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Power Smoothing Strategy for Wind Generation Based on Fuzzy Control Strategy with Battery Energy Storage System

Author

Listed:
  • Pablo L. Tabosa da Silva

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Pedro A. Carvalho Rosas

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • José F. C. Castro

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Davidson da Costa Marques

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Ronaldo R. B. Aquino

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Guilherme F. Rissi

    (CPFL Energy, Campinas 13087-397, SP, Brazil)

  • Rafael C. Neto

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

  • Douglas C. P. Barbosa

    (Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil)

Abstract

This work discusses the use of a battery energy storage system applied to the smoothing of power generated at the output of wind turbines based on a fuzzy logic power control. The fuzzy control logic proposed can perform the aforementioned activity while the state of charge of the energy storage system is maintained within operational limits. In order to assess the fuzzy logic power control’s effectiveness at maintaining the state of charge levels within the allowed range, two operating situations are explored: one in which the state of charge is above the upper limit allowed, and another in which the state of charge is in the minimum value allowed. The numerical results show that, when using the battery energy storage system in conjunction with the control logic proposed, the active power provided as the point of common coupling by the wind turbines can be smoothed, thus contributing to the Electric Power System reliability and stability. The main results of this paper are based on measurements of wind and active power associated with a wind generation plant installed in the northeast region of Brazil and equipped with 2.1 MW wind turbines.

Suggested Citation

  • Pablo L. Tabosa da Silva & Pedro A. Carvalho Rosas & José F. C. Castro & Davidson da Costa Marques & Ronaldo R. B. Aquino & Guilherme F. Rissi & Rafael C. Neto & Douglas C. P. Barbosa, 2023. "Power Smoothing Strategy for Wind Generation Based on Fuzzy Control Strategy with Battery Energy Storage System," Energies, MDPI, vol. 16(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6017-:d:1218815
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    References listed on IDEAS

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    1. Luana Pontes & Tatiane Costa & Amanda Souza & Nicolau Dantas & Andrea Vasconcelos & Guilherme Rissi & Roberto Dias & Mohamed A. Mohamed & Pierluigi Siano & Manoel Marinho, 2023. "Operational Data Analysis of a Battery Energy Storage System to Support Wind Energy Generation," Energies, MDPI, vol. 16(3), pages 1-20, February.
    2. Jun Deng & Zhenghao Qi & Nan Xia & Tong Gao & Yang Zhang & Jiandong Duan, 2022. "Control Strategy and Parameter Optimization Based on Grid Side Current Dynamic Change Rate for Doubly-Fed Wind Turbine High Voltage Ride Through," Energies, MDPI, vol. 15(21), pages 1-19, October.
    3. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    4. Ines Würth & Laura Valldecabres & Elliot Simon & Corinna Möhrlen & Bahri Uzunoğlu & Ciaran Gilbert & Gregor Giebel & David Schlipf & Anton Kaifel, 2019. "Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36," Energies, MDPI, vol. 12(4), pages 1-30, February.
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    Cited by:

    1. Zhe Chai & Yihan Zhang & Lanyi Wei & Junhui Liu & Yao Lu & Chunzheng Tian & Zhaoyuan Wu, 2024. "Value Evaluation Model of Multi-Temporal Energy Storage for Flexibility Provision in Microgrids," Energies, MDPI, vol. 17(9), pages 1-16, April.
    2. Henok Ayele Behabtu & Majid Vafaeipour & Abraham Alem Kebede & Maitane Berecibar & Joeri Van Mierlo & Kinde Anlay Fante & Maarten Messagie & Thierry Coosemans, 2023. "Smoothing Intermittent Output Power in Grid-Connected Doubly Fed Induction Generator Wind Turbines with Li-Ion Batteries," Energies, MDPI, vol. 16(22), pages 1-37, November.

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