IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i8p630-d75758.html
   My bibliography  Save this article

Coordinated Scheme of Under-Frequency Load Shedding with Intelligent Appliances in a Cyber Physical Power System

Author

Listed:
  • Qi Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Feng Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Mengya Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yang Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Ming Ni

    (State Grid Electric Power Research Institute, Nanjing 210003, Jiangsu, China)

Abstract

The construction of a cyber physical system in a power grid provides more potential control strategies for the power grid. With the rapid employment of intelligent terminal equipment (e.g., smart meters and intelligent appliances) in the environment of a smart grid, abundant dynamic response information could be introduced to support a secure and stable power system. Combining demand response technology with the traditional under-frequency load shedding (UFLS) scheme, a new UFLS strategy-determining method involving intelligent appliances is put forward to achieve the coordinated control of quick response resources and the traditional control resources. Based on this method, intelligent appliances can be used to meet the regulatory requirements of system operation in advance and prevent significant frequency drop, thereby improving the flexibility and stability of the system. Time-domain simulation verifies the effectiveness of the scheme, which is able to mitigate frequency drop and reduce the amount of load shedding.

Suggested Citation

  • Qi Wang & Yi Tang & Feng Li & Mengya Li & Yang Li & Ming Ni, 2016. "Coordinated Scheme of Under-Frequency Load Shedding with Intelligent Appliances in a Cyber Physical Power System," Energies, MDPI, vol. 9(8), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:630-:d:75758
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/8/630/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/8/630/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Spiros Livieratos & Vasiliki-Emmanouela Vogiatzaki & Panayotis G. Cottis, 2013. "A Generic Framework for the Evaluation of the Benefits Expected from the Smart Grid," Energies, MDPI, vol. 6(2), pages 1-21, February.
    2. Xuan Liu & Xingdong Liu & Zuyi Li, 2015. "Cyber Risk Assessment of Transmission Lines in Smart Grids," Energies, MDPI, vol. 8(12), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammad Dreidy & Hazlie Mokhlis & Saad Mekhilef, 2017. "Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation," Energies, MDPI, vol. 10(2), pages 1-24, January.
    2. Shun Li & Fei Tang & Youguo Shao & Qingfen Liao, 2017. "Adaptive Under-Frequency Load Shedding Scheme in System Integrated with High Wind Power Penetration: Impacts and Improvements," Energies, MDPI, vol. 10(9), pages 1-16, September.
    3. Robert Małkowski & Janusz Nieznański, 2020. "Underfrequency Load Shedding: An Innovative Algorithm Based on Fuzzy Logic," Energies, MDPI, vol. 13(6), pages 1-16, March.
    4. Skrjanc, T. & Mihalic, R. & Rudez, U., 2023. "A systematic literature review on under-frequency load shedding protection using clustering methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).

    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. Seong-Kyu Kim & Jun-Ho Huh, 2018. "A Study on the Improvement of Smart Grid Security Performance and Blockchain Smart Grid Perspective," Energies, MDPI, vol. 11(8), pages 1-22, July.
    2. Jinchao Li & Tianzhi Li & Liu Han, 2018. "Research on the Evaluation Model of a Smart Grid Development Level Based on Differentiation of Development Demand," Sustainability, MDPI, vol. 10(11), pages 1-25, November.
    3. Yi Liang & Yingying Fan & Yongfang Peng & Haigang An, 2022. "Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    4. Irene Muñoz-Benavente & Emilio Gómez-Lázaro & Tania García-Sánchez & Antonio Vigueras-Rodríguez & Angel Molina-García, 2017. "Implementation and Assessment of a Decentralized Load Frequency Control: Application to Power Systems with High Wind Energy Penetration," Energies, MDPI, vol. 10(2), pages 1-17, January.
    5. Jesús Rodríguez-Molina & Margarita Martínez-Núñez & José-Fernán Martínez & Waldo Pérez-Aguiar, 2014. "Business Models in the Smart Grid: Challenges, Opportunities and Proposals for Prosumer Profitability," Energies, MDPI, vol. 7(9), pages 1-30, September.
    6. Deniz Sun & Luis Olmos & Michel Rivier, 2020. "Considering Local Air Pollution in the Benefit Assessment and Cost Allocation of Cross Border Transmission Projects," Energies, MDPI, vol. 13(6), pages 1-20, March.
    7. Moretti, M. & Djomo, S. Njakou & Azadi, H. & May, K. & De Vos, K. & Van Passel, S. & Witters, N., 2017. "A systematic review of environmental and economic impacts of smart grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 888-898.
    8. Rafael Real-Calvo & Antonio Moreno-Munoz & Juan J. Gonzalez-De-La-Rosa & Victor Pallares-Lopez & Miguel J. Gonzalez-Redondo & Isabel M. Moreno-Garcia, 2016. "An Embedded System in Smart Inverters for Power Quality and Safety Functionality," Energies, MDPI, vol. 9(3), pages 1-25, March.

    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:9:y:2016:i:8:p:630-:d:75758. 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.