计及负载响应特性的多数据中心时空协同运行优化策略

doi:10.11930/j.issn.1004-9649.202412049

中国电力 ›› 2025, Vol. 58 ›› Issue (4): 170-181.DOI: 10.11930/j.issn.1004-9649.202412049

• 面向新型电力系统的智慧用能优化与控制 • 上一篇下一篇

计及负载响应特性的多数据中心时空协同运行优化策略

向世林¹(), 向月¹(), 王晏亮²(), 卢宇³()

1. 四川大学电气工程学院，四川成都　610041
2. 国网重庆市电力有限公司永川供电分公司，重庆　402160
3. 国网江苏省电力有限公司江阴供电分公司，江苏江阴　214400

收稿日期:2024-12-12 录用日期:2025-03-12 发布日期:2025-04-23 出版日期:2025-04-28
作者简介:
向世林（1999），女，硕士研究生，从事数据中心、需求响应研究，E-mail：xiangshilin_scu@163.com
向月（1987），男，通信作者，博士，教授，从事电力系统规划与优化运行、电力市场等研究，E-mail：xiang@scu.edu.cn
王晏亮（1999），男，硕士，从事电力系统规划与优化运行、电力市场等研究，E-mail：wang_yl0300@163.com
卢宇（1999），男，硕士，从事电力系统规划与运行研究，E-mail：luyu_scu@163.com
基金资助:
国家自然科学基金资助项目（52177103）。

Optimization Strategy for Spatiotemporal Cooperative Operation of Multiple Data Centers Considering Load Response Characteristics

XIANG Shilin¹(), XIANG Yue¹(), WANG Yanliang²(), LU Yu³()

1. School of Electrical Engineering, Sichuan University, Chengdu 610065, China
2. State Grid Chongqing Electric Power Co., Ltd. Yongchuan Power Supply Branch, Chongqing 402160, China
3. State Grid Jiangsu Electric Power Co., Ltd. Jiangyin Power Supply Branch, Jiangyin 214400, China

Received:2024-12-12 Accepted:2025-03-12 Online:2025-04-23 Published:2025-04-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.52177103).

摘要/Abstract

摘要：

与仅具备时间调节能力的传统柔性负荷不同，多数据中心之间负载还可进行空间上的迁移，因此成为一种具备时空调节潜力的灵活性需求响应资源。然而，以往多数据中心负荷管理的研究忽略了根据批处理负载响应特性中时效性不同而设置多时间窗口的必要性。对此，提出计及负载响应特性的多数据中心时空协同运行优化策略。首先，对多数据中心业务流与能量流耦合进行建模，灵活配置服务器开机数量。其次，分析批处理负载的时空迁移特性，建立多数据中心需求响应机制。然后，以总运行成本最小化为目标函数，用户满意度为约束，建立多数据中心运行优化模型。最后，以多数据中心为例进行算例分析，仿真结果表明，该方案可以有效激发多数据中心的需求响应潜力，减少总运行成本的同时兼顾用户满意度，提供行之有效的多数据中心负荷管理策略。

关键词: 负载响应特性, 多数据中心, 时空协同, 多时间窗口, 业务流与能量流耦合, 需求响应

Abstract:

Unlike traditional flexible loads that have only temporal regulation capability, the loads of multiple data centers can be migrated among them both in time and spatiality, thus becoming a flexible demand response resource with temporal and spatial regulation potential. However, previous studies on load management of multiple data centers have neglected the need for multiple time windows depending on the timeliness in batch load response characteristics. In this regard, we propose a spatio-temporal cooperative operation optimization strategy for multiple data centers considering the load response characteristics. Firstly, the coupling of business and energy flows of multiple data centers is modeled to flexibly configure the number of servers powered on. Then, the temporal and spatial migration characteristics of batch loads are analyzed to establish a multi-data center demand response mechanism. And then, a multi-data center operation optimization model is established with minimum total operation cost as the objective function and user satisfaction as the constraint. Finally, a case study is carried out with multiple data centers as an example. The simulation results show that the proposed scheme can effectively stimulate the demand response potential of multiple data centers, reduce the total operation cost while ensuring user satisfaction, and provide an effective load management strategy for multiple data centers.

Key words: load response characteristics, multiple data centers, spatio-temporal coordination, multiple time windows, business flow and energy flow coupling, demand response

向世林, 向月, 王晏亮, 卢宇. 计及负载响应特性的多数据中心时空协同运行优化策略[J]. 中国电力, 2025, 58(4): 170-181.

XIANG Shilin, XIANG Yue, WANG Yanliang, LU Yu. Optimization Strategy for Spatiotemporal Cooperative Operation of Multiple Data Centers Considering Load Response Characteristics[J]. Electric Power, 2025, 58(4): 170-181.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202412049

https://www.electricpower.com.cn/CN/Y2025/V58/I4/170

图/表 10

图 1 数据中心运行架构

Fig.1 Data center operational architecture

图 2 批处理负载响应特性

Fig.2 Batch load response characteristics

图 3 电价

Fig.3 electricity price

表 1 数据中心相关参数设置

Table 1 Data center related parameter settings

参数	取值	参数	取值
$ \gamma _i^{{\text{pue}}} $	1.25	$ P_i^{\max } $/kW	0.65
$ m $/%	10	$ P_i^{_{{\text{idle}}}} $/kW	0.25
$ \bar N $/台	10000	$ {d^{{\text{ser}}}} $/(个·h^–1)	85
$ T_{i,t}^{\max } $/h	0.1	$ {v^{{\text{dtol}}}} $/(个·h^–1)	8000
$ \alpha $	90	$ {c^{{\text{mig}}}} $/(元·个^–1)	10^–4

图 4 数据中心业务流和能量流耦合

Fig.4 Coupling of data center business flow and energy flow

图 5 灵活配置服务器后的开机数量

Fig.5 The number of servers powered on after flexible server configuration

表 2 灵活配置服务器前后对比情况

Table 2 Comparison before and after flexible server configuration

对比项	平均开机数量/台	平均CPU 利用率	总用电功率/kW	购电成本/元
灵活配置前	6 000	0.6534	366 082	255 869
灵活配置后	4 308	0.8999	315 321	227 436
变化率/%	–28.2	37.7	–13.9	–11.1

图 6 灵活配置服务器前后变化

Fig.6 Changes before and after flexible server configuration

表 3 各场景运行成本对比

Table 3 Comparison of operating costs by scenario

场景	总成本/元	购电成本/元	迁移成本/元	成本降低率/%
1	227 436	227 436	—	—
2	220 455	220 189	266	3.1
3	216 903	216 496	407	4.6
4	210 469	209 823	646	7.5

图 7 工作负载调度结果

Fig.7 Workload scheduling results

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