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Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression

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  • Dongsen Li

    (Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
    School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Kang Qian

    (Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China)

  • Ciwei Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yiyue Xu

    (Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China)

  • Qiang Xing

    (School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

  • Zhangfan Wang

    (Department of Integrated Energy Engineering, China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China)

Abstract

Due to real-time fluctuations in wind farm output, large-scale renewable energy (RE) generation poses significant challenges to power system stability. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based electric hydrogen hybrid storage (EHHS) strategy to mitigate wind power fluctuations (WPFs). First, a wavelet packet power decomposition algorithm based on variable frequency entropy improvement is proposed. This algorithm characterizes the energy characteristics of the original wind power in different frequency bands. Second, to minimize WPF and the comprehensive operating cost of EHHS, an optimization model for suppressing wind power in the integrated power and hydrogen system (IPHS) is constructed. Next, considering the real-time and stochastic characteristics of wind power, the wind power smoothing model is transformed into a Markov decision process. A modified proximal policy optimization (MPPO) based on wind power deviation is proposed for training and solving. Based on the DRL agent’s real-time perception of wind power energy characteristics and the IPHS operation status, a WPF smoothing strategy is formulated. Finally, a numerical analysis based on a specific wind farm is conducted. The simulation results based on MATLAB R2021b show that the proposed strategy effectively suppresses WPF and demonstrates excellent convergence stability. The comprehensive performance of the MPPO is improved by 21.25% compared with the proximal policy optimization (PPO) and 42.52% compared with MPPO.

Suggested Citation

  • Dongsen Li & Kang Qian & Ciwei Gao & Yiyue Xu & Qiang Xing & Zhangfan Wang, 2024. "Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression," Energies, MDPI, vol. 17(20), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5019-:d:1495256
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    References listed on IDEAS

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    1. Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
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    4. Chen, Peng & Han, Dezhi, 2023. "Reward adaptive wind power tracking control based on deep deterministic policy gradient," Applied Energy, Elsevier, vol. 348(C).
    5. Andrea Mannelli & Francesco Papi & George Pechlivanoglou & Giovanni Ferrara & Alessandro Bianchini, 2021. "Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries," Energies, MDPI, vol. 14(8), pages 1-32, April.
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    Cited by:

    1. Daniel Fodorean, 2024. "Technological Trends for Electrical Machines and Drives Used in Small Wind Power Plants—A Review," Energies, MDPI, vol. 17(24), pages 1-28, December.

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