考虑竞争偏好的数据中心-电动汽车用能联合优化策略

doi:10.12204/j.issn.1000-7229.2023.03.009

电力建设 ›› 2023, Vol. 44 ›› Issue (3): 85-92.doi: 10.12204/j.issn.1000-7229.2023.03.009

• 双碳驱动下配电网与新型负荷互动关键技术·栏目主持穆云飞教授、宋毅教授级高工· • 上一篇下一篇

考虑竞争偏好的数据中心-电动汽车用能联合优化策略

张祥成¹(), 刘飞¹(), 田旭¹(), 王世斌¹(), 杜亚彬²(), 李彬²()

1.国网青海省电力公司经济技术研究院,西宁市 810001
2.华北电力大学电气与电子工程学院,北京市 102206

收稿日期:2022-04-18 出版日期:2023-03-01 发布日期:2023-03-02
通讯作者: 杜亚彬(1998),男,硕士研究生, 主要从事数据中心能耗优化、需求侧管理等技术研究工作,E-mail:dyb19980214@126.com
作者简介:张祥成(1983),男,学士,高级工程师,从事电网规划设计及新能源并网发电技术研究工作,E-mail:zxcheng4031@qh.sgcc.com
刘飞(1988),男,硕士,高级工程师,从事电网规划设计及新能源并网发电技术研究工作,E-mail:liufei602@163.com
田旭(1990),男,学士,工程师,从事电力系统规划设计、新能源并网发电研究工作,E-mail:tianxu0111@163.com
王世斌(1995),男,学士,工程师,从事电网规划设计及新能源并网发电技术研究工作,E-mail:1047265141@qq.com
李彬(1983),男,博士,副教授,主要从事电力通信、电气信息技术相关研究工作,E-mail:direfish@163.com
基金资助:
国家自然科学基金项目“信息-物理-社会多域耦合下的数据中心与配电网交互集成方法研究”(52177082);国家电网有限公司科技项目“电力系统需求侧用户可调节潜力测算与发展规划研究”(SGQHJY00GHJS2100232)

Joint Optimization Strategy for Energy Consumption of Data Center and Electric Vehicles Considering Competitive Preference

ZHANG Xiangcheng¹(), LIU Fei¹(), TIAN Xu¹(), WANG Shibin¹(), DU Yabin²(), LI Bin²()

1. Economic and Technological Research Institute of State Grid Qinghai Electric Power Company, Xining 810001, China
2. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Received:2022-04-18 Online:2023-03-01 Published:2023-03-02
Supported by:
National Natural Science Foundation of China(52177082);State Grid Corporation of China Research Program(SGQHJY00GHJS2100232)

摘要/Abstract

摘要：

利用现场可再生能源有利于数据中心降低用能成本、减少碳排放,但可再生能源出力的间歇性会导致部分现场可再生能源无法得到有效的利用。因此,利用数据中心负载的可调节特性,设计了数据中心运营商与电动汽车运营商可再生能源联合消纳机制,从而达到完成剩余可再生能源的利用。充分考虑多元主体竞争偏好行为,基于Stackelberg博弈理论,建立了由能源服务商代售的单一模型和直接售电与能源服务商代售结合的多重模型。根据电动汽车充电站日充电负荷的需求和2种模式优势对比,最终提出一种考虑竞争偏好的数据中心-电动汽车用能联合优化策略,并进行仿真与分析,结果表明所采用的优化策略具有较好的经济收益。

关键词: 数据中心, 可再生能源消纳, 竞争偏好, Stackelberg博弈, 电动汽车

Abstract:

The use of on-site renewable energy is beneficial to the data center to reduce energy costs and carbon emissions, but the intermittent output of renewable energy will lead to the ineffective use of some on-site renewable energy. According to the adjustable characteristics of data center load, this paper designs a joint renewable energy consumption mechanism between data center operators and electric vehicle operators, so as to achieve the utilization of residual renewable energy. According to Stackelberg game theory, this paper fully considers the competitive preference behavior of multiple subjects, and establishes a single model of energy service providers' consignment and a multiple model that considers the combination of direct electricity sales and energy service providers' consignment. According to the demand of daily charging load of electric vehicle charging station and the comparison of advantages of the two modes, a joint optimization strategy for energy consumption of data center and electric vehicles considering competitive preference is finally proposed. Simulation and analysis results show that the optimization strategy adopted in this paper has good economic benefits.

