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

doi:10.11930/j.issn.1004-9649.202401079

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401079

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

Figures/Tables 11

References 25

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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

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

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

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

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