang, L., Cheng, H., Yang, T., et al. (2023). Modeling and Performance Analysis of a Pump-Driven Chip-Level Two-Phase Cooling System in Data Centers. Appl. Sci. Basel 13(13): 7472. DOI: 10.3390/app13137472. View in Article Google Scholar
[153]	Wang, X., Wen, Q., Yang, J., et al. (2022). A review on data centre cooling system using heat pipe technology. Sustainable Computing: Inform. Sys. 35:100774. DOI: 10.1016/j.suscom.2022.100774. View in Article Google Scholar
[154]	Liu, L., Zhang, Q., Zhai, Z.J., et al. (2020). State-of-the-art on thermal energy storage technologies in data center. Energy Build. 226: 110345. DOI: 10.1016/j.enbuild.2020.110345. View in Article Google Scholar
[155]	Zhu, Y., Zhang, Q., Zeng, L., et al. (2024). An advanced control strategy of hybrid cooling system with cold water storage system in data center. Energy 291:130304. DOI: 10.1016/j.energy.2024.130304. View in Article Google Scholar
[156]	Han, O., Ding, T., Mu, C., et al. (2023). Waste heat reutilization and integrated demand response for decentralized optimization of data centers. Energy 264:126101. DOI: 10.1016/j.energy.2022.126101. View in Article Google Scholar
[157]	Yu, L., Jiang, T., and Zou, Y. (2018). Price-Sensitivity Aware Load Balancing for Geographically Distributed Internet Data Centers in Smart Grid Environment. IEEE Trans. Cloud Comput. 6: 1125−1135. DOI: 10.1109/TCC.2016.2564406. View in Article CrossRef Google Scholar
[158]	Chen, M., Gao, C., Chen, S., et al. (2019). Bi-level Economic Dispatch Modeling Considering the Load Regulation Potential of Internet Data Centers. Proceed. CSEE 39: 1301-1314. (in Chinese). View in Article Google Scholar
[159]	Alinezhadi, A., Sheikholeslami, S.M., Atapour, S.K., et al. (2023). Intelligent privacy-preserving demand response for green data centers. Electr. Power Sys. Res. 221:109394. DOI: 10.1016/j.jpgr.2023.109394. View in Article Google Scholar
[160]	Cao, Y., Cheng, M., Zhang, S., et al. (2022). Data-driven flexibility assessment for internet data center towards periodic batch workloads. Appl. Energy 324: 119665. DOI: 10.1016/j.apenergy.2022.119665. View in Article Google Scholar
[161]	Wang, J., Deng, H., Liu, Y., et al. (2023). Coordinated optimal scheduling of integrated energy system for data center based on computing load shifting. Energy 267: 126585. DOI: 10.1016/j.energy.2022.126585. View in Article Google Scholar
[162]	Rao, L., Liu, X., Xie, L., et al. (2011). Hedging Against Uncertainty: A Tale of Internet Data Center Operations Under Smart Grid Environment. IEEE Trans. Smart Grid 2(3): 555−563. DOI: 10.1109/TSG.2011.2159523. View in Article CrossRef Google Scholar
[163]	Yu, L., Jiang, T., Zou, Y., et al. (2016). Joint Energy Management Strategy for Geo-Distributed Data Centers and Electric Vehicles in Smart Grid Environment. IEEE Trans. Smart Grid 7(5): 2378−2392. DOI: 10.1109/TSG.2016.2542261. View in Article CrossRef Google Scholar Scopus
[164]	Li, J., and Qi, W. (2018). Toward Optimal Operation of Internet Data Center Microgrid. IEEE Trans. Smart Grid 9: 971−979. DOI: 10.1109/TSG.2016.2572402. View in Article CrossRef Google Scholar
[165]	Guo, Y., Gong, Y., Fang, Y., et al. (2014). Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage. IEEE Trans. Paral. Distri. Sys. 25: 2030−2042. DOI: 10.1109/TPDS.2013.278. View in Article CrossRef Google Scholar
[166]	He, Z., Ding, T., Liu, Y., et al. (2018). Analysis of a district heating system using waste heat in a distributed cooling data center. Appl. Thermal Eng. 141: 1131−1140. DOI: 10.1016/j.applthermaleng.2018.06.036. View in Article CrossRef Google Scholar Scopus
[167]	Deymi-Dashtebayaz, M., Valipour Namanlo, S., and Arabkoohsar, A. (2019). Simultaneous use of air-side and water-side economizers with the air source heat pump in a data center for cooling and heating production. Appl. Thermal Eng. 161: 114133. DOI: 10.1016/j.applthermaleng.2019.114133. View in Article Google Scholar
[168]	Yu, J., Jiang, Y., and Yan, Y. (2019). A simulation study on heat recovery of data center: A case study in Harbin, China. Renew. Energy 130: 154−173. DOI: 10.1016/j.renene.2018.06.067. View in Article CrossRef Google Scholar Scopus
