促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略

doi:10.12204/j.issn.1000-7229.2022.04.001

电力建设 ›› 2022, Vol. 43 ›› Issue (4): 1-9.doi: 10.12204/j.issn.1000-7229.2022.04.001

• 支撑碳达峰、碳中和的能源电力技术、经济和政策·栏目主持赵俊华副教授、邱靖博士、文福拴教授· • 上一篇下一篇

促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略

兰洲¹(), 蒋晨威²^,³(), 谷纪亭¹(), 文福拴²^,³(), 杨侃¹(), 王坤¹()

1.国网浙江省电力有限公司经济技术研究院,杭州市 310016
2.浙江大学电气工程学院,杭州市 310027
3.杭州沃瑞电力科技有限公司,杭州市 310012

收稿日期:2021-08-09 出版日期:2022-04-01 发布日期:2022-03-24
通讯作者: 文福拴 E-mail:lanzhou_zju@163.com;chenweijiang@zju.edu.cn;gu_jiting@zj.sgcc.com.cn;fushuan.wen@gmail.com;yang_kan@zj.sgcc.com.cn;wang_kun@zj.sgcc.com.cn
作者简介:兰洲 (1980),男,博士,高级工程师,主要从事电网发展战略与规划等方面的研究工作,E-mail: lanzhou_zju@163.com;
蒋晨威 (1997),男,硕士研究生,主要从事电力需求响应、综合能源系统规划等方面的研究工作,E-mail: chenweijiang@zju.edu.cn;
谷纪亭(1989),男,硕士,工程师,主要从事电网规划与电力市场等方面的研究工作,E-mail: gu_jiting@zj.sgcc.com.cn;
杨侃 (1983),男,硕士,工程师,主要从事电力市场与电价方面的研究工作,E-mail: yang_kan@zj.sgcc.com.cn;
王坤 (1988),男,硕士,工程师,主要从事配电网规划方面的研究工作,E-mail: wang_kun@zj.sgcc.com.cn。
基金资助:
国家自然科学基金资助项目(U1910216);国网浙江省电力有限公司科技项目(5211JY19000T)

Optimal Dispatch and Demand Response Strategies of Data Centers for Promoting Accommodation of Renewable Energy Generation and Reducing Carbon Emission

LAN Zhou¹(), JIANG Chenwei²^,³(), GU Jiting¹(), WEN Fushuan²^,³(), YANG Kan¹(), WANG Kun¹()

1. Economic and Technical Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310016, China
2. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
3. Hangzhou Worui Electric Power Technology Co., Ltd., Hangzhou 310012, China

Received:2021-08-09 Online:2022-04-01 Published:2022-03-24
Contact: WEN Fushuan E-mail:lanzhou_zju@163.com;chenweijiang@zju.edu.cn;gu_jiting@zj.sgcc.com.cn;fushuan.wen@gmail.com;yang_kan@zj.sgcc.com.cn;wang_kun@zj.sgcc.com.cn
Supported by:
National Natural Science Foundation of China(U1910216);Science and Technology Project from State Grid Zhejiang Electric Power Company(5211JY19000T)

摘要/Abstract

摘要：

数据中心是用户侧重要的灵活性资源,通过对其合理调度与引导需求响应可有效提升数据中心对分布式可再生能源发电的就地消纳能力,进而为实现中国的“双碳”目标提供支撑。在此背景下,文章研究了促进可再生能源发电消纳的数据中心优化调度与需求响应策略。首先,考虑数据中心用能特性,建立包含数据中心能耗与用户服务质量要求的数据中心常规运行模型。接着,基于延迟容忍型工作负荷的可调度特性,提出数据中心面向用户侧的可再生能源补贴机制,以促进用户数据服务需求向可再生能源发电出力高峰时期转移。然后,引入绿色证书机制,以数据中心运行成本最小、环保效益最大为目标,建立数据中心日前最优的运行调度模型。最后,以某典型数据中心为例,对文章所提方法的可行性进行验证;仿真结果表明,所提出的数据中心优化调度与需求响应策略可以有效调动数据中心及其用户的需求响应潜力,提升数据中心对可再生能源发电的消纳能力,从而实现碳减排。

关键词: 数据中心, 优化调度, 需求响应, 可再生能源发电消纳

Abstract:

