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A Power Smoothing Control Strategy and Optimized Allocation of Battery Capacity Based on Hybrid Storage Energy Technology

Author

Listed:
  • Xiaojuan Han

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Fang Chen

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiwang Cui

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Yong Li

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiangjun Li

    (Institute of Electrical Engineering, China Electric Power Research Institute, Beijing 100085, China)

Abstract

Wind power parallel operation is an effective way to realize the large scale use of wind power, but the fluctuations of power output from wind power units may have great influence on power quality, hence a new method of power smoothing and capacity optimized allocation based on hybrid energy storage technology is proposed in terms of the uncontrollable and unexpected characteristics of wind speed in wind farms. First, power smoothing based on a traditional Inertial Filter is introduced and the relationship between the time constant, its smoothing effect and capacity allocation are analyzed and combined with Proportional Integral Differential (PID) control to realize power smoothing control of wind power. Then wavelet theory is adopted to realize a multi-layer decomposition of power output in some wind farms, a power smoothing model based on hybrid energy storage technology is constructed combining the characteristics of the Super Capacitor (SC) and Battery Energy Storage System (BESS) technologies. The hybrid energy storage system is available for power fluctuations with high frequency-low energy and low frequency-high energy to achieve good smoothing effects compared with a single energy storage system. The power fluctuations filtered by the Wavelet Transform is regarded as the target value of BESS, the charging and discharging control for battery is completed quickly by Model Algorithm Control (MAC). Because of the influence of the inertia and the response speed of the battery, its actual output is not completely equal to the target value which mainly reflects in high-frequency part, the difference part uses SC to compensate and makes the output of battery and SC closer to the target value on the whole. Compared with the traditional Inertial Filter and PID control method, the validity of the model was verified by simulation results. Finally under the premise of power grid standards, the corresponding capacity design had been given to reduce the size of the energy storage devices as far as possible, which has a certain practical engineering value.

Suggested Citation

  • Xiaojuan Han & Fang Chen & Xiwang Cui & Yong Li & Xiangjun Li, 2012. "A Power Smoothing Control Strategy and Optimized Allocation of Battery Capacity Based on Hybrid Storage Energy Technology," Energies, MDPI, vol. 5(5), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:5:p:1593-1612:d:17858
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    Citations

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    Cited by:

    1. Fan Wu & Jun Wang & Zhang Sun & Tao Wang & Lei Chen & Xiaoyan Han, 2019. "An Optimal Wavelet Packets Basis Method for Cascade Hydro-PV-Pumped Storage Generation Systems to Smooth Photovoltaic Power Fluctuations," Energies, MDPI, vol. 12(24), pages 1-22, December.
    2. Lamsal, Dipesh & Sreeram, Victor & Mishra, Yateendra & Kumar, Deepak, 2019. "Output power smoothing control approaches for wind and photovoltaic generation systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. Javier Marcos & Iñigo De la Parra & Miguel García & Luis Marroyo, 2014. "Control Strategies to Smooth Short-Term Power Fluctuations in Large Photovoltaic Plants Using Battery Storage Systems," Energies, MDPI, vol. 7(10), pages 1-27, October.
    4. Jae Woong Shim & Heejin Kim & Kyeon Hur, 2019. "Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency," Energies, MDPI, vol. 12(7), pages 1-13, March.
    5. Masaki, Mukalu Sandro & Zhang, Lijun & Xia, Xiaohua, 2019. "A hierarchical predictive control for supercapacitor-retrofitted grid-connected hybrid renewable systems," Applied Energy, Elsevier, vol. 242(C), pages 393-402.
    6. Francisco Jesús Guillén-Arenas & José Fernández-Ramos & Luis Narvarte, 2022. "A New Strategy for PI Tuning in Photovoltaic Irrigation Systems Based on Simulation of System Voltage Fluctuations Due to Passing Clouds," Energies, MDPI, vol. 15(19), pages 1-20, September.
    7. Yi-Zeng Hsieh & Shih-Syun Lin & En-Yu Chang & Kwong-Kau Tiong & Shih-Wei Tan & Chiou-Yi Hor & Shyi-Chy Cheng & Yu-Shiuan Tsai & Chao-Rong Chen, 2021. "Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based Optimization Algorithm," Energies, MDPI, vol. 14(14), pages 1-17, July.
    8. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).

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