基于蒙特卡洛模拟法的人工智能数据中心备用电源供电可靠性分析
Reliability Analysis of Backup Power Supply in AI Data Centers Based on Monte Carlo Simulation Method
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摘要: 本研究采用蒙特卡洛模拟法,针对人工智能数据中心(Artificial Intelligence Data Center,AIDC)备用电源配置场景,对比分析柴油发电机与第三路市电方案的供电可靠性。通过构建包含市电中断、发电机启动失败等随机事件的失效概率模型,进行大规模随机抽样,量化评估各方案的年供电可用性指标。模拟结果表明,第三路市电方案在供电可靠性上与柴油发电机方案基本持平,同时可有效规避后者占地面积大、日常维护复杂、运行利用率低等工程痛点,为数据中心电源架构的绿色化与集约化转型提供了定量决策依据。Abstract: This study employs the Monte Carlo simulation method to compare the power supply reliability of two configurations for backup power in Artificial Intelligence Data Centers (AIDC): diesel generators and a third utility power source. By constructing a failure probability model incorporating random events such as utility power interruptions and generator startup failures, large-scale random sampling is conducted to quantify the annual power supply availability metrics of each configuration. The simulation results indicate that the third utility power source solution achieves comparable reliability to the diesel generator approach while effectively mitigating engineering challenges such as high land footprint, complex daily maintenance, and low operational utilization rates. This provides quantitative decision-making support for the green and intensive transformation of data center power architectures.
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