考虑5G网络用电需求及可靠性的变电站双Q规划法

doi:10.19595/j.cnki.1000-6753.tces.220146

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1605 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The large-scale application of 5G base stations can provide high-quality technical support in expanding channel capacity and promoting smart grid construction.5G network not only provides technical support for the transformation and upgrading of substations, but also represents the emerging high-energy consumption power load in the substation supply area. Therefore, it is necessary to consider its power demand during substation planning in the future. Substation planning is faced with such a challenge, that is, to design a double Q planning scheme that takes into account both quantity and quality while reducing the total planning cost. At present, there are few studies on substation location planning for 5G network electricity demand. If the actual peak power consumption of 5G network is used as the basis for substation planning, it will lead to high investment cost and low utilization of substation capacity. It is necessary to further explore substation planning methods that adapt to the large-scale application of 5G base stations and take into account both economy and reliability.
This paper takes into account the economy and reliability of substation planning, and proposes a double Q planning method for substation considering the power demand of 5G network and reliability. Firstly, the energy saving potential of 5G network is analyzed, and the energy efficiency index of 5G network is established. Then, the power demand of 5G network is analyzed from two aspects: the load power consumption after optimizing the energy efficiency of 5G network and the backup power consumption of 5G base station. The energy efficiency optimization model of 5G network is designed based on the base station sleep strategy. The standby energy storage and adjustable energy storage of 5G base station are quantified according to the spatial and temporal distribution of communication load. The standby energy storage supplies power to the base station when the power grid is faulty to ensure the reliability of the power supply of the base station. The energy storage can be regulated to participate in peak cutting and valley filling when the power grid is faulty, and also participate in fault power supply when the power grid is faulty to improve the reliability of the supply area of the substation. Finally, the double Q planning method of substation is transformed into the substation planning problem considering the electricity demand of 5G network and the opportunistic constraint regulation problem of base station energy storage under different reliability confidence in the substation supply area, so as to achieve the double Q goal of substation planning.
Through the analysis of numerical examples, the practicability and effectiveness of the proposed double Q programming method for substation planning are verified, and the following conclusions are drawn: The energy efficiency of 5G network is optimized by executing the sleep strategy for the micro-base station. Although the overall power consumption of 5G network is reduced, the quality of service of communication load is guaranteed. The storage capacity of 5G base station is reserved to ensure uninterrupted power supply for the base station in case of power grid failure, and the power supply reliability of the base station is guaranteed. Activate redundant energy storage of base station to respond to power grid demand, participate in fault power supply, peak cutting and valley filling for profit, and realize mutual benefit and win-win between power grid and communication operators. The economy and reliability of substation planning are guaranteed.

Key words： Energy efficiency of 5G network base station energy storage double Q planning demand response reliability

Received: 28 January 2022

PACS:	TM715
	TM732

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Ma Xiufan
	Feng Xiaoyu

Cite this article:

Ma Xiufan,Feng Xiaoyu. Double Q Planning Method for Substation Considering Power Demand of 5G Network and Reliability[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 2962-2976.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220146 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I11/2962

