IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i6p2422-d1357068.html
   My bibliography  Save this article

Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station

Author

Listed:
  • Jingang Han

    (Institute of Electric Drives and Control Systems, Shanghai Maritime University, Shanghai 201306, China)

  • Shiwei Lin

    (Institute of Electric Drives and Control Systems, Shanghai Maritime University, Shanghai 201306, China)

  • Boyu Pu

    (Shanghai Keenwoo More Electric Technology Co., Ltd., Shanghai 201306, China)

Abstract

For 5G base stations equipped with multiple energy sources, such as energy storage systems (ESSs) and photovoltaic (PV) power generation, energy management is crucial, directly influencing the operational cost. Hence, aiming at increasing the utilization rate of PV power generation and improving the lifetime of the battery, thereby reducing the operating cost of the base station, a hierarchical energy management strategy based on the improved dung beetle optimization (IDBO) algorithm is proposed in this paper. The first control layer provides bus voltage control to each power module. In the second control layer, a dynamic balance control strategy calculates the power of the ESSs using the proportional–integral (PI) controller and distributes power based on the state of charge (SOC) and virtual resistance. The third control layer uses the IDBO algorithm to solve the DC microgrid’s optimization model in order to achieve the minimum daily operational cost goal. Simulation results demonstrate that the proposed IDBO algorithm reduces the daily cost in both scenarios by about 14.64% and 9.49% compared to the baseline method. Finally, the feasibility and effectiveness of the proposed hierarchical energy management strategy are verified through experimental results.

Suggested Citation

  • Jingang Han & Shiwei Lin & Boyu Pu, 2024. "Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station," Sustainability, MDPI, vol. 16(6), pages 1-19, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2422-:d:1357068
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/6/2422/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/6/2422/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ferahtia, Seydali & Rezk, Hegazy & Olabi, A.G. & Alhumade, Hesham & Bamufleh, Hisham S. & Doranehgard, Mohammad Hossein & Abdelkareem, Mohammad Ali, 2022. "Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications," Applied Energy, Elsevier, vol. 327(C).
    2. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    3. Jani, Ali & Jadid, Shahram, 2023. "Two-stage energy scheduling framework for multi-microgrid system in market environment," Applied Energy, Elsevier, vol. 336(C).
    4. Ziad M. Ali & Martin Calasan & Shady H. E. Abdel Aleem & Francisco Jurado & Foad H. Gandoman, 2023. "Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review," Energies, MDPI, vol. 16(16), pages 1-41, August.
    5. Wang, Shuoqi & Lu, Languang & Han, Xuebing & Ouyang, Minggao & Feng, Xuning, 2020. "Virtual-battery based droop control and energy storage system size optimization of a DC microgrid for electric vehicle fast charging station," Applied Energy, Elsevier, vol. 259(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. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    2. Marek Krok & Paweł Majewski & Wojciech P. Hunek & Tomasz Feliks, 2022. "Energy Optimization of the Continuous-Time Perfect Control Algorithm," Energies, MDPI, vol. 15(4), pages 1-13, February.
    3. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    4. Sijia Li & Arman Oshnoei & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2023. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    5. Awol Seid Ebrie & Chunhyun Paik & Yongjoo Chung & Young Jin Kim, 2023. "Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning," Energies, MDPI, vol. 16(16), pages 1-12, August.
    6. Lingling Hu & Junming Zhou & Feng Jiang & Guangming Xie & Jie Hu & Qinglie Mo, 2023. "Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization," Sustainability, MDPI, vol. 16(1), pages 1-25, December.
    7. Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    8. Irina Picioroaga & Madalina Luca & Andrei Tudose & Dorian Sidea & Mircea Eremia & Constantin Bulac, 2023. "Resilience-Driven Optimal Sizing of Energy Storage Systems in Remote Microgrids," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    9. Abdul Rauf & Mahmoud Kassas & Muhammad Khalid, 2022. "Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    10. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    11. Suroso Isnandar & Jonathan F. Simorangkir & Kevin M. Banjar-Nahor & Hendry Timotiyas Paradongan & Nanang Hariyanto, 2024. "A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition," Energies, MDPI, vol. 17(15), pages 1-28, August.
    12. Wang, Shuoqi & Guo, Dongxu & Han, Xuebing & Lu, Languang & Sun, Kai & Li, Weihan & Sauer, Dirk Uwe & Ouyang, Minggao, 2020. "Impact of battery degradation models on energy management of a grid-connected DC microgrid," Energy, Elsevier, vol. 207(C).
    13. Förster, Robert & Kaiser, Matthias & Wenninger, Simon, 2023. "Future vehicle energy supply - sustainable design and operation of hybrid hydrogen and electric microgrids," Applied Energy, Elsevier, vol. 334(C).
    14. Lei Zhang & Rui Tang, 2023. "Dispatch for a Continuous-Time Microgrid Based on a Modified Differential Evolution Algorithm," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    15. Fahad R. Albogamy & Ghulam Hafeez & Imran Khan & Sheraz Khan & Hend I. Alkhammash & Faheem Ali & Gul Rukh, 2021. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    16. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    17. Zhao, Shihao & Li, Kang & Yang, Zhile & Xu, Xinzhi & Zhang, Ning, 2022. "A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies," Applied Energy, Elsevier, vol. 314(C).
    18. Mirzaei, Farzad & Mahdavi, Sadegh & Bayat, Alireza, 2019. "Economic Operation of Unit Commitment Using Multiverse Optimization Algorithm," MPRA Paper 95894, University Library of Munich, Germany.
    19. Masoumi, A.P. & Tavakolpour-Saleh, A.R. & Rahideh, A., 2020. "Applying a genetic-fuzzy control scheme to an active free piston Stirling engine: Design and experiment," Applied Energy, Elsevier, vol. 268(C).
    20. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.

    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:gam:jsusta:v:16:y:2024:i:6:p:2422-:d:1357068. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.