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1.太原理工大学电气与动力工程学院,山西省太原市 030024
2.煤电清洁智能控制教育部重点实验室(太原理工大学),山西省太原市 030024
秦雪雪(2001—),女,硕士研究生,主要研究方向:数据中心的能量管理。E-mail:xuexueqin2023@163.com
李泽宁(1994—),男,通信作者,博士,讲师,主要研究方向:城市配电网负荷恢复、集成智能楼宇的主动配电网优化运行。E-mail:lizening@tyut.edu.cn
薛屹洵(1993—),男,博士,教授,主要研究方向:综合能源系统运行与规划。E-mail:xueyixun@tyut.edu.cn
收稿:2024-07-06,
修回:2025-01-14,
网络出版:2025-05-12,
纸质出版:2025-06-10
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秦雪雪,李泽宁,薛屹洵等.计及建筑热动态特性的数据中心时空协调两阶段鲁棒优化方法[J].电力系统自动化,2025,49(11):59-69.
QIN Xuexue,LI Zening,XUE Yixun,et al.Two-stage Robust Optimization Method for Spatio-Temporal Coordination of Data Center Considering Thermal Dynamic Characteristics of Buildings[J].Automation of Electric Power Systems,2025,49(11):59-69.
秦雪雪,李泽宁,薛屹洵等.计及建筑热动态特性的数据中心时空协调两阶段鲁棒优化方法[J].电力系统自动化,2025,49(11):59-69. DOI: 10.7500/AEPS20240706001.
QIN Xuexue,LI Zening,XUE Yixun,et al.Two-stage Robust Optimization Method for Spatio-Temporal Coordination of Data Center Considering Thermal Dynamic Characteristics of Buildings[J].Automation of Electric Power Systems,2025,49(11):59-69. DOI: 10.7500/AEPS20240706001.
为实现数据中心的经济灵活运行,提出一种计及建筑热动态特性的数据中心时空协调两阶段鲁棒优化方法。首先,基于数据中心建筑的热动态特性与云用户负载的差异化实时性需求,构建考虑服务器运行环境温度的数据中心云用户负载时空协调数学模型。其次,考虑到常规负载与室外温度的不确定性,提出计及数据中心建筑热动态特性的数据中心两阶段鲁棒优化方法,通过设置不确定性调节参数灵活调整优化保守程度。然后,采用列与约束生成(C&CG)算法迭代求解得到原问题的最优解。最后,通过算例分析对所提方法进行验证。结果表明,所提方法可在保障服务器运行环境温度的前提下,充分挖掘云用户负载时空灵活性和空调制冷灵活性的协同潜力,显著提升了数据中心运行的经济性,有效提高了系统抵御风险的能力。
To achieve the economic and flexible operation of data centers
a two-stage robust optimization method for spatio-temporal coordination of data center considering thermal dynamic characteristics of buildings is proposed. Firstly
based on the thermal dynamic characteristics of data center buildings and the differentiated real-time requirements of cloud user loads
a spatio-temporal coordination mathematical model of cloud user loads in data centers considering the operation environment temperature of servers is constructed. Secondly
considering the uncertainties of conventional load and outdoor temperature
a two-stage robust optimization method for data center considering the thermal dynamic characteristics of data center buildings is proposed. The degree of optimization conservatism is flexibly adjusted by setting uncertainty adjustment parameters. Then
the column and constraint generation (C&CG) algorithm is adopted for iterative solution to obtain the optimal solution to the original problem. Finally
the proposed method is verified through case analysis. The results show that the proposed method can fully tap the synergy potential of the spatio-temporal flexibility of cloud user loads and the flexibility of air conditioning cooling on the premise of ensuring the operation environment temperature of the server
significantly improve the economy of data center operation
and effectively enhance the system ability to resist risks.
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