A data center represents an important flexible resource on the user side. Through reasonable scheduling and motivating demand response, the capability of the data center to accommodate the power generation from distributed renewable energy sources (RESs) can be effectively improved, which is conducive for achieving the specified “dual carbon” target in China. In this paper, the optimal dispatch and demand response strategies for a data center are addressed to promote the accommodation of the power generation from RESs. Firstly, considering the load characteristics of the data center, a conventional data center operation model including the power demand profile and quality of service (QoS) is established. Then, according to the schedulable characteristics of delay-tolerant workloads, a renewable energy subsidy mechanism is proposed for the data center to motivate the shift of the user data service demand to the peak power generation period of RESs. Then, on the basis of the green certificate (GC) mechanism, with the objective of minimizing the operating cost of the data center and maximizing environmental benefits, an optimal dispatch model of the data center is presented. Finally, a typical data center is employed to demonstrate the feasibility and efficiency of the presented method. Simulation results show that the presented method can effectively explore the demand response potential of the data center and its users, enhance the accommodation capability for power generation from RESs, and hence reduce carbon emission.

Key words: data center, optimal dispatch, demand response, accommodation of renewable energy generation

中图分类号:

TM73

兰洲, 蒋晨威, 谷纪亭, 文福拴, 杨侃, 王坤. 促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略[J]. 电力建设, 2022, 43(4): 1-9.

LAN Zhou, JIANG Chenwei, GU Jiting, WEN Fushuan, YANG Kan, WANG Kun. Optimal Dispatch and Demand Response Strategies of Data Centers for Promoting Accommodation of Renewable Energy Generation and Reducing Carbon Emission[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(4): 1-9.

图/表 10

图1 数据中心常规运行框架

Fig.1 Framework of routine operation in a data center

图A1 光伏电站最大有功出力

Fig.A1 Maximum active power output of the photovoltaic station

图A2 分时电价

Fig.A2 Time-of-use electricity price

图A3 延迟敏感型工作负荷

Fig.A3 Delay-sensitive workloads

图A4 延迟容忍型工作负荷

Fig.A4 Delay-tolerant workloads

表A1 仿真案例参数

Table A1 A1 Parameters of simulation cases

表1 三个场景中的数据中心优化结果对比

Table 1 Comparison of optimization results of the data center among three scenarios

图2 三个场景中各类工作负荷能耗情况

Fig.2 Energy consumption of various workloads in three scenarios

图3 三个场景的负荷曲线对比

Fig.3 Comparison of load curves among three scenarios

表2 不同补贴时段长度的优化结果对比

Table 2 Comparison of optimization results among different subsidy period lengths

