Abstract:The massive load from the surge of the 5G base station construction affects the operational economics of the distribution network. However, base station backup energy storage can participate in peak reduction and valley filling under the guarantee of reliability and dynamic power backup. How to use distribution network reconfiguration and energy storage regulation to improve the operational economy of both sides needs to be studied. Firstly, considering that the 5G base station backup energy storage needs to ensure the uninterrupted operation of the base station, this paper analyses the regulation potential of 5G base station backup energy storage and establishes a model for the regulable capacity of base station energy storage. Secondly, considering that the topology of the distribution network is changed after reconfiguration and the power supply paths of some nodes are changed, which leads to changes in node reliability and changes in the 5G base station backup demand, this paper use the sequential Monte Carlo method to evaluates the node reliability of the distribution network with different topologies and proposes a method to determine the backup duration of base station energy storage considering the node reliability. Thirdly, this paper constructs a two-layer optimization model for distribution network reconfiguration considering optimal regulation of 5G base station backup energy storage. The upper-layer model is reconfigured with the objective of minimum comprehensive operating cost of the distribution network, and the lower-layer model determines the base station energy storage regulation strategy with the objectives of minimum the base station operating cost and load curve variance. Finally, the CPLEX commercial solver in Yalmip toolbox of Matlab platform is used to solve the model. The upper-layer model transmits the reconfiguration results to the lower-layer, and the lower-layer model determines the backup duration of 5G base station energy storage under this topology structure and solves the base station energy storage regulation strategy, and then transmits the updated load data to the upper-layer for the next round of reconfiguration solving. The upper-layer and lower-layer models iterate with each other to obtain the upper-layer reconfiguration strategy of distribution network and the lower-layer regulation strategy of 5G base station energy storage. The results show that the distribution network reconfiguration considering base station backup energy storage in peak reduction and valley filling reduces the daily comprehensive operation cost by 12.22 yuan compared to that without consideration, while improving the quality of the node voltage level. The 5G base station storage regulation considering the distribution network reconfiguration improves the node reliability, so that the base station energy storage has more regulation space, and reduces the daily base station operation cost by 101.6 yuan. The following conclusions can be drawn from the analysis of the example:(1) The distribution network reconfiguration considering the role of 5G base station backup energy storage in peak reduction and valley filling improves the operation of the distribution network in terms of economy and safety. (2) The 5G base station backup energy storage regulation strategy under the optimized distribution network topology reduces the operation cost of 5G base stations while reducing the comprehensive operation cost of the distribution network, and realizes the mutual benefits for both the base station and the power grid. (3) The base station energy storage backup considering the reliability of distribution network nodes can distinguish the energy storage backup duration of 5G base stations connected to different nodes, and refine the energy storage regulation potential of each base station.
麻秀范, 张乐萱, 于琨澎, 杨璐. 考虑5G基站备用储能优化调控的配电网重构双层优化方法[J]. 电工技术学报, 2024, 39(16): 5028-5041.
Ma Xiufan, Zhang Lexuan, Yu Kunpeng, Yang Lu. A Two-Layer Optimization Approach for Distribution Network Reconfiguration Considering Optimal Regulation of 5G Base Station Backup Energy Storage. Transactions of China Electrotechnical Society, 2024, 39(16): 5028-5041.
[1] 工信部运行监测协调局. 2022年通信业统计公报[J]. 通信企业管理, 2023(2): 8-12. [2] 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. [3] Sarma N D R, Prakasa Rao K S. A new 0-1 integer programming method of feeder reconfiguration for loss minimization in distribution systems[J]. Electric Power Systems Research, 1995, 33(2): 125-131. [4] Ahmad Quadri I, Bhowmick S, Joshi D.Multi-objective approach to maximise loadability of distribution networks by simultaneous reconfiguration and allocation of distributed energy resources[J]. IET Generation, Transmission & Distribution, 2018, 12(21): 5700-5712. [5] 李超, 苗世洪, 盛万兴, 等. 考虑动态网络重构的主动配电网优化运行策略[J]. 电工技术学报, 2019, 34(18): 3909-3919. Li Chao, Miao Shihong, Sheng Wanxing, et al.Optimization operation strategy of active distribution network considering dynamic network reconfiguration[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3909-3919. [6] Ma Xiufan, Zhu Qiuping, Duan Ying, et al.Optimal configuration of 5G base station energy storage considering sleep mechanism[J]. Global Energy Interconnection, 2022, 5(1): 66-76. [7] 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. [8] 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. [9] 乔锐勋, 王军华, 韦道明, 等. 