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

Xiaoguang Hao (), Chunlai Yang, Heng Chen, Jianning Dong, Jiandong Bao, Hui Wang and Wenbin Zhang
Additional contact information
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

Energies, 2024, vol. 17, issue 11, 1-20

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.

Keywords: automatic power generation control; deep learning; LSTM neural networks; numerical modeling; load command optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2668/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2668/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:11:p:2668-:d:1405810

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().