考虑5G基站储能参与配网供电恢复研究

doi:10.19595/j.cnki.1000-6753.tces.230341

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摘要

5G的快速发展使得基站储能容量大幅增加,如何充分发挥时常处于休眠状态的基站储能资源,使其参与配电网供电恢复,降低配网失电量亟待研究。本文通过协调基站储能参与配电网供电恢复,以发挥其储能价值：首先建立风光联合出力场景集,通过赤池信息准则和平方欧式距离确定各时段最优Copula函数,进而对其进行抽样聚类获得风光典型出力场景集;其次建立基站备用储能模型,通过正弦函数叠加建立不同区域的通信量模型,利用泰尔熵和修正基尼系数建立综合脆弱度模型确定各基站储能备用时间及备用储能模型,进而获得不同基站储能的可调度容量;最后以失负荷量损失最小为目标,建立考虑基站储能参与及风光出力波动的含可调参数的两阶段鲁棒优化模型。以改进的IEEE-33节点模型为例进行仿真验证,分别对比了定基站备用时间与本文方法确定备用时间、鲁棒优化与确定性优化等方案对比下系统失负荷量的变化情况,仿真结果表明,5G基站储能可降低失负荷量损失成本,提高风光消纳率。

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关键词 ： 5G基站, 备用储能, 供电恢复, 修正基尼系数

Abstract：

China has vigorously promoted the development of 5G communication in recent years. The number of 5G base stations is growing rapidly, greatly demanding energy storage capacity. At present, China's power supply reliability has improved. Base stations' energy storage is often in a dormant state, thus causing a large amount of energy storage resource waste. It is urgent to study how to use the base station's energy storage resources and play its energy storage value. This paper proposed a 5G base stations' energy storage scheduling model, which jointly participates in the power supply restoration of the distribution network by combining wind-solar output, thus reducing the power loss load of the distribution network.
Firstly, the wind-solar combined output scenario set is established. The optimal Copula function in each period is determined by the Akaike information criterion and squared Euclidian distance, obtained the typical wind-solar output scenario set by sampling and clustering. Second, a backup energy storage model for 5G base stations is established. Different regions communication traffic volume models are established through sine function superposition. The synthetic vulnerability model is established by using the Theil's entropy and the modified Gini coefficient to determine the backup time and the backup storage model of each base station energy storage. Then the schedulable capacity of different base stations' energy storage is obtained. Finally, a two-stage robust optimization model considering the synergistic scheduling of base stations' energy storage and wind-solar output is established with the objective of minimizing the loss of load volume.
Simulations are performed with the improved IEEE-33 node model. The results indicate that the reserve capacity of 5G base station' energy storage is directly proportional to the node vulnerability of the power grid and inversely proportional to the load level. Compared with the traditional fixed backup time, this paper's method can increase the base stations' callable capacity by 317kWh. When performing emergency power restoration to the distribution network, it reduced the lost load by 1609kW and the cost of lost power by ￥47,208.9 compared to the fixed backup time approach. At the same time, it can use the 5G base station's energy storage for wind power and photovoltaic absorption during the restoration of the power supply in the distribution network. Among them, the wind-light absorption rate increases by 0.0079, 0.092, and 0.1818 at the moments of t = 14h, t = 15h, and t = 16h, respectively, which in turn reduces the wind and light abandonment rate of the system and improves the distribution network economics.
The following conclusions can be drawn from the simulation analysis: (1) Make full use of base station energy storage, improve the utilization rate of base station energy storage, and reduce the amount of distribution network load loss. At the same time, it can enhance the wind-solar absorption rate and reduce the degree of wind and light abandonment. (2) Combining the vulnerability of the grid node where the base station is located and the communication volume, the backup energy storage is dynamically determined, which can make full use of the energy storage resources of the base station and bring its value into play.

Key words： 5G base station backup energy storage power supply restoration corrected Gini coefficient

收稿日期: 2023-03-23

PACS:

TM732

基金资助:

国家自然科学基金（52177114,61403127）和国网江西省电力有限公司科技项目（521820210005,521820220016）资助

通讯作者: 王晓卫男,1983年生,博士,副教授,博士生导师,研究方向为配电网故障选线与定位、高阻故障检测、5G在配网中的应用。E-mail：proceedings@126.com

作者简介: 康乾坤男,1999年生,硕士研究生,研究方向为5G在配电网中的应用。E-mail：kangqk_edu@126.com

引用本文:

王晓卫, 康乾坤, 梁振锋, 郭亮, 张帆. 考虑5G基站储能参与配网供电恢复研究[J]. 电工技术学报, 0, (): 230637-230637. Wang Xiaowei, Kang Qiankun, Liang Zhenfeng, Guo Liang, Zhang Fan. Distribution Network Restoration Supply Method Considers 5G Base Station Energy Storage Participation. Transactions of China Electrotechnical Society, 0, (): 230637-230637.

