基于计算热点转移的5G车联网能量实时协同管理策略

doi:10.19595/j.cnki.1000-6753.tces.231896

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1021 KB)
输出: BibTeX | EndNote (RIS)

摘要

5G基站与电动汽车规模持续增长,开展能量管理有利于减碳降本,缓解电网压力。但车联网的信息与能量高度耦合,使得基站独立管理能量易恶化车主通信质量,且基站空调热负荷内部转移能力有限,电动汽车的车载算力调度潜力亟待发掘。对此,首先提出联合电动汽车充放电、5G网络与空调负荷和储能调度的能量管理模型,构建车联网时间平均用能成本最小化目标函数;然后,考虑网络状态不确定性,利用改进Lyapunov优化将随机优化问题转化为确定性问题。其次,利用车载计算零热点特性,提出基于主从博弈的计算热点转移策略。此外,考虑电网通信安全需求,提出融合启发式与连续凸近似的求解方法。最后,仿真表明,所提方法相比现有实时能量管理方法,有效削减车联网整体用能成本。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈恺
	付宇
	孙毅
	李跃
	杨泓玥

关键词 ： 5G边缘计算, 电动汽车, 能量管理, Lyapunov优化, 热点转移, 主从博弈

Abstract：

With the development of smart vehicle network promoted by China, the number of electric vehicle (EV) and 5G base station (BS) is increasing rapidly, while also bringing huge operation pressure and energy consumption to power grid and network operator, respectively. Therefore, how to coordinate the flexible resource of EV and BS to reduce electric load peak for power grid are important issues. At the same time, as EV equipping with powerful chip, BS can transfer some tasks to idle parked EV for cut down energy consumption of BS. However, due to the communication of EV is coupling with 5G BS, additional communication delay caused by BS energy management will affect the ordered charging scheduling signal between power grid and EV, increasing charging cost of EV. In addition, if 5G BS provided low delay communication for EV, it will bring more energy consumption. Moreover, the optimization of air condition load of BS is still lack of attention, which energy consumption is related with computing hotspot. To this end, this paper establishes a Stackelberg game based online energy management strategy faced to 5G vehicle network, aiming at minimizing the time average battery charging cost of EV and electricity purchasing cost of BS.
First of all, considering vehicle computing characteristics, and the coupling between hot load of air condition and computing tasks, a task migration model between 5G BS and EV is established to transfer the hot load and electric load of BS. Secondly, according to the analysis of communication delay to EV ordered charging, this paper proposes a calculation method of EV real charging capacity considering 5G wireless communication delay, and a communication delay penalty function for 5G network operator. Thirdly, based on above model, considering the stochastic events including task arrival and renewable energy, this paper investigates a long-term average electricity purchasing cost of BS and EV charging cost minimizing problem. Finally, an improved Lyapunov optimization method is proposed to transform the problem to a real time optimization problem. The real time problem can be divided into one upper layer subproblem and two lower layer subproblems, including computing electricity and battery (dis)charging problem, EV charging and communication resource allocation problem, and Stackelberg game-based computing hotspot transferring and BS air condition load control problem, respectively. By introducing an incentive function for EV accepting tasks of BS, the existence of the Nash equilibrium is proved, and this paper gives a close form solution on computing hotspot transferring. Meanwhile, other subproblems can be solved by successive convex approximation method.
In this paper, four scenarios are designed for verifying the performance of our proposed strategy, and analyze the impact of network scale, random task traffic, control parameter, and EV computing capacity on EV charging and BS electricity cost optimization. The simulation results show that proposed method can reduce electricity purchasing cost for both EV and BS without prior knowledge, and the hot load of BS is cut down without adding extra charging cost of parked EV. The following conclusions can be drawn from the simulation analysis: 1) The cooperation between EV and 5G BS can optimize the distribution of energy and information flow, reduce the energy consumption caused by cooling computing hotspot of BS. 2) By introducing improved Lyapunov optimization method, the network resource and energy scheduling of 5G BS and EV can be made online, and the resource scheduling result will not break the backup energy constraint and communication quality.

