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Adaptive Active Disturbance Rejection Load Frequency Control for Power System with Renewable Energies Using the Lyapunov Reward-Based Twin Delayed Deep Deterministic Policy Gradient Algorithm

Author

Listed:
  • Yuemin Zheng

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Jin Tao

    (Silo AI, 00100 Helsinki, Finland)

  • Qinglin Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Hao Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Zengqiang Chen

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Mingwei Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

Abstract

The substitution of renewable energy sources (RESs) for conventional fossil fuels in electricity generation is essential in addressing environmental pollution and resource depletion. However, the integration of RESs in the load frequency control (LFC) of power systems can have a negative impact on frequency deviation response, resulting in a decline in power quality. Moreover, load disturbances can also affect the stability of frequency deviation. Hence, this paper presents an LFC method that utilizes the Lyapunov reward-based twin delayed deep deterministic policy gradient (LTD3) algorithm to optimize the linear active disturbance rejection control (LADRC). With the advantages of being model-free and mitigating unknown disturbances, LADRC can regulate load disturbances and renewable energy deviations. Additionally, the LTD3 algorithm, based on the Lyapunov reward function, is employed to optimize controller parameters in real-time, resulting in enhanced control performance. Finally, the LADRC-LTD3 is evaluated using a power system containing two areas, comprising thermal, hydro, and gas power plants in each area, as well as RESs such as a noise-based wind turbine and photovoltaic (PV) system. A comparative analysis is conducted between the performance of the proposed controller and other control techniques, such as integral controller (IC), fractional-order proportional integral derivative (FOPID) controller, I-TD, ID-T, and TD3-optimized LADRC. The results indicate that the proposed method effectively addresses the LFC problem.

Suggested Citation

  • Yuemin Zheng & Jin Tao & Qinglin Sun & Hao Sun & Zengqiang Chen & Mingwei Sun, 2023. "Adaptive Active Disturbance Rejection Load Frequency Control for Power System with Renewable Energies Using the Lyapunov Reward-Based Twin Delayed Deep Deterministic Policy Gradient Algorithm," Sustainability, MDPI, vol. 15(19), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14452-:d:1253033
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    References listed on IDEAS

    as
    1. Zhou, Ping & Hu, Xikui & Zhu, Zhigang & Ma, Jun, 2021. "What is the most suitable Lyapunov function?," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    2. Yuemin Zheng & Jin Tao & Qinglin Sun & Hao Sun & Zengqiang Chen & Mingwei Sun & Feng Duan, 2022. "Deep-Reinforcement-Learning-Based Active Disturbance Rejection Control for Lateral Path Following of Parafoil System," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    3. Ruisheng Wang & Zhong Chen & Qiang Xing & Ziqi Zhang & Tian Zhang, 2022. "A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station," Sustainability, MDPI, vol. 14(3), pages 1-14, February.
    4. Huang, Ruchen & He, Hongwen & Zhao, Xuyang & Wang, Yunlong & Li, Menglin, 2022. "Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm," Applied Energy, Elsevier, vol. 321(C).
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