计及后备储能及空调调度潜力的5G基站多时间尺度优化方法[J]. 电力系统自动化, 2023, 47(4): 111-120. Qiao Ruixun, Wang Junhua, Wei Daoming, et al.Multi-time-scale optimization method for 5G base station considering backup energy storage and air-conditioning scheduling potential[J]. Automation of Electric Power Systems, 2023, 47(4): 111-120. [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] 李忠瑞, 聂子玲, 艾胜, 等. 一种基于非线性扰动观测器的飞轮储能系统优化充电控制策略[J]. 电工技术学报, 2023, 38(6): 1506-1518. Li Zhongrui, Nie Ziling, Ai Sheng, et al.An optimized charging control strategy for flywheel energy storage system based on nonlinear disturbance observer[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1506-1518. [12] 徐卫君, 张伟, 胡宇涛, 等. 先进绝热压缩空气储能多能流优化调度模型[J]. 电工技术学报, 2022, 37(23): 5944-5955. Xu Weijun, Zhang Wei, Hu Yutao, et al.Multi energy flow optimal scheduling model of advanced adiabatic compressed air energy storage[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5944-5955. [13] 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 dispatch method of virtual power plant with backup energy storage in communication base stations[J]. Automation of Electric Power Systems, 2022, 46(18): 99-107. [14] 曾博, 穆宏伟, 董厚琦, 等. 考虑5G基站低碳赋能的主动配电网优化运行[J]. 上海交通大学学报, 2022, 56(3): 279-292. Zeng Bo, Mu Hongwei, Dong Houqi, et al.Optimization of active distribution network operation considering decarbonization endowment from 5G base stations[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 279-292. [15] 毛安家, 张丽婧, 盛倩倩. 考虑通信可靠性的5G基站储能聚合商优化调度研究[J]. 电工技术学报, 2023, 38(9): 2364-2374. Mao Anjia, Zhang Lijing, Sheng Qianqian.Research on optimal scheduling of 5G base station energy storage aggregators considering communication reliability[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2364-2374. [16] 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. [17] 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, 2023, 38(11): 2962-2976. 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. [18] 李俊双, 胡炎, 邰能灵. 计及通信负载与供电可靠性的5G基站储能与配电网协同优化调度[J]. 上海交通大学学报, 2023, 57(7): 791-802. Li Junshuang, Hu Yan, Tai Nengling.Collaborative optimization scheduling of 5G base station energy storage and distribution network considering communication load and power supply reliability[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 791-802. [19] 黄鸣宇, 张庆平, 张沈习, 等. 高比例清洁能源接入下计及需求响应的配电网重构[J]. 电力系统保护与控制, 2022, 50(1): 116-123. Huang Mingyu, Zhang Qingping, Zhang Shenxi, et al.Distribution network reconfiguration considering demand-side response with high penetration of clean energy[J]. Power System Protection and Control, 2022, 50(1): 116-123. [20] 林文键, 朱振山, 温步瀛. 含电动汽车和智能软开关的配电网动态重构[J]. 电力自动化设备, 2022, 42(10): 202-209, 217. Lin Wenjian, Zhu Zhenshan, Wen Buying.Dynamic reconfiguration of distribution network with electric vehicles and soft open point[J]. Electric Power Automation Equipment, 2022, 42(10): 202-209, 217. [21] 申洪涛, 岳凡丁, 史轮, 等. 考虑DG及负荷时序性的多目标配电网重构与DG调控综合优化规划[J]. 现代电力, 2022, 39(2): 182-194. Shen Hongtao, Yue Fanding, Shi Lun, et al.Comprehensive optimal planning of multi-objective distribution network reconfiguration and DG regulation considering DG and load sequence[J]. Modern Electric Power, 2022, 39(2): 182-194. [22] 中华人民共和国住房和城乡建设部. 通信电源设备安装工程设计规范: GB 51194—2016[S]. 北京: 中国计划出版社, 2017. [23] 何洛滨. 含分布式电源的配电网可靠性建模与供电可靠性研究[D]. 北京: 北京交通大学, 2018. He Luobin.Research on reliability modeling and power supply reliability of distribution network with distributed generation[D]. Beijing: Beijing Jiaotong University, 2018. [24] 王舒萍, 张沈习, 程浩忠, 等. 计及用户热舒适度的综合能源系统可靠性指标及评估方法[J]. 电力系统自动化, 2023, 47(1): 86-95. Wang Shuping, Zhang Shenxi, Cheng Haozhong, et al.Reliability indices and evaluation method of integrated energy system considering thermal comfort level of customers[J]. Automation of Electric Power Systems, 2023, 47(1): 86-95. [25] 李锰, 王利利, 刘向实, 等. 基于门当户对遗传算法的配电网多目标主动重构研究[J]. 电力系统保护与控制, 2019, 47(7): 30-38. Li Meng, Wang Lili, Liu Xiangshi, et al.Multi-objective active reconfiguration of distribution network based on the “properly matched marriage” genetic algorithm[J]. Power System Protection and Control, 2019, 47(7): 30-38. [26] 张帅, 刘文霞, 张艺伟, 等. 计及多重热惯性特征的区域综合能源系统可靠性评估[J]. 电工技术学报, 2023, 38(12): 3289-3305. Zhang Shuai, Liu Wenxia, Zhang Yiwei, et al.Reliability assessment of regional integrated energy system considering with multiple thermal inertia characteristics[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3289-3305. [27] 章博, 刘晟源, 林振智, 等. 高比例新能源下考虑需求侧响应和智能软开关的配电网重构[J]. 电力系统自动化, 2021, 45(8): 86-94. Zhang Bo, Liu Shengyuan, Lin Zhenzhi, et al.Distribution network reconfiguration with high penetration of renewable energy considering demand response and soft open point[J]. Automation of Electric Power Systems, 2021, 45(8): 86-94. [28] 郭昕瑜. 含分布式电源的配电网网络重构研究[D]. 西安: 西安石油大学, 2020. Guo Xinyu.Research on distribution network reconfiguration with distributed generation[D]. Xi’an: Xi’an Shiyou University, 2020. [29] 胡斌. 基于小生境改进遗传算法的配电网重构与可靠性分析[D]. 广州: 华南理工大学, 2010. Hu Bin.Distribution network reconfiguration and reliability analysis based on niche improved genetic algorithm[D]. Guangzhou: South China University of Technology, 2010. [30] 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. Cong Ziqi.Research on load of TD-LTE base station based on data mining[D]. Beijing: Beijing University of Posts and Telecommunications, 2018.