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Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

Author

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  • Xilin Zhao

    (Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Zhenyu Lin

    (Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Bo Fu

    (Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Li He

    (Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Na Fang

    (Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

Abstract

High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power system. An improved AGC method named predictive optimal 2-degree-of-freedom proportion integral differential (PO-2-DOF-PID) is proposed in this paper, which wind farm will participate in the load frequency control process. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of wind power is built. Then, predictive optimal 2-degree-of-freedom PID controller is presented to improve the system robustness considering system uncertainties. The objective function is designed based on the wind speed and whether wind farm takes part in AGC or not. The controller solves the optimization problem through the predictive theory while taking into account given constraints. In order to obtain the predictive sequence of output of the whole system, the characteristic of the 2-DOF-PID controller is integrated in the system model. A three interconnected power system is introduced as an example to test the feasibility and effectiveness of the proposed method. When considering the penetration of wind power, two cases of high wind speed and low wind speed are analyzed. The simulation results indicate that the proposed method can effectively deal with the negative influence caused by wind power when wind power participates in AGC.

Suggested Citation

  • Xilin Zhao & Zhenyu Lin & Bo Fu & Li He & Na Fang, 2018. "Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System," Energies, MDPI, vol. 11(12), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3325-:d:186236
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    References listed on IDEAS

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    1. Wickramasinghe, Amila & Perera, Sarath & Agalgaonkar, Ashish P. & Meegahapola, Lasantha, 2016. "Synchronous mode operation of DFIG based wind turbines for improvement of power system inertia," Renewable Energy, Elsevier, vol. 95(C), pages 152-161.
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    3. Dreidy, Mohammad & Mokhlis, H. & Mekhilef, Saad, 2017. "Inertia response and frequency control techniques for renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 144-155.
    4. Mehdi Tavakkoli & Jafar Adabi & Sasan Zabihi & Radu Godina & Edris Pouresmaeil, 2018. "Reserve Allocation of Photovoltaic Systems to Improve Frequency Stability in Hybrid Power Systems," Energies, MDPI, vol. 11(10), pages 1-19, September.
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    1. Hao Ma & Jian Pan & Lei Lv & Guanghui Xu & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2019. "Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems," Mathematics, MDPI, vol. 7(6), pages 1-18, June.
    2. Jun Deng & Jun Suo & Jing Yang & Shutao Peng & Fangde Chi & Tong Wang, 2019. "Adaptive Damping Control Strategy of Wind Integrated Power System," Energies, MDPI, vol. 12(1), pages 1-18, January.
    3. Junxia Ma & Qiuling Fei & Fan Guo & Weili Xiong, 2019. "Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems," Mathematics, MDPI, vol. 7(12), pages 1-16, November.
    4. Ali Dokht Shakibjoo & Mohammad Moradzadeh & Seyed Zeinolabedin Moussavi & Lieven Vandevelde, 2020. "A Novel Technique for Load Frequency Control of Multi-Area Power Systems," Energies, MDPI, vol. 13(9), pages 1-19, April.
    5. Lijuan Wan & Ximei Liu & Feng Ding & Chunping Chen, 2019. "Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems," Mathematics, MDPI, vol. 7(7), pages 1-20, July.
    6. Kaleem Ullah & Abdul Basit & Zahid Ullah & Sheraz Aslam & Herodotos Herodotou, 2021. "Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview," Energies, MDPI, vol. 14(9), pages 1-43, April.

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