Key words: data center, renewable energy consumption, competitive preference, Stackelberg game, electric vehicle

中图分类号:

TM73

张祥成, 刘飞, 田旭, 王世斌, 杜亚彬, 李彬. 考虑竞争偏好的数据中心-电动汽车用能联合优化策略[J]. 电力建设, 2023, 44(3): 85-92.

ZHANG Xiangcheng, LIU Fei, TIAN Xu, WANG Shibin, DU Yabin, LI Bin. Joint Optimization Strategy for Energy Consumption of Data Center and Electric Vehicles Considering Competitive Preference[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(3): 85-92.

图/表 11

图1 系统架构

Fig.1 System architecture

图2 单一模式

Fig.2 Single mode

图3 多重模式

Fig.3 Multiple mode

图4 考虑供需关系的能源联合消纳策略流程

Fig.4 Joint energy consumption strategy process considering the relationship between supply and demand

表1 系统主要参数设定

Table 1 System key parameter setting

图5 充电站日充电负荷

Fig.5 Daily charging load of charging station

图6 数据中心能源利用情况

Fig.6 Data center energy utilization

表2 不同偏好下多重模式的交易时刻

Table 2 Multi-mode trading time under different preference

图7 各交易案例中竞争偏好与收益的关系

Fig.7 The relationship between competitive preference and revenue in different trading scenarios

图8 不同价格弹性系数下的各种案例收益分析

Fig.8 Revenue analysis of each scenarios under different price elasticity coefficients

