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Research on Hierarchical Control Strategy of ESS in Distribution Based on GA-SVR Wind Power Forecasting

Author

Listed:
  • Linlin Yu

    (Economic and Technological Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450015, China)

  • Gaojun Meng

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211100, China)

  • Giovanni Pau

    (Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy)

  • Yao Wu

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211100, China)

  • Yun Tang

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211100, China)

Abstract

In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is facing severe challenges. Aiming at the irregular fluctuation of wind power output and the restriction between the charge and discharge depth and service life of hybrid energy storage equipment, a hierarchical control strategy for a hybrid energy storage system based on improved GA-SVR wind power prediction is proposed. First of all, the short-term prediction of wind power output is carried out using Support Vector Regression (SVR), and the improved genetic algorithm is used for optimization. Then, the result obtained from the prediction calculation is used as the wind power output, and the internal initial power of each energy storage element is obtained through the hybrid energy storage capacity configuration method and further controlled through hierarchical control regulation. Finally, a simulation experiment is carried out on the proposed control strategy. The simulation algorithm shows that the proposed method can not only enhance the effective output of new energy but also extend the service life of energy storage and ensure the safe and stable operation of the power system.

Suggested Citation

  • Linlin Yu & Gaojun Meng & Giovanni Pau & Yao Wu & Yun Tang, 2023. "Research on Hierarchical Control Strategy of ESS in Distribution Based on GA-SVR Wind Power Forecasting," Energies, MDPI, vol. 16(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2079-:d:1074919
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    Citations

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    Cited by:

    1. Wiem Fekih Hassen & Maher Challouf, 2024. "Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model," Energies, MDPI, vol. 17(13), pages 1-19, June.
    2. Huang, Hao-Hsuan & Huang, Yun-Hsun, 2024. "Applying green learning to regional wind power prediction and fluctuation risk assessment," Energy, Elsevier, vol. 295(C).
    3. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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