参考文献 21

[1]	韩肖清, 李廷钧, 张东霞, 等. 双碳目标下的新型电力系统规划新问题及关键技术[J]. 高电压技术, 2021, 47(9):3036-3046.
	HAN Xiaoqing, LI Tingjun, ZHANG Dongxia, et al. New issues and key technologies of new power system planning under double carbon goals[J]. High Voltage Engineering, 2021, 47(9):3036-3046.
[2]	曾博, 徐富强, 刘裕, 等. 考虑可再生能源与需求响应协同增效的能量枢纽多目标区间优化规划方法[J]. 中国电机工程学报, 2021, 41(21):7212-7225.
	ZENG Bo, XU Fuqiang, LIU Yu, et al. Multi-objective interval optimization approach for energy hub planning with consideration of renewable energy and demand response synergies[J]. Proceedings of the CSEE, 2021, 41(21):7212-7225.
[3]	唐程辉, 张凡, 张宁, 等. 考虑可再生能源随机性和需求响应的电力系统日前经济调度[J]. 电力系统自动化, 2019, 43(15):18-25, 63.
	TANG Chenghui, ZHANG Fan, ZHANG Ning, et al. Day-ahead economic dispatch of power system considering renewable power uncertainty and demand response[J]. Automation of Electric Power Systems, 2019, 43(15):18-25, 63.
[4]	杨谦, 刘继春, 蒋万枭. 光伏不同渗透率下考虑源网荷储深度互动的电力系统调峰策略[J]. 电力建设, 2021, 42(9):74-84.
	YANG Qian, LIU Jichun, JIANG Wanxiao. Peak regulation strategy of power system considering the interaction of source-network-load-storage under different penetration rate of PV[J]. Electric Power Construction, 2021, 42(9):74-84.
[5]	WANG H, HUANG J W, LIN X J, et al. Proactive demand response for data centers: A win-win solution[J]. IEEE Transactions on Smart Grid, 2016, 7(3):1584-1596.
[6]	桑丙玉, 王德顺, 杨波, 等. 基于经济性的互联网数据中心光-储协同优化配置[J]. 电力系统保护与控制, 2020, 48(17):131-138.
	SANG Bingyu, WANG Deshun, YANG Bo, et al. Collaborative optimization configuration of photovoltaic-energy storage based on economy in an Internet data center[J]. Power System Protection and Control, 2020, 48(17):131-138.
[7]	CIE. Green data center white paper[R]. Beijing: CIE, 2019.
[8]	China Industry, and Information Technology. Research report on the development of China’s new infrastructure[R]. Beijing: CCID Academy for Industry and Information Technology, 2020.
[9]	吴刚, 高赐威, 陈宋宋, 等. 考虑需求响应的数据中心用电负荷优化研究综述[J]. 电网技术, 2018, 42(11):3782-3788.
	WU Gang, GAO Ciwei, CHEN Songsong, et al. A survey on data center power load optimization considering demand response[J]. Power System Technology, 2018, 42(11):3782-3788.
[10]	BELOGLAZOV A, BUYYA R. Energy efficient allocation of virtual machines in cloud data centers[C]// 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. May 17-20, 2010, Melbourne, VIC, Australia. IEEE, 2010: 577-578.
[11]	XIAO Z, SONG W J, CHEN Q. Dynamic resource allocation using virtual machines for cloud computing environment[J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(6):1107-1117. doi: 10.1109/TPDS.2012.283 URL
[12]	LI J, BAO Z, LI Z Y. Modeling demand response capability by Internet data centers processing batch computing jobs[J]. IEEE Transactions on Smart Grid, 2015, 6(2):737-747. doi: 10.1109/TSG.2014.2363583 URL
[13]	TRAN N H, TRAN D H, REN S L, et al. How geo-distributed data centers do demand response: A game-theoretic approach[J]. IEEE Transactions on Smart Grid, 2016, 7(2):937-947.
[14]	杨挺, 李洋, 盆海波, 等. 基于需求侧响应的数据中心联络线功率控制方法[J]. 中国电机工程学报, 2017, 37(19):5529-5540.
	YANG Ting, LI Yang, PEN Haibo, et al. Tie-line power fluctuation smoothing algorithm based on data center demand response[J]. Proceedings of the CSEE, 2017, 37(19):5529-5540.
[15]	郭嘉, 杨洪明, 张鹏兴, 等. 面向需求响应的数据中心供能系统优化调度[J]. 电力科学与技术学报, 2018, 33(2):43-49.
	GUO Jia, YANG Hongming, ZHANG Pengxing, et al. Optimal dispatching of data center energy-supplying system with demand response[J]. Journal of Electric Power Science and Technology, 2018, 33(2):43-49.
[16]	PAUL D, ZHONG W D, BOSE S K. Demand response in data centers through energy-efficient scheduling and simple incentivization[J]. IEEE Systems Journal, 2017, 11(2):613-624. doi: 10.1109/JSYST.2015.2476357 URL
[17]	LIU N, DONG Z Q, ROJAS-CESSA R. Task scheduling and server provisioning for energy-efficient cloud-computing data centers[C]// 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops. July 8-11, 2013, Philadelphia, PA, USA. IEEE, 2013: 226-231.
[18]	王彩霞, 时智勇, 梁志峰, 等. 新能源为主体电力系统的需求侧资源利用关键技术及展望[J]. 电力系统自动化, 2021, 45(16):37-48.
	WANG Caixia, SHI Zhiyong, LIANG Zhifeng, et al. Key technologies and prospects of demand-side resource utilization for power systems dominated by renewable energy[J]. Automation of Electric Power Systems, 2021, 45(16):37-48.
[19]	WEI W, LIU F, MEI S W. Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty[J]. IEEE Transactions on Smart Grid, 2015, 6(3):1364-1374. doi: 10.1109/TSG.2014.2376522 URL
[20]	冯成, 王毅, 陈启鑫, 等. 能源互联网下的数据中心能量管理综述[J]. 电力自动化设备, 2020, 40(7):1-9.
	FENG Cheng, WANG Yi, CHEN Qixin, et al. Review of energy management for data centers in energy Internet[J]. Electric Power Automation Equipment, 2020, 40(7):1-9.
[21]	LIU Z H, LIN M H, WIERMAN A, et al. Greening geographical load balancing[J]. IEEE/ACM Transactions on Networking, 2015, 23(2):657-671. doi: 10.1109/TNET.2014.2308295 URL