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[1] 腾讯网. 中国工信部:加快5G、工业互联网等新型信息基础设施建设和应用[EB/OL].[2023-03-14] https://new.qq.com/rain/a/20230314A08RJ300.
[2] 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.
[3] 蒋廷耀, 谢龙恩, 杜雨, 等. 基于深度强化学习的5G基站储能调度策略[J]. 电力系统自动化, 2023, 47(9): 147-157.
Jiang Tingyao, Xie Longen, Du Yu, et al.Dispatching strategy of energy storage for 5G base stations based on deep reinforcement learning[J]. Automation of Electric Power Systems, 2023, 47(9): 147-157.
[4] Yong Pei, Zhang Ning, Hou Qingchun, et al.Evaluating the dispatchable capacity of base station backup batteries in distribution networks[J]. IEEE Transactions on Smart Grid, 2021, 12(5): 3966-3979.
[5] 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.
[6] 岑海凤, 许苑, 王军伟, 等. 通信基站备用电池的云储能系统设计与应用[J]. 电源技术, 2020, 44(6): 902-904.
Cen Haifeng, Xu Yuan, Wang Junwei, et al.Design and application of cloud energy storage system for backup battery in communication base station[J]. Chinese Journal of Power Sources, 2020, 44(6): 902-904.
[7] 中国新闻网. 深圳5G基站储能将全部接入深圳虚拟电厂管理中心[EB/OL].[2022-12-14] https://baijiahao.baidu.com/s?id=1752198644606400924&wfr=spider&for=pc.
[8] 林固静, 高赐威, 宋梦, 等. 含通信基站备用储能的虚拟电厂构建及调度方法[J]. 电力系统自动化, 2022, 46(18): 99-107.
Lin Gujing, Gao Ciwei, Song Meng, et al.Construction and scheduling method of virtual power plant with backup energy storage of communication base station[J]. Automation of Electric Power Systems, 2022, 46(18): 99-107.
[9] 麻秀范,冯晓瑜. 考虑5G网络用电需求及可靠性的变电站双Q规划法[J].电工技术学报:1-14[2023-03-16].
Ma Xiufan,Feng Xiaoyu.Double Q Planning Method for Substation Considering Power Demand of 5G Network and Reliability[J]. Transactions of China Electrotechnical Society. 1-14[2023-03-16].
[10] 胡思洋, 廖凯, 杨健维, 等. 基于V2G技术的城市电网供电恢复策略[J]. 电力自动化设备:1-17[2023-03-16].
Hu Siyang, Liao Kai, Yang Jianwei, et al.Power supply restoration strategy of urban power grid based on V2G technology[J]. Electric Power Automation Equipment:1-17[2023-03-16].
[11] 刘伟佳, 孙磊, 林振智, 等. 含间歇电源、储能和电动汽车的配电孤岛短时恢复供电策略[J]. 电力系统自动化, 2015, 39(16): 49-58.
Liu Weijia, Sun Lei, Lin Zhenzhi, et al.Short-period restoration strategy in isolated electrical islands with intermittent energy sources, energy storage systems and electric vehicles[J]. Automation of Electric Power Systems, 2015, 39(16): 49-58.
[12] 许寅, 王颖, 和敬涵, 等. 多源协同的配电网多时段负荷恢复优化决策方法[J]. 电力系统自动化, 2020, 44(2): 123-131.
Xu Yin, Wang Ying, He Jinghan, et al.Optimal decision-making method for multi-period load restoration in distribution network with coordination of multiple sources[J]. Automation of Electric Power Systems, 2020, 44(2): 123-131.
[13] 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): 5540-5552.
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): 5540-5552.
[14] 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): 5452-5462.
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): 5452-5462.
[15] 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.
[16] 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, .
[17] 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:1-15[2023-03-16].
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 Socity: 1-15[2023-03-16].
[18] 陈泽雄, 张新民, 王雪锋, 等. 分布式光伏电站接入配电网的分布鲁棒优化配置方法[J]. 电力系统保护与控制, 2021, 49(13): 30-42.
Chen Zexiong, Zhang Xinmin, Wang Xuefeng, et al.A distributionally robust optimal allocation method for distributed photovoltaic generation stations integrated into a distribution network[J]. Power System Protection and Control, 2021, 49(13): 30-42.
