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Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization – Long Short-Term Memory Model

Author

Listed:
  • Xiaoguang Hao

    (State Grid Hebei Energy Technology Service Co., Ltd., Shijiazhuang 050081, China)

  • Chunlai Yang

    (State Grid Hebei Energy Technology Service Co., Ltd., Shijiazhuang 050081, China)

  • Heng Chen

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Jianning Dong

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Jiandong Bao

    (State Grid Hebei Energy Technology Service Co., Ltd., Shijiazhuang 050081, China)

  • Hui Wang

    (State Grid Hebei Energy Technology Service Co., Ltd., Shijiazhuang 050081, China)

  • Wenbin Zhang

    (State Grid Hebei Energy Technology Service Co., Ltd., Shijiazhuang 050081, China)

Abstract

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.

Suggested Citation

  • Xiaoguang Hao & Chunlai Yang & Heng Chen & Jianning Dong & Jiandong Bao & Hui Wang & Wenbin Zhang, 2024. "Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization – Long Short-Term Memory Model," Energies, MDPI, vol. 17(11), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2668-:d:1405810
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    References listed on IDEAS

    as
    1. Zhou, Shengdong & Bai, Zhang & Li, Qi & Yuan, Yu & Wang, Shuoshuo, 2024. "Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Optimized recuperation regulation with syngas storage," Applied Energy, Elsevier, vol. 353(PB).
    2. Zhang, Guangming & Zhang, Chao & Wang, Wei & Cao, Huan & Chen, Zhenyu & Niu, Yuguang, 2023. "Offline reinforcement learning control for electricity and heat coordination in a supercritical CHP unit," Energy, Elsevier, vol. 266(C).
    3. Li, Gen & Du, Guanghan & Liu, Guixiu & Yan, Junjie, 2024. "Study on the dynamic characteristics, control strategies and load variation rates of the concentrated solar power plant," Applied Energy, Elsevier, vol. 357(C).
    4. Ju, Liwei & Lv, ShuoShuo & Zhang, Zheyu & Li, Gen & Gan, Wei & Fang, Jiangpeng, 2024. "Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism," Applied Energy, Elsevier, vol. 362(C).
    5. Chunlai Yang & Xiaoguang Hao & Qijun Zhang & Heng Chen & Zhe Yin & Fei Jin, 2023. "Performance Analysis of a 300 MW Coal-Fired Power Unit during the Transient Processes for Peak Shaving," Energies, MDPI, vol. 16(9), pages 1-17, April.
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