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Analysis on data center power supply system based on multiple renewable power configurations and multi-objective optimization

Author

Listed:
  • He, Wei
  • Xu, Qing
  • Liu, Shengchun
  • Wang, Tieying
  • Wang, Fang
  • Wu, Xiaohui
  • Wang, Yulin
  • Li, Hailong

Abstract

With rapid development of data center industry, achieving low energy consumption and costs become important. How to provide an optimal configuration on renewable distributed energy systems combining data centers is challenging. This work discussed possible hybrid configurations by combining diesel, photovoltaic, wind, and battery capacity in data center. For each mode, renewable penetration (RP) and the Levelized Cost of Electricity (LCOE) were used to evaluate the performance of system. A multi-objective optimization analysis of the case is performed based on the entropy weighted-TOPSIS method, genetic algorithm, and NSGA-II aiming to obtain both a high RP and a low LCOE. Then, comparative research on optimal power configuration with conventional or renewable energy was conducted. The results show that it is recommended to take a high-rated PV power. When wind power is taken, it is only meaningful when combined with PV power system. For the hybrid renewable power system without an energy storage unit, it's easy to realize a lower LCOE compared to Diesel mode, and its realizable maximum RP is 28.31 %. When battery energy storage is taken in the hybrid system, six optimal configurations are recommended which can achieve the lowest cost of 0.21 $/kWh and the highest RP of 44.5 %.

Suggested Citation

  • He, Wei & Xu, Qing & Liu, Shengchun & Wang, Tieying & Wang, Fang & Wu, Xiaohui & Wang, Yulin & Li, Hailong, 2024. "Analysis on data center power supply system based on multiple renewable power configurations and multi-objective optimization," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148123017809
    DOI: 10.1016/j.renene.2023.119865
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    Cited by:

    1. Luo, Ding & Zhao, Ye & Cao, Jin & Chen, Wei-Hsin & Zhao, Yulong & Cao, Bingyang, 2024. "Performance analysis of a novel thermoelectric-based battery thermal management system," Renewable Energy, Elsevier, vol. 224(C).
    2. Luo, Ding & Zhang, Haokang & Cao, Jin & Yan, Yuyin & Cao, Bingyang, 2024. "Numerical investigation and optimization of a hexagonal thermoelectric generator with diverging fins for exhaust waste heat recovery," Energy, Elsevier, vol. 301(C).

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