上海交通大学学报

上海交通大学学报

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计及㶲效率的低碳数据中心算力综合能源系统规划及运行优化（网络首发）

  

  1. 1. 北京理工大学机械与车辆学院；2. 北京理工大学重庆创新中心
  • 基金资助:
    国家自然科学基金青年科学基金（52006114）资助项目

Capacity Planning and Operational Optimization for Low-Carbon Data Center Integrated Energy System Considering Exergy Efficiency

  1. 1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100083, China; 2. Innovation Center in Chongqing, Beijing Institute of Technology, Chongqing 401120, China

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摘要： 随着数字经济的快速发展，数据中心的能耗及其碳排放显著增加。近些年，数据中心算力综合能源系统（data center integrated energy system， DC-IES）的构建已经成为全球净零排放趋势下数据中心（data center，DC）节能减排的重要趋势之一。为助力低碳DC-IES规划与构建，本文基于热力学第二定律?的“品质”分析法，综合考虑能源转化设备?效率随设备负载率变化的特性，构建计及设备动态?特性的低碳DC-IES容量配置及运行优化能量和经济的多目标规划优化模型，揭示了不同目标下DC-IES 的能流分布特性。结果表明，计及动态设备?效率的优化方案相较恒定设备?效率的优化方案，?损失率、经济成本、碳排放量分别降低2.6%、1.9%、4.8%，存在明显优势。同时，相较于经济最优方案，多目标优化方案能够显著降低DC-IES的碳排放量及?损失率，降低幅度分别为22.72%和20.73%；相较于?损失率最小和碳排放量最低方案，多目标优化方案下DC-IES经济成本分别降低54.54%和60.78%；与仅使用电网供电的方案相比，将DC视为综合能源系统的多目标优化方案能够降低40.97%的碳排放量。

关键词: 数据中心, 综合能源系统, ?效率, 算力, 能效

Abstract: With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers have significantly increased. The concept and methodology of Data Center Integrated Energy Systems (DC-IES) have emerged as an important trend for energy efficiency and emissions reduction in the context of the carbon peaking and carbon neutrality goals. this paper employs an energy "quality" analysis method based on exergy and characterizes the change of key equipments’ exergy efficiency with load rates. It constructs a capacity configuration and operational optimization model for DC-IES driven by low-carbon energy sources, achieving multi-objective optimization of DC-IES energy system economic cost and efficiency. The computational results indicate that, The optimization scheme that takes into account the dynamic exergy efficiency outperforms the scheme treating the exergy efficiency as a fixed value, with reductions in the rate of loss of exergy, economic cost and carbon emissions of 2.6%, 1.9% and 4.8%, respectively. Compared to the economically optimal single-objective optimization scenario, the multi-objective optimization scenario achieves a notable reduction of 22.72% in DC-IES carbon emissions and a decrease in the exergy loss rate by 20.73%. Furthermore, compared to scenarios with the minimum exergy loss rate and the minimum carbon emissions separately, the multi-objective optimization scenario reduces DC-IES economic costs by 54.54% and 60.78%, respectively. In comparison to scenarios relying solely on grid electricity supply, carbon emissions witness a significant decrease by 40.97%.

Key words: data center (DC), integrated energy system（IES）, exergy efficiency, computational power, energy efficiency

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