[1] 张宁, 杨经纬, 王毅, 等. 面向泛在电力物联网的5G通信: 技术原理与典型应用[J]. 中国电机工程学报, 2019, 39(14): 4015-4024.
Zhang Ning, Yang Jingwei, Wang Yi, et al.5G communication for the ubiquitous Internet of Things in electricity: technical principles and typical applications[J]. Proceedings of the CSEE, 2019, 39(14): 4015-4024.
[2] Gandotra P, Jha R K, Jain S.Prolonging user battery lifetime using green communication in spectrum sharing networks[J]. IEEE Communications Letters, 2018, 22(7): 1490-1493.
[3] 黄彦钦, 余浩, 尹钧毅, 等. 电力物联网数据传输方案:现状与基于5G技术的展望[J]. 电工技术学报, 2021, 36(17): 3581-3593.
Huang Yanqin, Yu Hao, Yin Junyi, et al.Data transmission schemes of power Internet of Things: present and outlook based on 5G technology[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3581-3593.
[4] 刘雨佳, 樊艳芳. 计及5G基站储能和技术节能措施的虚拟电厂调度优化策略[J]. 电力系统及其自动化学报, 2022, 34(1): 8-15.
Liu Yujia, Fan Yanfang.Optimal scheduling strategy for virtual power plant considering 5G base station technology, energy-storage, and energy-saving measures[J]. Proceedings of the CSU-EPSA, 2022, 34(1): 8-15.
[5] 周宸宇, 冯成, 王毅. 基于移动用户接入控制的5G通信基站需求响应[J]. 中国电机工程学报, 2021, 41(16): 5452-5461.
Zhou Chenyu, Feng Cheng, Wang Yi.Demand response of 5G communication base stations based on admission control of mobile users[J]. Proceedings of the CSEE, 2021, 41(16): 5452-5461.
[6] 李笑蓉, 王婕, 丁健民, 等. 基于Voronoi图的多站融合变电站选址定容优化[J]. 电网与清洁能源, 2020, 36(2): 44-54.
Li Xiaorong, Wang Jie, Ding Jianmin, et al.Optimal sizing and locating of multi-functional integrated substation based on voronoi diagram[J]. Power System and Clean Energy, 2020, 36(2): 44-54.
[7] 朱文广, 张长生, 熊宁, 等. 考虑综合能源站用电需求不确定性的配电网变电站机会约束规划[J]. 电力建设, 2019, 40(8): 19-25.
Zhu Wenguang, Zhang Changsheng, Xiong Ning, et al.Chance-constrained programming for distribution substations considering uncertainty of electric load in comprehensive energy stations[J]. Electric Power Construction, 2019, 40(8): 19-25.
[8] 王梓旭, 林伟, 杨知方, 等. 考虑负荷弹性空间的配电网可靠性扩展规划方法[J/OL]. 中国电机工程学报: 1-13[2021-12-01].https://kns.cnki.net/kcms/ detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=ZGDC2021111100B&uniplatform=NZKPT&v=3UDY-INjhgox-n2Buq0hMGs6xfLLP1ST0 mC3ftSVZxhMcwfrIOC_UmGagZla2zpj.
Wang Zixu, Lin Wei, Yang Zhifang, et al.A reliability-constrained distribution network expansion planning method considering flexibility space of power demand[J/OL]. Proceedings of CSEE: 1-13[2021-12-01].https://kns.cnki.net/kcms/detail/detail. aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=ZGDC2021111100B&uniplatform=NZKPT&v=3UDY-INjhgox-n2Buq0hMGs6xfLLP1ST0mC3ftSVZxh McwfrIOC_UmGagZla2zpj.
[9] 王梓耀, 陈俊斌, 林丹, 等. 基于精英蚁群Q算法的中压配电网双Q规划模型[J]. 电力自动化设备, 2020, 40(11): 32-39.
Wang Ziyao, Chen Junbin, Lin Dan, et al.Double Q planning model for medium voltage distribution network based on Elite Ant-Q algorithm[J]. Electric Power Automation Equipment, 2020, 40(11): 32-39.
