IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p373-d1317614.html
   My bibliography  Save this article

Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed

Author

Listed:
  • Matthew Boeding

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Paul Scalise

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Michael Hempel

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Hamid Sharif

    (Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Juan Lopez

    (Office of Science and Engineering, Science and Technology Directorate, Department of Homeland Security, Washington, DC 20528, USA)

Abstract

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices’ contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol’s time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G.

Suggested Citation

  • Matthew Boeding & Paul Scalise & Michael Hempel & Hamid Sharif & Juan Lopez, 2024. "Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed," Energies, MDPI, vol. 17(2), pages 1-18, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:373-:d:1317614
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/373/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/2/373/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hui, Hongxun & Ding, Yi & Shi, Qingxin & Li, Fangxing & Song, Yonghua & Yan, Jinyue, 2020. "5G network-based Internet of Things for demand response in smart grid: A survey on application potential," Applied Energy, Elsevier, vol. 257(C).
    2. Preetha Thulasiraman & Michael Hackett & Preston Musgrave & Ashley Edmond & Jared Seville, 2023. "Anomaly Detection in a Smart Microgrid System Using Cyber-Analytics: A Case Study," Energies, MDPI, vol. 16(20), pages 1-25, October.
    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. Turki Alsuwian & Aiman Shahid Butt & Arslan Ahmed Amin, 2022. "Smart Grid Cyber Security Enhancement: Challenges and Solutions—A Review," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    2. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    3. Mehar Ullah & Daniel Gutierrez-Rojas & Eero Inkeri & Tero Tynjälä & Pedro H. J. Nardelli, 2022. "Operation of Power-to-X-Related Processes Based on Advanced Data-Driven Methods: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-17, October.
    4. Sun, Mingyi & Zhao, Xia & Tan, Hong & Li, Xinyi, 2022. "Coordinated operation of the integrated electricity-water distribution system and water-cooled 5G base stations," Energy, Elsevier, vol. 238(PC).
    5. Xiao, Jucheng & He, Guangyu & Fan, Shuai & Zhang, Siyuan & Wu, Qing & Li, Zuyi, 2020. "Decentralized transfer of contingency reserve: Framework and methodology," Applied Energy, Elsevier, vol. 278(C).
    6. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    7. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    8. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    9. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    10. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    11. Lyu, Wenjing & Liu, Jin, 2021. "Soft skills, hard skills: What matters most? Evidence from job postings," Applied Energy, Elsevier, vol. 300(C).
    12. Zeng, Bo & Zhang, Weixiang & Hu, Pinduan & Sun, Jing & Gong, Dunwei, 2023. "Synergetic renewable generation allocation and 5G base station placement for decarbonizing development of power distribution system: A multi-objective interval evolutionary optimization approach," Applied Energy, Elsevier, vol. 351(C).
    13. Lilia Tightiz & Hyosik Yang & Mohammad Jalil Piran, 2020. "A Survey on Enhanced Smart Micro-Grid Management System with Modern Wireless Technology Contribution," Energies, MDPI, vol. 13(9), pages 1-21, May.
    14. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.
    15. Hui, Hongxun & Ding, Yi & Song, Yonghua, 2020. "Adaptive time-delay control of flexible loads in power systems facing accidental outages," Applied Energy, Elsevier, vol. 275(C).
    16. Cambini, Carlo & Congiu, Raffaele & Jamasb, Tooraj & Llorca, Manuel & Soroush, Golnoush, 2020. "Energy Systems Integration: Implications for public policy," Energy Policy, Elsevier, vol. 143(C).
    17. Stracqualursi, Erika & Rosato, Antonello & Di Lorenzo, Gianfranco & Panella, Massimo & Araneo, Rodolfo, 2023. "Systematic review of energy theft practices and autonomous detection through artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Chen, Lin & Wang, Jianxiao & Wu, Zhaoyuan & Li, Gengyin & Zhou, Ming & Li, Peng & Zhang, Yihan, 2021. "Communication reliability-restricted energy sharing strategy in active distribution networks," Applied Energy, Elsevier, vol. 282(PB).
    19. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
    20. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N. & Burmester, Daniel, 2021. "Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation," Applied Energy, Elsevier, vol. 287(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:gam:jeners:v:17:y:2024:i:2:p:373-:d:1317614. 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.