Key words： 5G edge computing electric vehicle energy management Lyapunov optimization hotspot transfer Stackelberg game

收稿日期: 2023-11-04

PACS:

TM93

基金资助:

贵州电网有限责任公司科技资助项目（GZKJXM20222149）

通讯作者: 陈恺男,1997年生,博士研究生,研究方向为能源互联网,电力信息与通信技术等。E-mail:kulewubi@163.com

作者简介: 付宇男,1983年生,高级工程师,硕士,研究方向为配网运行分析。

引用本文:

陈恺, 付宇, 孙毅, 李跃, 杨泓玥. 基于计算热点转移的5G车联网能量实时协同管理策略[J]. 电工技术学报, 0, (): 231896-231896. Chen Kai, Fu Yu, Sun Yi, Li Yue, Yang Hongyue. Online Energy Management Strategy for 5G-Vehicle Network based on Computing Hotspot Transfer. Transactions of China Electrotechnical Society, 0, (): 231896-231896.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.231896 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/231896

[1] Lü Lin, Yang Xinran, Xiang Yue, et al. Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis[J]. Energy, 2020, 198: 117301.
[2] 王晓卫, 康乾坤, 梁振锋, 等. 考虑5G基站储能参与配网供电恢复研究[J/OL]. 电工技术学报, 2023: 1-18. https://kns.cnki.net/kcms/detail/11.2188.TM.20230518.1116.004.html.
Wang Xiaowei, Kang Qiankun, Liang Zhenfeng, et al. Distribution network restoration supply method considers 5G base station energy storage participation[J/OL]. Transactions of China Electrotechnical Society, 2023: 1-18. https://kns.cnki.net/kcms/detail/11.2188.TM.20230518.1116.004.html.
[3] 毛玲, 张钟浩, 赵晋斌, 等. 车-桩-网交融技术研究现状及展望[J]. 电工技术学报, 2022, 37(24): 6357-6371.
Mao Ling, Zhang Zhonghao, Zhao Jinbin, et al. Research status and prospects of fusion technology of vehicle-charging pile-power grid[J]. Transactions of China Electrotechnical Society, 2022, 37(24): 6357-6371.
[4] Zhang Qian, Wu Xiaohan, Deng Xiaosong, et al. Bidding strategy for wind power and Large-scale electric vehicles participating in day-ahead energy and frequency regulation market[J]. Applied Energy, 2023, 341: 121063.
[5] 王海鑫, 袁佳慧, 陈哲, 等. 智慧城市车-站-网一体化运行关键技术研究综述及展望[J]. 电工技术学报, 2022, 37(1): 112-132.
Wang Haixin, Yuan Jiahui, Chen Zhe, et al. Review and prospect of key techniques for vehicle-station-network integrated operation in smart city[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 112-132.
[6] 葛晓琳, 曹士鹏, 符杨, 等. 基于区域解耦的时空双尺度电动汽车优化调度[J/OL]. 中国电机工程学报, 2022: 1-13. https://kns.cnki.net/kcms/detail/11.2107.TM.20220907.1523.006.html.
Ge Xiaolin, Cao Shipeng, Fu Yang, et al. Optimal scheduling of space-time dual-scale electric vehicles based on regional decoupling[J/OL]. Proceedings of the CSEE, 2022: 1-13. https://kns.cnki.net/kcms/detail/11.2107.TM.20220907.1523.006.html.
[7] 蔡国伟, 姜雨晴, 黄南天, 等. 电力需求响应机制下基于多主体双层博弈的规模化电动汽车充放电优化调度[J]. 中国电机工程学报, 2023, 43(1): 85-98.
Cai Guowei, Jiang Yuqing, Huang Nantian, et al. Large-scale electric vehicles charging and discharging optimization scheduling based on multi-agent two-level game under electricity demand response mechanism[J]. Proceedings of the CSEE, 2023, 43(1): 85-98.
[8] 李昆, 方家琨, 艾小猛, 等. 考虑通信与配套设备协调优化的大规模5G宏基站网络能量管理模型[J]. 中国电机工程学报, 2023, 43(14): 5391-5404.
Li Kun, Fang Jiakun, Ai Xiaomeng, et al. Energy management model of large-scale 5G macro base station network considering the coordinated optimization of communication equipment and standard equipment[J]. Proceedings of the CSEE, 2023, 43(14): 5391-5404.
[9] Munir M S, Abedin S F, Tran N H, et al. Risk-aware energy scheduling for edge computing with microgrid: a multi-agent deep reinforcement learning approach[J]. IEEE Transactions on Network and Service Management, 2021, 18(3): 3476-3497.
[10] 麻秀范, 刘子豪, 王颖, 等. 考虑通信负载迁移及储能动态备电的5G基站光伏消纳能力研究[J]. 电工技术学报, 2023, 38(21): 5832-5845, 5922.
Ma Xiufan, Liu Zihao, Wang Ying, et al. Research on photovoltaic absorption capacity of 5G base station considering communication load migration and energy storage dynamic backup[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5832-5845, 5922.
[11] 浪潮5G MEC边缘计算一体机iAIO V1.0发布[J]. 自动化博览, 2021, 38(2): 7.
Inspur 5G MEC edge computing all-in-one iAIO V1.0 released[J]. Automation Panorama, 2021, 38(2): 7.