[169]	Murphy, A.R., and Fung, A.S. (2019). Techno-economic study of an energy sharing network comprised of a data centre and multi-unit residential buildings for cold climate. Energy Build. 186: 261−275. DOI: 10.1016/j.enbuild.2019.01.012. View in Article CrossRef Google Scholar Scopus
[170]	Wang, X., Li, H., Wang, Y., et al. (2021). Energy, exergy, and economic analysis of a data center energy system driven by the CO2 ground source heat pump: Prosumer perspective. Energy Conver. Manag. 232: 13877. DOI: 10.1016/j.enconman.2021.113877. View in Article Google Scholar
[171]	Wahlroos, M., Parssinen, M., Manner, J., et al. (2017). Utilizing data center waste heat in district heating Impacts on energy efficiency and prospects for low-temperature district heating networks. Energy 140: 1228−1238. DOI: 10.1016/j.energy.2017.08.078. View in Article CrossRef Google Scholar
[172]	Li, H., Hou, J., Hong, T., et al. (2021). Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres. Energy 219: 119582. DOI: 10.1016/j.energy.2020.119582. View in Article Google Scholar
[173]	Araya, S., Jones, G.F., Fleischer, A.S., et al. (2016). The Design and Construction of a Bench-top Organic Rankine Cycle for Data Center Applications. ITHERM. DOI: 10.1109/ITHERM.2016.7517577. View in Article Google Scholar
[174]	Lin, X., Zuo, L., Yin, L., et al. (2022). An idea to efficiently recover the waste heat of Data Centers by constructing an integrated system with carbon dioxide heat pump, mechanical subcooling cycle and lithium bromide-water absorption refrigeration cycle. Energy Conver. Manag. 256: 115398. DOI: 10.1016/j.enconman.2022.115398. View in Article Google Scholar
[175]	Khalid, R., Schon, S.G., Ortega, A., et al. (2019). Waste Heat Recovery Using Coupled 2-Phase Cooling & Heat-Pump Driven Absorption Refrigeration. ITHERM. DOI: 10.1109/ITHERM.2019.8757465. View in Article Google Scholar
[176]	Kim, Y.J., Joshi, Y.K., and Fedorou, A.G. (2008). An absorption based miniature heat pump system for electronics cooling. Int. J. Referg. Revue Int. DU FROID 31: 23−33. DOI: 10.1016/j.ijrefrig.2007.07.003. View in Article CrossRef Google Scholar Scopus
[177]	Chiriac, V., and Chiriac, F. (2010). Absorption refrigeration method with alternative water-ammonia solution circulation system for microelectronics cooling. ITHERM. DOI: 10.1109/ITHERM.2010.5501391. View in Article Google Scholar
[178]	Liang, Y., Lin, X., Su, W., et al. (2023). Preliminary design and optimization of a solar-driven combined cooling and power system for a data center. Energy Conver. Manag. X 20:100409. DOI: 10.1016/j.ecmx.2023.100409. View in Article Google Scholar
[179]	Zhang, P., Wang, B., Wu, W., et al. (2015). Heat recovery from Internet data centers for space heating based on an integrated air conditioner with thermosyphon. Renew. Energy 80: 396−406. DOI: 10.1016/j.renene.2015.02.032. View in Article CrossRef Google Scholar

Cited by

ABOUT THIS ARTICLE

Cite this article:

Wang Y., Han Y., Shen J., et al., (2024). Data center integrated energy system for sustainability: Generalization, approaches, methods, techniques, and future perspectives. The Innovation Energy 1(1): 100014. https://doi.org/10.59717/j.xinn-energy.2024.100014

Wang Y., Han Y., Shen J., et al., (2024). Data center integrated energy system for sustainability: Generalization, approaches, methods, techniques, and future perspectives. The Innovation Energy 1(1): 100014. https://doi.org/10.59717/j.xinn-energy.2024.100014

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

PDF

Figures(7) Tables(3)

Article Metrics

Article views(31582) PDF downloads(25845)

Data center integrated energy system for sustainability: Generalization, approaches, methods, techniques, and future perspectives

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}

Data center integrated energy system for sustainability: Generalization, approaches, methods, techniques, and future perspectives

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

Export File

Citation

Format

Content

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}