图9 各案例24 h收益

Fig.9 24-hour revenue of each scenario

参考文献 26

[1]	陈培恩, 王岚. 以节能为核心的绿色数据中心建设探究[J]. 节能, 2019, 38(12): 172-174.
	CHEN Peien, WANG Lan. Research on the construction of green data center with energy saving as the core[J]. Energy Conservation, 2019, 38(12): 172-174.
[2]	张家口市察北管理区管理委员会. 张家口察北新能源大数据中心建设项目[BE/OL]. [2021-04-06]. http://www.zjkcb.gov.cn/xxgk/content.jsp?code=zjkcbqgwh/2020-07303.
[3]	国家发展改革委国家能源局关于印发新能源微电网示范项目名单的通知[BE/OL]. [2021-04-06]. http://www.bzfxw.com/nengyuan/1000180000000619.html.
[4]	杜新江, 李会林. 中国能建葛洲坝电力公司总承包建设张北新能源项目主体工程封顶[BE/OL]. (2020-06-15) [2021-04-06]. http://www.ceec.net.cn/art/2020/6/15/art_11019_2143259.html.
[5]	HADDAD M, DA COSTA G, NICOD J M, et al. Combined IT and power supply infrastructure sizing for standalone green data centers[J]. Sustainable Computing: Informatics and Systems, 2021, 30: 100505. doi: 10.1016/j.suscom.2020.100505 URL
[6]	王玚, 李鹏, 冀浩然, 等. 考虑多类型资源的数据中心园区供电协调规划[J]. 电力系统自动化, 2022, 46(14): 19-28.
	WANG Yang, LI Peng, JI Haoran, et al. Coordination planning of power supply in data center parks with multiple resources[J]. Automation of Electric Power Systems, 2022, 46(14): 19-28.
[7]	GUO C S, LUO F J, CAI Z X, et al. Integrated planning of internet data centers and battery energy storage systems in smart grids[J]. Applied Energy, 2021, 281: 116093. doi: 10.1016/j.apenergy.2020.116093 URL
[8]	QI W B, LI J, LIU Y Q, et al. Planning of distributedinternet data center microgrids[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 762-771. doi: 10.1109/TSG.2017.2751756 URL
[9]	LU Y W, WANG R, WANG P, et al. Energy-efficient task scheduling for data centers with unstable renewable energy: a robust optimization approach[C]// 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2019: 455-462.
[10]	WANG P, XIE L Y, LU Y, et al. Day-ahead emission-aware resource planning for data center considering energy storage and batch workloads[C]// 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018: 1-6.
[11]	DING Z H, XIE L Y, LU Y, et al. Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management[J]. IEEE Transactions on Industry Applications, 2018, 54(6): 5599-5608. doi: 10.1109/TIA.2018.2851516 URL
[12]	PENG X P, KAUTEN C, ZHANG C W, et al. REDUX: managing renewable energy in data centers using distributed UPS systems[C]// 2018 IEEE International Conference on Smart Cloud (SmartCloud). IEEE, 2018: 46-53.
[13]	ASLAM S, ASLAM S, HERODOTOU H, et al. Towards energy efficiency and power trading exploiting renewable energy in cloud data centers[C]// 2019 International Conference on Advances in the Emerging Computing Technologies (AECT). IEEE, 2020: 1-6.
[14]	SUN Z H, KONG F X, LIU X, et al. Intelligent joint spatio-temporal management of electric vehicle charging and data center power consumption[C]// International Green Computing Conference. IEEE, 2015: 1-8.
[15]	LI S, BROCANELLI M, ZHANG W, et al. Integrated power management of data centers and electric vehicles for energy and regulation market participation[J]. IEEE Transactions on Smart Grid, 2014, 5(5): 2283-2294. doi: 10.1109/TSG.2014.2321519 URL
[16]	ZHANG S, SUN X S, LIU H C. Combining data centers with electric vehicle battery swapping stations for grid regulation[C]// 2016 2nd International Conference on Intelligent Green Building and Smart Grid (IGBSG). IEEE, 2016: 1-6.
[17]	BROCANELLI M, LI S, WANG X R, et al. Maximizing the revenues of data centers in regulation market by coordinating with electric vehicles[J]. Sustainable Computing: Informatics and Systems, 2015, 6: 26-38. doi: 10.1016/j.suscom.2014.03.004 URL
[18]	YU L, JIANG T, ZOU Y L. Distributed online energy management for data centers and electric vehicles in smart grid[J]. IEEE Internet of Things Journal, 2016, 3(6): 1373-1384. doi: 10.1109/JIOT.2016.2602846 URL
[19]	蔡黎, 葛棚丹, 代妮娜, 等. 电动汽车入网负荷预测及其与电网互动研究进展综述[J]. 智慧电力, 2022, 50(7): 96-103.
	CAI Li, GE Pengdan, DAI Nina, et al. Review of research progress on load prediction and grid interaction of electric vehicles[J]. Smart Power, 2022, 50(7): 96-103.
[20]	蔡黎, 张权文, 代妮娜, 等. 规模化电动汽车接入主动配电网研究进展综述[J]. 智慧电力, 2021, 49(6): 75-82.
	CAI Li, ZHANG Quanwen, DAI Nina, et al. Review on research progress of large-scale electric vehicle access to active distribution network[J]. Smart Power, 2021, 49(6): 75-82.
[21]	李彬, 杜亚彬, 曹望璋, 等. 考虑风光储互补与工作负载分配的数据中心优化调度[J]. 现代电力, 2022, 39(3): 356-363.
	LI Bin, DU Yabin, CAO Wangzhang, et al. Optimal scheduling of data center considering wind-solar-storage complementary and workload distribution[J]. Modern Electric Power, 2022, 39(3): 356-363.
[22]	NOJAVAN S, ALLAH AALAMI H. Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program[J]. Energy Conversion and Management, 2015, 103: 1008-1018. doi: 10.1016/j.enconman.2015.07.018 URL
[23]	包宇庆, 王蓓蓓, 李扬, 等. 考虑大规模风电接入并计及多时间尺度需求响应资源协调优化的滚动调度模型[J]. 中国电机工程学报, 2016, 36(17): 4589-4600.
	BAO Yuqing, WANG Beibei, LI Yang, et al. Rolling dispatch model considering wind penetration and multi-scale demand response resources[J]. Proceedings of the CSEE, 2016, 36(17): 4589-4600.
[24]	丁川, 王开弘, 冉戎. 基于公平偏好的营销渠道合作机制研究[J]. 管理科学学报, 2013, 16(8): 80-94.
	DING Chuan, WANG Kaihong, RAN Rong. Marketing channel coordination mechanism based on fairness preferences[J]. Journal of Management Sciences in China, 2013, 16(8): 80-94.
[25]	刘青, 戚中译. 基于蒙特卡洛法的电动汽车负荷预测建模[J]. 电力科学与工程, 2014, 30(10): 14-19.
	LIU Qing, QI Zhongyi. Electric vehicles load forecasting model based on Monte Carlo simulation[J]. Electric Power Science and Engineering, 2014, 30(10): 14-19.
[26]	CHARNESS G, RABIN M. Understanding social preferences with simple tests[J]. The Quarterly Journal of Economics, 2002, 117(3): 817-869. doi: 10.1162/003355302760193904 URL

