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Load Frequency Control of Multi-Region Interconnected Power Systems with Wind Power and Electric Vehicles Based on Sliding Mode Control

Author

Listed:
  • Zhenghao Wang

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Yonghui Liu

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Zihao Yang

    (College of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Wanhao Yang

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

Abstract

In recent years, wind power systems have been used extensively, which not only improve the efficiency of current conventional power generation systems, but also can save traditional fossil fuel resources. However, considering the instability of wind power, after being grid connected, it can easily cause an impact on the stability of the grid operation. Considering the above problems, this paper considers to make full use of the energy storage part of electric vehicles (EVs) to increase the stability of grid operation. Based on the mathematical model, this paper studies the load frequency control (LFC) problem of a multi-region interconnected power system with wind power and EVs. First, since the system states are difficult to be monitored, a state observer is designed to estimate the state. Based on this, the integral sliding mode controller (SMC) is designed to realize the LFC of the interconnected power system. Meanwhile, to obtain better control performance, this paper further analyzes and optimizes the controller parameters based on Lyapunov stability theory. At last, simulations are carried out for the power systems with two regions in Simulink. The results show that the designed controllers are effective to compensate the load demand disturbances. In addition, it is demonstrated that the battery storage of EVs can play the role of peak-shaving and valley-filling in LFC.

Suggested Citation

  • Zhenghao Wang & Yonghui Liu & Zihao Yang & Wanhao Yang, 2021. "Load Frequency Control of Multi-Region Interconnected Power Systems with Wind Power and Electric Vehicles Based on Sliding Mode Control," Energies, MDPI, vol. 14(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2288-:d:538895
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    References listed on IDEAS

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    1. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    2. Ourahou, M. & Ayrir, W. & EL Hassouni, B. & Haddi, A., 2020. "Review on smart grid control and reliability in presence of renewable energies: Challenges and prospects," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 19-31.
    3. Shang-Guan, Xingchen & He, Yong & Zhang, Chuanke & Jiang, Lin & Spencer, Joseph William & Wu, Min, 2020. "Sampled-data based discrete and fast load frequency control for power systems with wind power," Applied Energy, Elsevier, vol. 259(C).
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    Cited by:

    1. Xinghua Liu & Siwei Qiao & Zhiwei Liu, 2023. "A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies," Energies, MDPI, vol. 16(5), pages 1-22, February.
    2. Bashar Abbas Fadheel & Noor Izzri Abdul Wahab & Ali Jafer Mahdi & Manoharan Premkumar & Mohd Amran Bin Mohd Radzi & Azura Binti Che Soh & Veerapandiyan Veerasamy & Andrew Xavier Raj Irudayaraj, 2023. "A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System," Energies, MDPI, vol. 16(3), pages 1-28, January.
    3. Adlan Pradana & Mejbaul Haque & Mithulanathan Nadarajah, 2023. "Control Strategies of Electric Vehicles Participating in Ancillary Services: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-36, February.
    4. Yixuan Ge & Guobao Liu & Guishu Zhao & Huai Liu & Ji Sun, 2022. "Observer-Based H ∞ Load Frequency Control for Networked Power Systems with Limited Communications and Probabilistic Cyber Attacks," Energies, MDPI, vol. 15(12), pages 1-16, June.

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