
1.沈阳工业大学电气工程学院,辽宁省沈阳市 110870
2.东北大学信息科学与工程学院,辽宁省沈阳市 110819
Received:14 November 2025,
Revised:2026-03-06,
Online First:06 May 2026,
Published:10 October 2026
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佟邢宇,孙秋野,任汝飞等.计及数据中心热电耦合特性的需求响应调节能力量化[J].电力系统自动化,2026,50(19):77-86.
TONG Xingyu,SUN Qiuye,REN Rufei,et al.Quantification of Demand Response Regulation Capability Considering Thermoelectric Coupling Characteristics in Data Centers[J].Automation of Electric Power Systems,2026,50(19):77-86.
佟邢宇,孙秋野,任汝飞等.计及数据中心热电耦合特性的需求响应调节能力量化[J].电力系统自动化,2026,50(19):77-86. DOI: 10.7500/AEPS20251114002.
TONG Xingyu,SUN Qiuye,REN Rufei,et al.Quantification of Demand Response Regulation Capability Considering Thermoelectric Coupling Characteristics in Data Centers[J].Automation of Electric Power Systems,2026,50(19):77-86. DOI: 10.7500/AEPS20251114002.
数据中心作为电力系统负荷侧的重要可调资源,其服务器计算功耗与制冷功耗间的热电耦合特性直接影响其在需求响应中的调节潜力与经济收益。为保证数据中心参与需求响应的报量精度、避免因偏差引致的经济惩罚,需对其日内各时段的调节能力进行精确量化。为此,文中提出一种考虑中央处理器(CPU)工况变动下制冷功耗变化的数据中心调节能力量化方法。首先,建立工作负载与服务器运行温度的关联模型,依据CPU工况变化计算服务器温度分布及制冷功耗;其次,深入剖析空间转移、时间推迟策略下的能效响应机理,揭示了随着工作负载转移或推迟比例的增加,制冷功耗变化对调节能力贡献的边际递减与饱和现象;最后,基于实际服务器集群数据进行仿真验证,量化计及热电耦合影响的数据中心需求响应调节能力。仿真结果验证了所提方法能有效修正调节容量评估偏差,为数据中心规避经济考核风险及电网制定安全调度决策提供精确的量化依据。
As an important regulable resource on the load side of the power system
the thermoelectric coupling characteristics between server computing power consumption and cooling power consumption in a data center (DC) directly affect its regulation potential and economic benefits in demand response. To ensure the accuracy of capacity reporting for DCs participating in demand response and to avoid economic penalties caused by assessment deviations
it is necessary to precisely quantify their regulation capability across various intraday time periods. To this end
this paper proposes a quantification method for data center regulation capability that accounts for variations in cooling power consumption under varying central processing unit (CPU) operating conditions. Firstly
a correlation model between workload and server operating temperature is established to calculate server temperature distribution and cooling power consumption based on CPU working condition changes. Secondly
the energy efficiency response mechanisms under spatial transfer and temporal postponement strategies are analyzed in depth
revealing the marginal diminishing returns and saturation phenomena of the contribution of cooling power consumption changes to regulation capability as the proportion of transferred or postponed workload increases. Finally
simulation based on real-world server cluster data are conducted to quantify the demand response regulation capability of DCs considering thermoelectric coupling effects. Simulation results verify that the proposed method effectively corrects regulation capacity assessment deviations
providing a precise quantitative basis for data centers to mitigate economic penalty risks and for the power grid to formulate safe scheduling decisions.
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