基于分层序列法的数据中心电-冷-算协同优化运行策略

茅云寿; 范俊秋; 黄淳驿; 唐学用

doi:10.19783/j.cnki.pspc.260309

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基于分层序列法的数据中心电-冷-算协同优化运行策略

应用研究 | 更新时间：2026-09-28

- 基于分层序列法的数据中心电-冷-算协同优化运行策略
- Coordinated optimal operation strategy for data center power-cooling-computing based on hierarchical sequence method
- 电力系统保护与控制 2026年54卷第19期
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- DOI：10.19783/j.cnki.pspc.260309
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- 纸质出版：2026
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茅云寿, 范俊秋, 黄淳驿, 等. 基于分层序列法的数据中心电-冷-算协同优化运行策略[J]. 电力系统保护与控制, 2026,54(19).

MAO Yunshou, FAN Junqiu, HUANG Chunyi, et al. Coordinated optimal operation strategy for data center power-cooling-computing based on hierarchical sequence method[J]. Power System Protection and Control, 2026, 54(19).
茅云寿, 范俊秋, 黄淳驿, 等. 基于分层序列法的数据中心电-冷-算协同优化运行策略[J]. 电力系统保护与控制, 2026,54(19). DOI： 10.19783/j.cnki.pspc.260309.

MAO Yunshou, FAN Junqiu, HUANG Chunyi, et al. Coordinated optimal operation strategy for data center power-cooling-computing based on hierarchical sequence method[J]. Power System Protection and Control, 2026, 54(19). DOI： 10.19783/j.cnki.pspc.260309.

摘要

针对数据中心电-冷-算多能流深度耦合与算力服务合规保障需求，以及传统调度方法在处理异质指标权衡时存在的收敛性差、决策主观性强等问题，提出一种计及能源利用效率(power usage effectiveness

PUE)凸化与工程优先级的协同调度方法。首先，构建算力时间窗与延迟惩罚机制量化任务柔性，并建立基于自适应安全裕度的机会约束模型。其次，设计“PUE凸化 + 分层序列”高效求解框架：利用分层序列法确立“合规-服务-经济”决策优先级，规避传统加权法弊端，并引入改进逐次凸逼近算法实现非凸PUE模型的快速寻优。算例结果表明：所提方法将系统综合PUE降至1.1417，在95%置信水平下实现安全与经济的帕累托最优。相比传统加权法，本策略在精准挖掘服务质量(quality of service

QoS)宽容度的同时，突破了非凸模型易陷入局部最优的局限，计算速度提升约55%，近似误差低至0.026%。

Abstract

To address the deep coupling of power-cooling-computing multi-energy flows in data centers and the requirement for computing service compliance assurance

as well as the poor convergence and strong decision subjectivity of traditional scheduling methods when handling heterogeneous metric trade-offs

this paper proposes a cooperative scheduling method considering power usage effectiveness (PUE) convexification and engineering priority. First

a computing time window and a delay penalty mechanism are constructed to quantify task flexibility. Meanwhile

a chance-constrained model based on adaptive safety margins is established. Subsequently

an efficient PUE convexification and hierarchical sequence solving framework is designed. This framework utilizes a hierarchical sequence method to establish a “compliance-service-economy” decision-making priority

thereby avoiding the drawbacks of traditional weighting methods. Furthermore

an improved sequential convex programming (SCP) algorithm is introduced to achieve rapid optimization of the non-convex PUE model. Case studies demonstrate that the proposed method reduces the comprehensive system PUE to 1.1417 and achieves Pareto optimality between security and economy at a 95% confidence level. Compared with traditional weighting methods

the proposed strategy accurately exploits the tolerance of quality of service (QoS) while overcoming the limitation of non-convex models being prone to local optima

thereby improving computational speed by approximately 55% with an approximation error as low as 0.026%.

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references

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HUANG Xiaoge, ZHAO Tianqiao, HUANG Bin, et al. Advancing energy system optimization via data-centric task-oriented forecasting: an application in PV-battery operation[J]. Applied Energy, 2025, 378.

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