[12] 长安汽车. 新时代下智能汽车发展趋势与长安实践[EB/OL]. 2023-09-28.https://jcpage.jp/jcevent/file/event/upload/file/301/02_automobile_05_changanqiche_he_C_v2.pdf.
[13] Fan Wenhao, Liu Jie, Hua Mingyu, et al. Joint task offloading and resource allocation for multi-access edge computing assisted by parked and moving vehicles[J]. IEEE Transactions on Vehicular Technology, 2022, 71(5): 5314-5330.
[14] Zhang Jieyi, Zhang Biling, Han Zhu. Coalition formation game based information-energy collaboration in vehicle edge computing networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(6): 7717-7727.
[15] Bahreini T, Brocanelli M, Grosu D. VECMAN: a framework for energy-aware resource management in vehicular edge computing systems[J]. IEEE Transactions on Mobile Computing, 2023, 22(2): 1231-1245.
[16] Wei Zhiwei, Li Bing, Zhang Rongqing, et al. Contract-based charging protocol for electric vehicles with vehicular fog computing: an integrated charging and computing perspective[J]. IEEE Internet of Things Journal, 2023, 10(9): 7667-7680.
[17] 张巍, 王丹. 基于云边协同的电动汽车实时需求响应调度策略[J]. 电网技术, 2022, 46(4): 1447-1458.
Zhang Wei, Wang Dan. Real-time demand response scheduling strategy for electric vehicles based on cloud edge collaboration[J]. Power System Technology, 2022, 46(4): 1447-1458.
[18] 潘建辉, 张宁, 雍培, 等. 面向海量灵活性资源的两阶段分布式协同调度方法[J]. 电力系统自动化, 2023, 47(15): 67-79.
Pan Jianhui, Zhang Ning, Yong Pei, et al. Two-stage distributed collaborative dispatching method for massive flexible resources[J]. Automation of Electric Power Systems, 2023, 47(15): 67-79.
[19] Miao Ankang, Yuan Yue, Wu Han, et al. Planning method and coordinated operation strategy for multi-station integration system[J]. CSEE Journal of Power and Energy Systems, 2023, 9(6): 2394-2408.
[20] 胡龙,方八零,樊飞龙等.用户-基站-充电站能量共享、互动优化方法及在园区中的应用[J/OL].上海交通大学学报, 2023: 1-11. https://doi.org/10.16183/j.cnki.jsjtu.2023.407.
Hu Long, Fang Baling, Fan Feilong, et al. Energy sharing and interaction optimization method for users, base stations, and charging stations: an application in parks[J/OL]. Journal of Shanghai Jiao Tong University, 2023: 1-11. https://doi.org/10.16183/j.cnki.jsjtu.2023.407.
[21] Bahri R, Zeynali S, Nasiri N, et al. Economic-environmental energy supply of mobile base stations in isolated nanogrids with smart plug-in electric vehicles and hydrogen energy storage system[J]. International Journal of Hydrogen Energy, 2023, 48(10): 3725-3739.
[22] 华为. 绿色5G白皮书[EB/OL]. [2021-10-1]. https://www.huawei.com/cn/huaweitech/industry-insights/outlook/green-5g-white-paper.
[23] Chen Xiaojing, Lu Zhouyu, Ni Wei, et al. Cooling-aware optimization of edge server configuration and edge computation offloading for wirelessly powered devices[J]. IEEE Transactions on Vehicular Technology, 2021, 70(5): 5043-5056.
[24] 麻秀范, 孟祥玉, 朱秋萍, 等. 计及通信负载的5G基站储能调控策略[J]. 电工技术学报, 2022, 37(11): 2878-2887.
Ma Xiufan, Meng Xiangyu, Zhu Qiuping, et al. Control strategy of 5G base station energy storage considering communication load[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2878-2887.
[25] ASHRAE. 2021 equipment thermal guidelines for data processing environments [EB/OL]. [2022-06-14]. https://www.ashrae.org/File%20Library/Technical%20Resources/Bookstore/Supplemental%20Files/ReferenceCard_2021ThermalGuidelines.pdf.
[26] Neely M J, Huang Longbo. Dynamic product assembly and inventory control for maximum profit[C] //49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA, 2010: 2805-2812.
[27] Liu Lilan, Zhang Zhizhong, Wang Ning, et al. Online resource management of heterogeneous cellular networks powered by grid-connected smart micro grids[J]. IEEE Transactions on Wireless Communications, 2022, 21(10): 8416-8430.
[28] Tang Ming, V W S Wong. Deep reinforcement learning for task offloading in mobile edge computing systems[J]. IEEE Transactions on Mobile Computing, 2022, 21(6): 1985-1997.