Biomass and Solar Complementary Cooling and Power Systems for Data Centers and Their Capacity Configuration

CHEN Fujie; ZHANG Wanglin; CHENG Ming; XIN Yu; HONG Hui; GAO Lin

doi:10.13334/j.0258-8013.pcsee.242318

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Biomass and Solar Complementary Cooling and Power Systems for Data Centers and Their Capacity Configuration

更新时间：2026-10-12

- Biomass and Solar Complementary Cooling and Power Systems for Data Centers and Their Capacity Configuration
- Vol. 46, Issue 4, Pages: 1396-1406(2026)
- 作者机构：
  
  1. 华北电力大学能源动力与机械工程学院，北京市昌平区,102206
  2. 中国科学院工程热物理研究所，北京市海淀区,100190
  3. 中国煤炭销售运输有限责任公司，北京市海淀区,100120
  4. 中国科学院大学，北京市海淀区,100049
  5. 怀柔实验室，北京市昌平区,101407
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.242318
  CLC：
- Published：2026
- 稿件说明：
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CHEN Fujie, ZHANG Wanglin, CHENG Ming, et al. Biomass and Solar Complementary Cooling and Power Systems for Data Centers and Their Capacity Configuration[J]. 2026, 46(4): 1396-1406.
DOI：

CHEN Fujie, ZHANG Wanglin, CHENG Ming, et al. Biomass and Solar Complementary Cooling and Power Systems for Data Centers and Their Capacity Configuration[J]. 2026, 46(4): 1396-1406. DOI： 10.13334/j.0258-8013.pcsee.242318.

摘要

面向规模化数据中心的绿色算力需求不断增加，该研究探索一种太阳能与生物质互补制甲烷燃料的综合能源系统，利用光伏制氢与生物质气化制甲烷燃料，通过燃气轮机布雷顿循环与内燃机奥托循环有机结合的发电模式，以提供数据中心绿色算力的需求。基于NSGA-II算法，建立多目标容量配置优化模型，以容量利用率和系统整体效率为目标，获得数据中心一周负荷需求下的Pareto最优解集。研究结果表明，系统选择内燃机容量为29.3 MW，燃气轮机容量为5.25 MW。这种配置下，燃气轮机与内燃机的容量利用率达到81.34%，系统整体效率为41.33%。燃气轮机和内燃机的配置分析表明，随着燃气轮机和内燃机规模的增大，容量利用率呈下降趋势；在一定范围内，系统整体效率随着设备规模的增大而提高，随后趋于平稳。该研究探索将数据中心的绿色算力与综合能源系统有机结合，展示了绿色能源技术在数据中心的应用潜力，有助于实现数据中心可持续发展目标。

Abstract

As the demand for green computing power in large-scale data centers continues to grow

this study explores an integrated energy system that combines solar energy and biomass to produce methane fuel. Utilizing photovoltaic hydrogen production and biomass gasification for methane fuel

the system integrates gas turbine Brayton cycles and internal combustion engine Otto cycles to meet the green computing power requirements of data centers. Based on the NSGA-II algorithm

a multi-objective capacity allocation optimization model is established

targeting capacity utilization and overall system efficiency

to obtain the Pareto optimal solution set for one week of data center load demand. The results indicate that the optimal configuration includes an internal combustion engine capacity of 29.3 MW and a gas turbine capacity of 5.25 MW. Under this configuration

the capacity utilization of the gas turbine and internal combustion engine reaches 81.34%

and the overall system efficiency is 41.33%. Analysis of the configuration shows that as the scale of gas turbines and internal combustion engines increases

capacity utilization decreases; within a certain range

overall system efficiency increases with equipment scale and then stabilizes. This study explores the integration of green computing power with comprehensive energy systems in data centers

demonstrating the potential of green energy technologies in data center applications and contributing to the achievement of sustainable development goals for data centers.

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