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Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Author

Listed:
  • Yu-Qing Bao

    (School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210042, China
    School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yang Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Beibei Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Minqiang Hu

    (School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210042, China
    These authors contributed equally to this work.)

  • Yanmin Zhou

    (School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210042, China
    These authors contributed equally to this work.)

Abstract

The development of advanced metering technologies makes demand response (DR) able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP). Finally, the proposed method is verified on numerical examples.

Suggested Citation

  • Yu-Qing Bao & Yang Li & Beibei Wang & Minqiang Hu & Yanmin Zhou, 2017. "Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource," Energies, MDPI, vol. 10(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:82-:d:87511
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    Citations

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    Cited by:

    1. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    2. Muhammad Saeed Uz Zaman & Syed Basit Ali Bukhari & Khalid Mousa Hazazi & Zunaib Maqsood Haider & Raza Haider & Chul-Hwan Kim, 2018. "Frequency Response Analysis of a Single-Area Power System with a Modified LFC Model Considering Demand Response and Virtual Inertia," Energies, MDPI, vol. 11(4), pages 1-20, March.
    3. Hsin-Wei Chiu & Le-Ren Chang-Chien & Chin-Chung Wu, 2021. "Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique," Energies, MDPI, vol. 14(2), pages 1-19, January.
    4. Qingshan Xu & Yifan Ding & Aixia Zheng, 2017. "An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability," Sustainability, MDPI, vol. 9(5), pages 1-20, May.

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