IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipds0306261924020889.html
   My bibliography  Save this article

Hierarchical regulation strategy based on dynamic clustering for economic optimization of large-scale 5G base stations

Author

Listed:
  • Mu, Yunfei
  • Jiang, Xinyang
  • Ma, Xiaoyan
  • Zhang, Jiarui
  • Jia, Hongjie
  • Jin, Xiaolong
  • Yao, Boren

Abstract

Utilizing the backup energy storage potential of 5G base stations (BSs) for economic regulation is an essential strategy to provide flexibility to the power grid and reduce operational costs. However, the dimensionality of the decision variables for centralized regulation of large-scale BSs is substantial, thereby increasing the computational complexity. Furthermore, the traditional clustering method, which could enhance solution speed, fails to account for the spatiotemporal dynamics of the regulation potential induced by the tidal effect and the sleep mechanism of 5G BSs. This limitation affects the accuracy of regulation and the utilization of the BSs' regulable potential. Therefore, a hierarchical regulation strategy based on dynamic clustering for economic optimization of large-scale 5G BSs is proposed, where BSs are regulated at two levels: cluster and individual. Focusing on the changes in 5G BSs' regulation potential, a dynamic clustering method based on K-means is proposed, which considers the regulable capacity and geographical location of BSs over time and space, thereby reducing the computational scale. The method accounts for changes in the regulable capacity to modify clusters and dynamically aggregates them for modeling. Furthermore, the clustering regulation economic optimization model and in-cluster power allocation control model are established respectively at the cluster and individual levels to solve the corresponding regulation schemes. Due to the interaction between the clustering and regulation in the overall strategy, the optimal clustering and regulation scheme are determined through the iteration of dynamic clustering, clustering regulation and in-cluster allocation. The simulation with 2916 BSs in a test area is conducted. The results show that the computation time of the proposed strategy is reduced to 2.34 % of the centralized regulation. The maximum error of the regulable capacity and regulation scheme decrease by 21.93 % and 9.32 %. It demonstrates that the proposed strategy enhances the speed of large-scale 5G BSs regulation while ensuring the accuracy of regulation and utilization of the regulable potential.

