|
|
Research on Optimal Scheduling of 5G Base Station Energy Storage Aggregators Considering Communication Reliability |
Mao Anjia1, Zhang Lijing1,2, Sheng Qianqian1 |
1. School of Electrical and Electronic Engineering North China Electric Power University Beijing 102206 China; 2. Chengdong Power Supply Branch of State Grid Tianjin Electric Power Company Tianjin 300250 China |
|
|
Abstract The rapid development of 5G communication technologies have increased communication rates, reduced latency, and improved user's experience, while also brought the problem of increased energy consumption and high electricity costs for 5G operators. Therefore, using existing resources and technologies to reduce operating costs has become an urgent challenge for 5G operators. At the same time, to ensure communication reliability, 5G base stations are usually equipped with internal standby battery storage to continue powering the base station equipment during utility power outages and maintain normal operations of the base station. However, with the improvement of power supply reliability of the power grid, the standby energy storage of 5G base stations is always in a float charging state, and the utilization rate is not high, resulting in the waste of resources. As the number of 5G base stations is very large, their internal idle backup energy storage can be used as a flexible resource to participate in power grid dispatching, optimizing grid operation while obtaining certain auxiliary service revenue, providing a possibility for 5G operators to reduce operating costs, enabling effective interaction between the power system and the communication system, and achieving win-win cooperation between the two sides. Firstly, considering the dispersed location of individual 5G base stations, their standby energy storage capacity is small, which is difficult to attract the dispatching interest of the power grid. However, the number of 5G base stations is huge, and through a reasonable management mode, large-scale base station energy storage can provide certain power and energy support for the power system and optimize the power system operation. Therefore, this paper introduces base station standby energy storage aggregator (BSA), which will participate in grid dispatching after aggregating the dispatchable potential of each base station standby energy storage, and sign a reasonable dispatching agreement with communication base stations to obtain direct control of base station standby energy storage. The BSA obtains the power consumption of base stations based on the distribution of users and communication reliability within the coverage area, and reserves backup capacity for base stations based on the utility reliability and real-time power consumption of base stations, and determines whether the energy storage of each base station participates in power grid dispatching on the basis of ensuring the communication reliability of base stations. Secondly, based on the analysis of user communication reliability, utility power supply reliability and power consumption characteristics of base stations, the calculation method of standby capacity of base station energy storage is proposed, and based on this, an overall dispatchable potential assessment model of base station energy storage considering the characteristics of base station location distribution, user communication reliability, base station power supply reliability, and base station backup storage energy and power constraints is established. Thirdly considering the maximization of social benefits, and taking the standard deviation of grid load fluctuation and aggregator' revenue as optimization objectives, a multi-objective optimization model for the participation of 5G base station energy storage aggregators in grid dispatch is established, and the proposed model is solved by an improved multi-objective particle swarm algorithm. Finally, the paper designs an example considering the density of communication users to verify the proposed method, and analyzes the impact of different zoning transmitting power and utility power reliability on the dispatchable capacity of base station standby energy storage. The results show that under the premise of setting the backup capacity to ensure the reliability of base station power supply, the base station energy storage still has considerable dispatching potential on the whole, and its response to power grid dispatch can smooth out the load fluctuation of the grid and reduce the peak-to-valley difference to achieve the goal of peak shaving and valley filling.
|
Received: 28 January 2022
|
|
|
|
|
[1] 信息通信发展司. 5G应用扬帆行动计划(2021-2023年)[EB/OL]. [2021-7-12]. https://www.miit.gov.cn/jgsj/txs/wjfb/art/2021/art_ccee7f20deb248358e2f348596e087da.html. [2] 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. [3] 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. [4] Jahid A, Hossain M S, Monju M K H, et al. Techno-economic and energy efficiency analysis of optimal power supply solutions for green cellular base stations[J]. IEEE Access, 8: 43776-43795. [5] Leithon J, Lim T J, Sun Sumei.Cost-aware renewable energy management with application in cellular networks[J]. IEEE Transactions on Green Communications and Networking, 2018, 2(1): 316-326. [6] Piovesan N, Temesgene D A, Miozzo M, et al.Joint load control and energy sharing for autonomous operation of 5G mobile networks in micro-grids[J]. IEEE Access, 2019, 7: 31140-31150. [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): 5540-5551. 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. [8] 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. [9] 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. [10] 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. [11] 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. [12] 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. [13] 李建林, 李雅欣, 吕超, 等. 退役动力电池梯次利用关键技术及现状分析[J]. 电力系统自动化, 2020, 44(13): 172-183. Li Jianlin, Li Yaxin, Lü Chao, et al.Key technology and research status of cascaded utilization in decommissioned power battery[J]. Automation of Electric Power Systems, 2020, 44(13): 172-183. [14] 陈鹏. 弹性负荷资源聚合及调节潜力预测模型研究[D]. 北京: 华北电力大学, 2021. [15] Chang Kuochi, Chu Kaichun, Wang H C, et al.Energy saving technology of 5G base station based on internet of things collaborative control[J]. IEEE Access, 8: 32935-32946. [16] Cai Shijie, Che Yueling, Duan Lingjie, et al.Green 5G heterogeneous networks through dynamic small-cell operation[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(5): 1103-1115. [17] 李晴. 5G多用户超密集网络有效容量分析及资源优化研究[D]. 北京: 北京邮电大学, 2020. [18] 李卓然. 面向5G移动通信的入网节能优化设计研究[D]. 呼和浩特: 内蒙古大学, 2020. [19] 倪萌, 王蓓蓓, 朱红, 等. 能源互联背景下面向高弹性的多元融合配电网双层分布式优化调度方法研究[J]. 电工技术学报, 2022, 37(1): 208-219. Ni Meng, Wang Beibei, Zhu Hong, et al.Study of two-layer distributed optimal scheduling strategy for highly elastic multi-resource fusion distribution network in energy interconnection environment[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 208-219. [20] 叶畅, 曹侃, 丁凯, 等. 基于广义储能的多能源系统不确定优化调度策略[J]. 电工技术学报, 2021, 36(17): 3753-3764. Ye Chang, Cao Kan, Ding Kai, et al.Uncertain optimal dispatch strategy based on generalized energy storage for multi-energy system[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3753-3764. [21] 刁涵彬, 李培强, 王继飞, 等. 考虑电/热储能互补协调的综合能源系统优化调度[J]. 电工技术学报, 2020, 35(21): 4532-4543. Diao Hanbin, Li Peiqiang, Wang Jifei, et al.Optimal dispatch of integrated energy system considering complementary coordination of electric/thermal energy storage[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4532-4543. [22] 赵冬梅, 王浩翔, 陶然. 计及风电-负荷不确定性的风-火-核-碳捕集多源协调优化调度[J]. 电工技术学报, 2022, 37(3): 707-718. Zhao Dongmei, Wang Haoxiang, Tao Ran.A multi-source coordinated optimal scheduling model considering wind-load uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 707-718. [23] 常烜语. 基站设备的智能化发电调度系统设计与实现[D]. 成都: 电子科技大学, 2020. [24] 尹渠凯. 规模化分布式储能聚合建模及其协同优化调控策略研究[D]. 北京: 华北电力大学, 2019. [25] Liu Junhui, Wang Shiqian, Yang Qinchen, et al.Feasibility study of power demand response for 5G base station[C]//2021 IEEE International Conference on Power Electronics, Computer Applications, Shenyang, China, 2021: 1038-1041. |
|
|
|