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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
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    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.
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    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. Robert Małkowski & Janusz Nieznański, 2020. "Underfrequency Load Shedding: An Innovative Algorithm Based on Fuzzy Logic," Energies, MDPI, vol. 13(6), pages 1-16, March.
    3. 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.
    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).

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