Suggested Citation

  • Mu, Yunfei & Jiang, Xinyang & Ma, Xiaoyan & Zhang, Jiarui & Jia, Hongjie & Jin, Xiaolong & Yao, Boren, 2025. "Hierarchical regulation strategy based on dynamic clustering for economic optimization of large-scale 5G base stations," Applied Energy, Elsevier, vol. 377(PD).
  • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020889
    DOI: 10.1016/j.apenergy.2024.124705
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020889
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.124705?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin, Wei & Jin, Xiaolong & Jia, Hongjie & Mu, Yunfei & Xu, Tao & Xu, Xiandong & Yu, Xiaodan, 2021. "Decentralized optimal scheduling for integrated community energy system via consensus-based alternating direction method of multipliers," Applied Energy, Elsevier, vol. 302(C).
    2. Mu, Yunfei & Chen, Wanqing & Yu, Xiaodan & Jia, Hongjie & Hou, Kai & Wang, Congshan & Meng, Xianjun, 2020. "A double-layer planning method for integrated community energy systems with varying energy conversion efficiencies," Applied Energy, Elsevier, vol. 279(C).
    3. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).
    4. Bao, Peng & Xu, Qingshan & Yang, Yongbiao & Nan, Yu & Wang, Yucui, 2024. "Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations," Applied Energy, Elsevier, vol. 358(C).
    5. Niu, Wen-jing & Luo, Tao & Yao, Xin-ru & Gong, Jin-tai & Huang, Qing-qing & Gao, Hao-yu & Feng, Zhong-kai, 2024. "Artificial intelligence-based response surface progressive optimality algorithm for operation optimization of multiple hydropower reservoirs," Energy, Elsevier, vol. 291(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong, Lei & Sun, Shiting & Zhang, Shiming & Zhang, Tao & Pu, Tianjiao, 2024. "Distributed restoration for integrated electricity-gas-heating energy systems with an iterative loop scheme," Energy, Elsevier, vol. 304(C).
    2. Zeli Ye & Wentao Huang & Jinfeng Huang & Jun He & Chengxi Li & Yan Feng, 2023. "Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models," Energies, MDPI, vol. 16(3), pages 1-22, January.
    3. Wang, Yongli & Guo, Lu & Wang, Yanan & Zhang, Yunfei & Zhang, Siwen & Liu, Zeqiang & Xing, Juntai & Liu, Ximei, 2024. "Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment," Energy, Elsevier, vol. 298(C).
    4. Zhang, Han & Han, Zhonghe & Wu, Di & Li, Peng & Li, Peng, 2023. "Energy optimization and performance analysis of a novel integrated energy system coupled with solar thermal unit and preheated organic cycle under extended following electric load strategy," Energy, Elsevier, vol. 272(C).
    5. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    6. Xiaozhi Gao & Han Xiao & Shiwei Xu & Hsiung-Cheng Lin & Pengyu Chang, 2024. "What Is the Optimal Solution for Scheduling Multiple Energy Systems? Overview and Analysis of Integrated Energy Co-Dispatch Models," Energies, MDPI, vol. 17(18), pages 1-25, September.
    7. Sun, Weijia & Wang, Qi & Ye, Yujian & Tang, Yi, 2022. "Unified modelling of gas and thermal inertia for integrated energy system and its application to multitype reserve procurement," Applied Energy, Elsevier, vol. 305(C).
    8. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    9. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    10. Zhao, Peiyao & Li, Zhengshuo & Bai, Xiang & Su, Jia & Chang, Xinyue, 2024. "Stochastic real-time dispatch considering AGC and electric-gas dynamic interaction: Fine-grained modeling and noniterative decentralized solutions," Applied Energy, Elsevier, vol. 375(C).
    11. Siqin, Zhuoya & Niu, DongXiao & Li, MingYu & Gao, Tian & Lu, Yifan & Xu, Xiaomin, 2022. "Distributionally robust dispatching of multi-community integrated energy system considering energy sharing and profit allocation," Applied Energy, Elsevier, vol. 321(C).
    12. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    13. Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).
    14. Wu, Min & Xu, Jiazhu & Zeng, Linjun & Li, Chang & Liu, Yuxing & Yi, Yuqin & Wen, Ming & Jiang, Zhuohan, 2022. "Two-stage robust optimization model for park integrated energy system based on dynamic programming," Applied Energy, Elsevier, vol. 308(C).
    15. Mohamed Ahmed Ali & Mohey Eldin Mandour & Mohammed Elsayed Lotfy, 2023. "Adaptive Estimation of Quasi-Empirical Proton Exchange Membrane Fuel Cell Models Based on Coot Bird Optimizer and Data Accumulation," Sustainability, MDPI, vol. 15(11), pages 1-20, June.
    16. Qibo He & Changming Chen & Xin Fu & Shunjiang Yu & Long Wang & Zhenzhi Lin, 2024. "Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information," Energies, MDPI, vol. 17(19), pages 1-20, September.
    17. Qin, Chun & Zhao, Jun & Chen, Long & Liu, Ying & Wang, Wei, 2022. "An adaptive piecewise linearized weighted directed graph for the modeling and operational optimization of integrated energy systems," Energy, Elsevier, vol. 244(PA).
    18. Meng, Anbo & Wu, Zhenbo & Zhang, Zhan & Xu, Xuancong & Tang, Yanshu & Xie, Zhifeng & Xian, Zikang & Zhang, Haitao & Luo, Jianqiang & Wang, Yu & Yan, Baiping & Yin, Hao, 2024. "Optimal scheduling of integrated energy system using decoupled distributed CSO with opposition-based learning and neighborhood re-dispatch strategy," Renewable Energy, Elsevier, vol. 224(C).
    19. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    20. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020889. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.