促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略

Optimal Dispatch and Demand Response Strategies of Data Centers for Promoting Accommodation of Renewable Energy Generation and Reducing Carbon Emission

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刁利, 李光, 宋雪莹, 德格吉日夫, 谭忠富. 新能源分比例渗透下基于博弈的多主体效益综合评估方法[J]. 电力建设, 2022, 43(6): 43-55.
[2]	王萍萍, 许建中, 闫庆友, 林宏宇. 计及灵活性负荷资源需求响应和不确定性的楼宇微网调度双层优化模型[J]. 电力建设, 2022, 43(6): 128-140.
[3]	刘雪飞, 庞凝, 王云佳, 姚伯岩, 魏子强, 温鹏, 高立艾. 农村综合能源系统多层协同优化运行方法[J]. 电力建设, 2022, 43(5): 63-71.
[4]	李彬, 魏吟娬, 祁兵, 孙毅, 陈宋宋. 基于EEMD-LSTM的需求响应终端DDoS攻击检测方法[J]. 电力建设, 2022, 43(4): 81-90.
[5]	赵玲霞, 王兴贵, 丁颖杰, 郭永吉, 李锦健. 考虑分时电价及光热电站参与的多能源虚拟电厂优化调度[J]. 电力建设, 2022, 43(4): 119-129.
[6]	丁曦, 姜威, 郭创新, 奚增辉, 高洁. 考虑需求响应的电/热/气云储能优化配置策略[J]. 电力建设, 2022, 43(3): 83-97.
[7]	李磊, 王朝亮, 郝一浩, 李彬, 祁兵, 陈宋宋. 面向需求响应业务的灵活弹性传输策略[J]. 电力建设, 2022, 43(2): 10-17.
[8]	魏震波, 马新如, 郭毅, 魏平桉, 卢炳文, 张海涛. 碳交易机制下考虑需求响应的综合能源系统优化运行[J]. 电力建设, 2022, 43(1): 1-9.
[9]	廖宗毅, 万文略, 陈曦, 陈岚. 基于服务商引导的综合能源系统源-荷协同优化方法[J]. 电力建设, 2022, 43(1): 38-48.
[10]	高建伟, 梁鹏程, 高芳杰, 门慧娟, 郎宇彤. 基于多方势博弈的综合能源系统运行优化[J]. 电力建设, 2021, 42(9): 32-40.
[11]	杨欢红, 史博文, 黄文焘, 丁宇涛, 王洁, 余威, 朱子叶. 基于综合需求响应的微能源网日前优化调度方法[J]. 电力建设, 2021, 42(7): 11-19.
[12]	郭祚刚, 徐敏, 李睿智, 袁智勇, 谈赢杰, 陈柏沅, 雷金勇, 刘念. 基于VaR理论的综合能源零售市场最优策略[J]. 电力建设, 2021, 42(6): 9-16.
[13]	薛溟枫, 毛晓波, 潘湧涛, 杨艳红, 赵振兴, 李彦君. 基于LSTM神经网络的电动汽车充电站需求响应特性封装及配电网优化运行[J]. 电力建设, 2021, 42(6): 76-85.
[14]	林铭蓉, 胡志坚, 高明鑫, 陈锦鹏. 考虑需求响应和电动汽车负荷路-电耦合特性的配电网可靠性评估[J]. 电力建设, 2021, 42(6): 86-95.
[15]	牛耕, 季宇, 陈培坤, 寇凌峰, 孙树敏, 滕玮, 张利伟, 陈继开. 含海洋能发电的海岛微网能量优化调度方法[J]. 电力建设, 2021, 42(6): 96-104.