[19] Zare A, Chung C Y, Zhan Junpeng, et al.A distributionally robust chance-constrained MILP model for multistage distribution system planning with uncertain renewables and loads[J]. IEEE Transactions on Power Systems, 2018, 33(5): 5248-5262.
[20] 顾雪平, 白岩松, 李少岩, 等. 考虑风电不确定性的电力系统恢复全过程两阶段鲁棒优化方法[J]. 电工技术学报, 2022, 37(21): 5462-5477.
Gu Xueping, Bai Yansong, Li Shaoyan, et al.Two-stage robust optimization method for the whole process of power system restoration considering wind power uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5462-5477.
[21] 瞿凯平, 苏伟行, 姜宇轩, 等. 基于点估计仿射可调鲁棒优化的含储能电力系统实时调度[J]. 电网技术:1-12[2023-03-16].
Qu Kaiping, Su Weihang, Jiang Yuxuan, et al.Real-time power dispatch with storages using a point estimate-based affinely adjustable robust optimization[J]. Power System Technology: 1-12[2023-03-16].
[22] 包广清, 周家武, 马明, 等. 考虑风电波动不确定性的两阶段鲁棒优化分频调度方法[J]. 电网技术, 2020, 44(12): 4530-4538.
Bao Guangqing, Zhou Jiawu, Ma Ming, et al.Two-stage frequency division robust optimal dispatching method considering wind power fluctuation uncertainty[J]. Power System Technology, 2020, 44(12): 4530-4538.
[23] Xu Xiaoyuan, Yan Zheng, Shahidehpour M, et al.Data-driven risk-averse two-stage optimal stochastic scheduling of energy and reserve with correlated wind power[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 436-447.
[24] 段偲默, 苗世洪, 霍雪松, 等. 基于动态Copula的风光联合出力建模及动态相关性分析[J]. 电力系统保护与控制, 2019, 47(5): 35-42.
Duan Simo, Miao Shihong, Huo Xuesong, et al.Modeling and dynamic correlation analysis of wind/solar power joint output based on dynamic Copula[J]. Power System Protection and Control, 2019, 47(5): 35-42.
[25] 关磊. 基于蜂窝网络业务空时分布规律的高能效服务机制研究[D]. 北京: 北京邮电大学, .
[26] Yong Pei, Zhang Ning, Liu Yuxiao, et al.Exploring the cellular base station dispatch potential towards power system frequency regulation[J]. IEEE Transactions on Power Systems, 2022, 37(1): 820-823.
[27] 张弘历, 李华强, 杨植雅, 等. 基于潮流增长率泰尔熵的脆弱支路辨识[J]. 电网技术, 2017, 41(7): 2340-2346.
Zhang Hongli, Li Huaqiang, Yang Zhiya, et al.Identification of vulnerable line based on the theil entropy of flow growth rate[J]. Power System Technology, 2017, 41(7): 2340-2346.
[28] 李雪. 基于均匀性理论的电网脆弱性评估与应用[D]. 徐州: 中国矿业大学, .
[29] 刘一欣, 郭力, 王成山. 微电网两阶段鲁棒优化经济调度方法[J]. 中国电机工程学报, 2018, 38(14): 4013-4022, 4307.
Liu Yixin, Guo Li, Wang Chengshan.Economic dispatch of microgrid based on two stage robust optimization[J]. Proceedings of the CSEE, 2018, 38(14): 4013-4022, 4307.
[30] 郭亮, 王晓卫, 康乾坤, 等. 基于粒子群优化与K-means聚类的配网5G改造经济性评价方法[J]. 电网与清洁能源, 2022, 38(6): 31-36, 43.
Guo Liang, Wang Xiaowei, Kang Qiankun, et al.An economic evaluation method for 5G transformation of distribution network based on particle swarm optimization and K-means clustering[J]. Power System and Clean Energy, 2022, 38(6): 31-36, 43.
[31] 闫涵, 王建华, 范须露, 等. 基于用户停电损失评估的有源配电网灾后供电恢复模型[J]. 电力系统自动化, 2022, 46(5): 31-42.
Yan Han, Wang Jianhua, Fan Xulu, et al.Post-disaster power supply restoration model for active distribution network based on customer interruption cost assessment[J]. Automation of Electric Power Systems, 2022, 46(5): 31-42.
[32] 丁浩然, 张博, 唐巍, 等. 考虑源-网-荷-储协同的配电台区分布式光伏消纳能力评估[J]. 供用电, 2023, 40(3): 2-8, 31.
Ding Haoran, Zhang Bo, Tang Wei, et al.Evaluation of distributed photovoltaic consumption capacity of distribution station area considering source-network-load-storage collaboration[J]. Distribution & Utilization, 2023, 40(3): 2-8, 31.