计及负荷时空转移需求响应的数据中心聚合商最优运行策略

doi:10.11930/j.issn.1004-9649.202401079

中国电力 ›› 2024, Vol. 57 ›› Issue (3): 20-26.DOI: 10.11930/j.issn.1004-9649.202401079

• 数字化技术驱动的新型配电网 • 上一篇下一篇

计及负荷时空转移需求响应的数据中心聚合商最优运行策略

甘润东¹(), 龙玉江¹(), 汤杰¹(), 何熙¹(), 罗鸿轩²^,³(), 金鑫²^,³()

1. 贵州电网有限责任公司信息中心，贵州贵阳　550002
2. 南方电网科学研究院有限责任公司，广东广州　510663
3. 广东省电网智能量测与先进计量企业重点实验室，广东广州　510663

收稿日期:2024-01-17 出版日期:2024-03-28 发布日期:2024-03-26
作者简介:甘润东（1988—），男，硕士，工程师，从事智能电网与大数据研究，E-mail：598821471@qq.com
龙玉江（1976—），男，高级工程师，从事电力大数据研究，E-mail：lixun8266687@163.com
罗鸿轩（1992—），男，通信作者，硕士，高级工程师，从事电力需求侧管理、智能物联传感装备科技研发，E-mail：642986893@qq.com
基金资助:
中国南方电网有限责任公司科技项目（066700KK52222019）。

Optimal Operation Strategy of Data Center Aggregators for Demand Response Considering Temporal and Spatial Load Shift

Rundong GAN¹(), Yujiang LONG¹(), Jie TANG¹(), Xi HE¹(), Hongxuan LUO²^,³(), Xin JIN²^,³()

1. Information Center of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
2. CSG Electric Power Research Institute, Guangzhou 510663, China
3. Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510663, China

Received:2024-01-17 Online:2024-03-28 Published:2024-03-26
Supported by:
This work is supported by the Science and Technology Project of China Southern Power Grid (No.066700KK52222019).

摘要/Abstract

摘要：

利用聚合技术构建数据中心聚合商以参与系统需求响应，提出一种计及负荷时空转移需求响应的数据中心聚合商最优运行策略。首先，根据数据中心的物理特性，建立负荷时空转移需求响应模型；然后，以数据中心运行成本最小化和数据中心聚合商利益最大化为目标，建立计及负荷时空转移需求响应的数据中心聚合商最优运行模型；最后，通过Karush-Kuhn-Tucker（KKT）条件以及大M法将双层模型转换成单层混合整数线性规划模型进行求解。仿真结果表明：所提最优运行策略可有效提高数据中心内工作负荷的灵活性，并促进数据中心运行经济性的提高。

关键词: 数据中心, 负荷时空转移需求响应, 聚合商

Abstract:

This paper uses aggregation technology to construct data center aggregators to participate in system demand response, and then proposes an optimal operation strategy of data center aggregators considering the demand response of load temporal and spatial shifts. First, according to the physical characteristics of the data center, the load time-space transfer demand response model is established. Then, the optimal operation model of data center aggregators was established with the goal of minimizing the operation cost of data center and maximizing the profit of data center aggregators. Finally, by using Karush-Kuhn-Tucker condition and large M method, the two-layer model is transformed into a single-layer mixed integer linear programming model. The simulation results show that the proposed optimal operation strategy can effectively improve the flexibility of the workload in the data center and promote the improvement of the operation economy of the data center.

Key words: data center, load spatio-temporal shift demand response, aggregator

甘润东, 龙玉江, 汤杰, 何熙, 罗鸿轩, 金鑫. 计及负荷时空转移需求响应的数据中心聚合商最优运行策略[J]. 中国电力, 2024, 57(3): 20-26.

Rundong GAN, Yujiang LONG, Jie TANG, Xi HE, Hongxuan LUO, Xin JIN. Optimal Operation Strategy of Data Center Aggregators for Demand Response Considering Temporal and Spatial Load Shift[J]. Electric Power, 2024, 57(3): 20-26.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401079

https://www.electricpower.com.cn/CN/Y2024/V57/I3/20

图/表 11

图 1 所提模型框架

Fig.1 The proposed model framework

表 1 DC的基本参数

Table 1 Basic parameters of DC

DC	$ {{{R}}_{{n}}} $	$ e_n^{\text{S}} $/(kW·h)	$ {\varepsilon _n} $	${{{T}}_{\text{d}}}$/h
1	4500	1.2	0.03	12
2	2500	1.2	0.03	8
3	2500	1.2	0.03	8

表 2 DC内发电、储能设备的基本参数

Table 2 Basic parameters of power generation and energy storage equipment in DC

DC	$ S_{ n}^{\text{T}} $, $ S_{ n}^{\text{D}} $/元	$ {c_{\rm g}} $/(元·(kW·h)^–1)	$ P_n^{{\text{G, min}}} $, $ P_n^{{\text{G, max}}} $/kW	$ {c_{{\text{bat}}}} $/(元·(kW·h)^–1)	$ E_n^{\min } $, $ E_n^{\max } $/(kW·h)	$ {\eta _{\text{c}}} $, $ {\eta _{\text{d}}} $	$ P_n^{{\text{bat, cmax}}} $, $ P_n^{{\text{bat, dmax}}} $/kW
1	10.5, 10.5	0.48	10, 200	0.3	10, 90	0.9, 0.95	35, 30
2	10, 10	0.45	10, 150	0.3	10, 90	0.9, 0.95	35, 30
3	10, 10	0.45	10, 150	0.3	10, 90	0.9, 0.95	35, 30

图 2 DC 1内发电、储能设备调度结果

Fig.2 Scheduling results of power generation and energy storage equipment within DC 1

图 3 DC 1内工作负荷调度结果

Fig.3 Results of workload scheduling within DC 1

图 4 DC 2内工作负荷调度结果

Fig.4 Results of workload scheduling within DC 2

图 5 DC 3内工作负荷调度结果

Fig.5 Results of workload scheduling within DC 3

图 6 DR计划实施后DCA的购电量

Fig.6 Electricity purchased by the DCA after the implementation of the DR scheme

图 7 DR计划实施前DCA的购电结果

Fig.7 Electricity purchased by the DCA before the implementation of the DR scheme

表 3 DR计划实施前后DC、DCA的盈利情况

Table 3 Profitability of DC and DCA before/after the implementation of DR plan 单位：元

市场主体	DR前净利润	DR后净利润	净利润增长值
DCA	–13.8	6 812.4	6 826.2
DC 1	–5 077.8	–889.8	4 188.0
DC 2	–3 208.8	–2 422.2	786.6
DC 3	–5 804.4	–3 160.5	2 643.9

表 4 不同案例下DCA的净利润对比

Table 4 Net profit comparison of DCA under different cases

案例		净利润/元
1		968.8
2		5 188.2
3		4 364.7
4		5 438.4

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