计及电转气和碳捕集的数据中心微电网“源-荷”低碳优化调度方法*

doi:10.11985/2022.03.010

电气工程学报 ›› 2022, Vol. 17 ›› Issue (3): 85-94.doi: 10.11985/2022.03.010

• 特邀专栏：储能(储氢)材料、技术、装置及新能源综合应用 • 上一篇下一篇

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计及电转气和碳捕集的数据中心微电网“源-荷”低碳优化调度方法^*

田明¹(), 张海峰¹(), 刘坤²()

1.上海工程技术大学机械与汽车工程学院上海 201620
2.西安交通大学智能网络与网络安全教育部重点实验室西安 710049

收稿日期:2022-06-11 修回日期:2022-08-15 出版日期:2022-09-25 发布日期:2022-10-28
通讯作者: 张海峰 E-mail:tianguangming0412@163.com;15901866629@163.com;kliu@sei.xjtu.edu.cn
作者简介:田明,男,1996年生,硕士研究生。主要研究方向为数据中心能量管理和智能电网等。E-mail： tianguangming0412@163.com
刘坤,男,1986年生,博士,助理研究员。主要研究方向为微电网能量管理、鲁棒优化等。E-mail： kliu@sei.xjtu.edu.cn
基金资助:
*国家自然科学基金资助项目(61903293)

“Source-load” Low-carbon Optimal Scheduling Method for Data Center Microgrids Considering Power to Gas and Carbon Capture

TIAN Ming¹(), ZHANG Haifeng¹(), LIU Kun²()

1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620
2. Key Laboratory of Intelligent Network and Network Security, Xi’an Jiaotong University, Xi’an 710049

Received:2022-06-11 Revised:2022-08-15 Online:2022-09-25 Published:2022-10-28
Contact: ZHANG Haifeng E-mail:tianguangming0412@163.com;15901866629@163.com;kliu@sei.xjtu.edu.cn

摘要/Abstract

摘要：

为促进数据中心多能互补以及低碳化,基于随机规划,提出了考虑电转气和碳捕集的数据中心微电网“源-荷”低碳优化调度模型。模型综合考虑了发电约束、数据负荷约束、电转气系统及碳捕集等约束,在发电侧引入碳捕集电厂-电转气-燃气机组(CCPP-P2G-CHP),将捕集的CO₂作为电转气系统合成甲烷的燃料;在负荷侧考虑即时型和可延迟型两种数据负荷,构建了数据中心功耗和数据负荷分配模型。仿真结果表明,所提模型在考虑碳排放的基础上,通过发电和数据中心数据负荷的优化调度,实现了数据中心微电网运行成本的最小化。

关键词: 电转气, 低碳优化调度, 碳捕集, 数据中心微电网, 随机规划

Abstract:

In order to promote multi energy complementary and low-carbon of data centers, a “source-load” low-carbon optimal scheduling model for data center microgrids is proposesd, which considers power to gas and carbon capture based on stochastic programming. The model considers the constraints of power generation, workloads, electricity to gas system and carbon capture. Carbon capture power plant, electric gas to gas, and combined heat power unit(CCPP-P2G-CHP) are introduced into the power generation side, the captured CO₂ is used as the fuel for the synthesis of methane in the electric gas to electric system. Considering the interactive and batch workloads on the load side, a data center power consumption and workload allocation model are constructed. The simulation results show that the proposed model can minimize the operation cost of the data center microgrid through the optimal scheduling of power generation and workload based on the consideration of carbon emission.

Key words: Power-to-gas, low-carbon optimal scheduling, carbon capture, data center microgrid, stochastic programming

中图分类号:

TM73

田明, 张海峰, 刘坤. 计及电转气和碳捕集的数据中心微电网“源-荷”低碳优化调度方法^*[J]. 电气工程学报, 2022, 17(3): 85-94.

TIAN Ming, ZHANG Haifeng, LIU Kun. “Source-load” Low-carbon Optimal Scheduling Method for Data Center Microgrids Considering Power to Gas and Carbon Capture[J]. Journal of Electrical Engineering, 2022, 17(3): 85-94.

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机组	最小出力/kW	最大出力/kW	最小开启/h	最小关闭/h	向下爬坡	向上爬坡
DG1	100	1 400	5	2	210	120
DG2	200	1 000	2	3	150	180
CHP1	200	600	6	2	150	420
CHP2	300	800	8	3	250	480

参数	数值
电池容量/(kW·h)	2 800
充电功率上限/kW	700
放电功率上限/kW	800
初始电量/(kW·h)	300
荷电上限	0.9
荷电下限	0.1
充电频率	0.91

设备	参数	数值
电解槽	电解槽效率电解槽功率上限/kW	0.65 1 000
燃料电池	燃料电池效率燃料电池放电上限/kW	0.75 200
甲烷反应器	甲烷反应器效率甲烷反应器功率上限	0.85 750
储氢罐	储氢罐容量上限/kmol 储氢罐容量下限/kmol	0 1 000

数据负荷	执行时长/h	执行周期/h	是否连续	GPU 频率/GHz
1	16	24	否	2 000
2	15	24	否	2 000
3	19	24	是	1 800
4	12	24	否	1 800
5	10	24	否	2 000
6	12	24	否	2 000

集群	空闲功耗/kW	满负载功耗/kW	最大负载量/个	开机功耗/kW	关机功耗/kW
1	80	145	80 000	100	90
2	68	126	80 000	90	80
3	106	200	120 000	160	150
4	120	180	120 000	160	150
5	88	150	100 000	120	110

计及电转气和碳捕集的数据中心微电网“源-荷”低碳优化调度方法^*

“Source-load” Low-carbon Optimal Scheduling Method for Data Center Microgrids Considering Power to Gas and Carbon Capture

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图/表 16

参考文献 28

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集群	状态
1	0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1
2	0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
4	0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
5	1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

数据负荷	状态
数据负荷1	1 1 0 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 0
数据负荷2	0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0
数据负荷3	0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
数据负荷4	1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 1 0
数据负荷5	0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 0
数据负荷6	0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0

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