[10] 麻秀范, 陈静, 余思雨, 等. 计及容量市场的用户侧储能优化配置研究[J]. 电工技术学报, 2020, 35(19): 4028-4037.
Ma Xiufan, Chen Jing, Yu Siyu, et al.Research on user side energy storage optimization configuration considering capacity market[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4028-4037.
[11] 边晓燕, 史越奇, 裴传逊, 等. 计及经济性和可靠性因素的区域综合能源系统双层协同优化配置[J]. 电工技术学报, 2021, 36(21): 4529-4543.
Bian Xiaoyan, Shi Yueqi, Pei Chuanxun, et al.Bi-level collaborative configuration optimization of integrated community energy system considering economy and reliability[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4529-4543.
[12] 汪露露, 吴红斌, 周亦尧. 基于供能可靠性的综合能源系统优化配置[J]. 太阳能学报, 2021, 42(12): 395-400.
Wang Lulu, Wu Hongbin, Zhou Yiyao.Optimal configuration of integrated energy system based on energy supply reliability[J]. Acta Energiae Solaris Sinica, 2021, 42(12): 395-400.
[13] 刘战捷. 计及基站备用储能的电力系统经济调度[D]. 济南: 山东大学, 2018.
[14] 雍培, 张宁, 慈松, 等. 5G通信基站参与需求响应:关键技术与前景展望[J]. 中国电机工程学报, 2021, 41(16): 5540-5551.
Yong Pei, Zhang Ning, Ci Song, et al.5G communication base stations participating in demand response: key technologies and prospects[J]. Proceedings of the CSEE, 2021, 41(16): 5540-5551.
[15] 刘友波, 王晴, 曾琦, 等. 能源互联网背景下5G网络能耗管控关键技术及展望[J]. 电力系统自动化, 2021, 45(12): 174-183.
Liu Youbo, Wang Qing, Zeng Qi, et al.Key technologies and prospects of energy consumption management for 5G network in background of energy internet[J]. Automation of Electric Power Systems, 2021, 45(12): 174-183.
[16] 刘娇. 面向5G超密集网络基站协同节能关键技术研究[D]. 北京: 北京交通大学, 2018.
[17] 徐越. 基于机器学习的无线网络负载优化方法研究[D]. 北京: 北京邮电大学, 2020.
[18] 马忠贵, 宋佳倩. 5G超密集网络的能量效率研究综述[J]. 工程科学学报, 2019, 41(8): 968-980.
Ma Zhonggui, Song Jiaqian.Survey of energy efficiency for 5G ultra-dense networks[J]. Chinese Journal of Engineering, 2019, 41(8): 968-980.
[19] 郭浩然. 超密集部署下基于能效的用户关联算法的研究[D]. 北京: 华北电力大学, 2019.
[20] 陈永红, 郭莉莉, 张士兵, 等. 基于微基站发射功率的异构蜂窝网络能效优化[J]. 计算机应用, 2020, 40(4): 1115-1118.
Chen Yonghong, Guo Lili, Zhang Shibing, et al.Energy efficiency optimization of heterogeneous cellular networks based on transmitting power of pico base station[J]. Journal of Computer Applications, 2020, 40(4): 1115-1118.
[21] 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.
[22] 齐先军, 程桥, 吴红斌, 等. 激励型需求响应对配电网运行可靠性的影响[J]. 电工技术学报, 2018, 33(22): 5319-5326.
Qi Xianjun, Cheng Qiao, Wu Hongbin, et al.Impact of incentive-based demand response on opreational reliability of distribution network[J]. Transactions of China Electrotechnical Society, 2018, 33(22): 5319-5326.
[23] 李阳洋, 关轶文, 赵佳琪, 等. 基于优化模型的有源配电网可靠性评估方法[J/OL]. 中国电机工程学报: 1-10.[2022-04-05]. https://kns.cnki.net/kcms/ detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=ZGDC20220321001&uniplatform=NZKPT&v=66MZTQRHAao6yMAHxmfBzK_zNr4syRqw-5G04ixOhxtX5xeYBsYTXdqpt7GcVcPy.
