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Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems

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  • Lu, Quan
  • Huang, Wenxuan
  • Yin, Linfei

Abstract

The number of distributed renewable energy sources of integrated energy systems (IESs) are increasing. The volatility of renewable energy exacerbates active power imbalances and increase frequency deviations in power systems. This work proposes a decomposition prediction fractional-order active disturbance rejection control deep Q network (DPFOADRCDQN) to control each generator of IESs more accurately and to reduce systemic frequency fluctuation. According to the fluctuation size of frequency deviation, the sub-signal obtained after the prediction of the modal decomposition of frequency deviation is classified into big fluctuating signals and small fluctuating signals; fractional-order active disturbance rejection control (FOADRC) controls small fluctuating signals; deep Q network (DQN) controls big fluctuating signals. Frequency deviation signals in complex power systems with a large percentage of renewable energy have higher smoothness after modal decomposition, FOADRC can efficiently follow the small fluctuating signals, and DQN can learn the rich data features in the big fluctuating signals and give precise control commands. The DPFOADRCDQN, proportional-integral-derivative, Q-learning, fuzzy control, and the previous state-of-the-art reinforcement learning are simulated in IESs where the renewable energy generators output is 0 %, 20 %, 40 %, 65 %, and 80 % of all generators output, the results show the average frequency deviation, total generation cost, and carbon emission cost are at least 42.7 %, 0.92 %, and 0.93 % smaller than the comparison algorithm, respectively.

Suggested Citation

  • Lu, Quan & Huang, Wenxuan & Yin, Linfei, 2025. "Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems," Applied Energy, Elsevier, vol. 377(PD).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924021561
    DOI: 10.1016/j.apenergy.2024.124773
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    References listed on IDEAS

    as
    1. Xiong, Houbo & Zhou, Yue & Guo, Chuangxin & Ding, Yi & Luo, Fengji, 2023. "Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method," Applied Energy, Elsevier, vol. 350(C).
    2. Li, Jiawen & Zhou, Tao & Keke, He & Yu, Hengwen & Du, Hongwei & Liu, Shuangyu & Cui, Haoyang, 2023. "Distributed quantum multiagent deep meta reinforcement learning for area autonomy energy management of a multiarea microgrid," Applied Energy, Elsevier, vol. 343(C).
    3. Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
    4. Ding, Yunfei & Chen, Zijun & Zhang, Hongwei & Wang, Xin & Guo, Ying, 2022. "A short-term wind power prediction model based on CEEMD and WOA-KELM," Renewable Energy, Elsevier, vol. 189(C), pages 188-198.
    5. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    6. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    7. Bazdar, Elaheh & Sameti, Mohammad & Nasiri, Fuzhan & Haghighat, Fariborz, 2022. "Compressed air energy storage in integrated energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Yin, Linfei & Zhang, Bin, 2023. "Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems," Applied Energy, Elsevier, vol. 330(PA).
    9. Li, Jiawen & Zhou, Tao, 2023. "Active fault-tolerant coordination energy management for a proton exchange membrane fuel cell using curriculum-based multiagent deep meta-reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    10. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).
    11. Li, Qingyang & Wang, Guosong & Wu, Xinrong & Gao, Zhigang & Dan, Bo, 2024. "Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN," Energy, Elsevier, vol. 299(C).
    12. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    13. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).
    14. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    15. Zhong, Jin & Bollen, Math & Rönnberg, Sarah, 2021. "Towards a 100% renewable energy electricity generation system in Sweden," Renewable Energy, Elsevier, vol. 171(C), pages 812-824.
    16. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    17. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    18. Leng, Ya-Jun & Li, Xiao-Shuang & Zhang, Huan, 2024. "NSGA-T: A novel evaluation method for renewable energy plans," Energy, Elsevier, vol. 290(C).
    19. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    Full references (including those not matched with items on IDEAS)

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