考虑竞争偏好的数据中心-电动汽车用能联合优化策略

Joint Optimization Strategy for Energy Consumption of Data Center and Electric Vehicles Considering Competitive Preference

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 26

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张宇华, 孙晓鹏, 郜登科, 李鸿彪. 数据中心多供电单元并联系统低频导纳建模及稳定性分析[J]. 电力建设, 2023, 44(3): 66-76.
[2]	姚寅, 朱烨冬, 李东东, 周波, 林顺富. 基于决策实验室算法-对抗解释结构模型的电动汽车多场景需求响应策略分析[J]. 电力建设, 2023, 44(3): 93-104.
[3]	付卓铭, 胡俊杰, 马文帅, 姚丽. 规模化电动汽车参与电力系统二次调频研究综述[J]. 电力建设, 2023, 44(2): 1-14.
[4]	解瑞硕, 于泽旭, 窦震海, 乔萌萌, 赵晔, 王媛媛. 基于议价能力的风-光-CHP多主体优化运行策略[J]. 电力建设, 2023, 44(1): 118-128.
[5]	丁峰, 李晓刚, 梁泽琪, 吴敏, 朱雨蕙, 冯冬涵, 周云. 国外可再生能源发展经验及其对我国相关扶持政策的启示[J]. 电力建设, 2022, 43(9): 1-11.
[6]	王婷, 陈晨, 谢海鹏. 配电网对分布式电源和电动汽车的承载力评估及提升方法综述[J]. 电力建设, 2022, 43(9): 12-24.
[7]	任鑫芳, 张志朝, 许李天伦, 王诗超, 刘展志, 许方圆. 计及电动汽车与温控负荷需求响应的分层能源系统优化调度[J]. 电力建设, 2022, 43(9): 77-86.
[8]	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.
[9]	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.
[10]	范培潇, 杨军, 肖金星, 徐冰雁, 叶影, 李勇汇, 李蕊. 基于深度Q学习的含电动汽车孤岛微电网负荷频率控制策略[J]. 电力建设, 2022, 43(4): 91-99.
[11]	马晓燕, 穆云飞, 李树荣, 姜欣阳, 李华, 陈长金. 数据中心综合能源系统优化运行研究综述[J]. 电力建设, 2022, 43(11): 1-13.
[12]	严欢, 胡俊杰, 黄旦莉, 张展宇, 岳园园. 考虑电动汽车虚拟电厂灵活性和高比例光伏接入的配电网规划[J]. 电力建设, 2022, 43(11): 14-23.
[13]	王苗苗, 李华强, 何永祥. 考虑多主体电能交易的云储能服务机制[J]. 电力建设, 2022, 43(11): 73-84.
[14]	王若谷, 陈果, 王秀丽, 钱涛, 高欣. 计及风电与电动汽车随机性的两阶段机组组合研究[J]. 电力建设, 2021, 42(8): 63-70.
[15]	刘洋, 李立生, 刘志伟, 苗世洪, 张世栋, 张林利. 考虑广义储能集群参与的配电网协同控制策略[J]. 电力建设, 2021, 42(8): 89-98.