Li Yangyang, Guan Yiwen, Zhao Jiaqi, et al.Reliability evaluation method of active distribution network based on optimization model[J/OL]. Proceedings of the CSEE: 1-10.[2022-04-05]. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=ZGDC20220321001&uniplatform=NZKPT&v=66MZTQRHAao6yMAHxmfBzK_zNr4syRqw-5G04ixOhxtX5xeYBsYTXdqpt7GcVcPy.
[24] 杨雨薇. 含风储并网的电力系统可靠性评估方法研究[D]. 北京: 华北电力大学(北京), 2021.
[25] 邓嘉明, 李俊杰, 姜世公, 等. 考虑配电网规划长期增量成本的可靠性定价方法[J]. 电网与清洁能源, 2021, 37(3): 17-23, 30.
Deng Jiaming, Li Junjie, Jiang Shigong, et al.A reliability pricing method considering long-term incremental cost of distribution network planning[J]. Power System and Clean Energy, 2021, 37(3): 17-23, 30.
[26] 赵传, 戴朝华, 付洋, 等. 考虑风电预测误差与系统安全域的风电装机规划多目标优化方法[J]. 太阳能学报, 2020, 41(2): 110-117.
Zhao Chuan, Dai Chaohua, Fu Yang, et al.Multi-objective optimization of wind power planning considering wind power predictive encoding and system security domain[J]. Acta Energiae Solaris Sinica, 2020, 41(2): 110-117.
[27] 刘志宏, 赵杰行, 薛鹏飞, 等. 面向偏远地区供电可靠性提升的配网储能电站机会约束规划方法[J]. 电网与清洁能源, 2021, 37(8): 128-138.
Liu Zhihong, Zhao Jiexing, Xue Pengfei, et al.Chance constrained planning of distribution-level energy storage stations for improving power supply reliability in remote areas[J]. Power System and Clean Energy, 2021, 37(8): 128-138.
[28] 刘洪, 王博, 李梅菊, 等. 基于改进加权Voronoi图算法的有源配电网变电站规划[J]. 电力系统自动化, 2017, 41(13): 45-52.
Liu Hong, Wang Bo, Li Meiju, et al.Substation planning of active distribution network based on improved weighted voronoi diagram method[J]. Automation of Electric Power Systems, 2017, 41(13): 45-52.
[29] 李振坤, 岳美, 胡荣, 等. 计及分布式电源与可平移负荷的变电站优化规划[J]. 中国电机工程学报, 2016, 36(18): 4883-4893, 5112.
Li Zhenkun, Yue Mei, Hu Rong, et al.Optimal planning of substation considering distributed generation and shiftable loads[J]. Proceedings of the CSEE, 2016, 36(18): 4883-4893, 5112.
[30] 周童, 程方, 张治中, 等. 基于非泊松点过程建模的微基站部署研究[J]. 计算机应用研究, 2021, 38(12): 3730-3732, 3738.
Zhou Tong, Cheng Fang, Zhang Zhizhong, et al.Pico base station deployment based on non-Poisson point process modeling[J]. Application Research of Computers, 2021, 38(12): 3730-3732, 3738.
[31] 从子奇. 基于数据挖掘的TD-LTE基站负载研究[D]. 北京: 北京邮电大学, 2018.
[32] 金娜. 无线蜂窝网络中基于遗传算法的能效策略研究[D]. 武汉: 华中科技大学, 2017.
[33] 尚博阳. 用户侧储能优化配置与运行方法研究[D]. 北京: 北京交通大学, 2021.
[34] 朱介北, 邱威, 孙宁, 等. 基于序贯蒙特卡洛法的安全稳定控制系统架构可靠性分析[J]. 电力系统自动化, 2021, 45(15): 21-27.
Zhu Jiebei, Qiu Wei, Sun Ning, et al.Reliability analysis of security and stability control system architecture based on sequential Monte Carlo method[J]. Automation of Electric Power Systems, 2021, 45(15): 21-27.
[35] 倪伟, 吕林, 向月, 等. 基于马尔可夫过程蒙特卡洛法的综合能源系统可靠性评估[J]. 电网技术, 2020, 44(1): 150-158.
Ni Wei, Lü Lin, Xiang Yue, et al.Reliability evaluation of integrated energy system based on Markov process Monte Carlo method[J]. Power System Technology, 2020, 44(1): 150-158.