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Optimal Load Distribution of CHP Based on Combined Deep Learning and Genetic Algorithm

Author

Listed:
  • Anping Wan

    (Department of Mechanical Engineering, Zhejiang University City College, Hangzhou 310015, China)

  • Qing Chang

    (Department of Mechanical Engineering, Zhejiang University City College, Hangzhou 310015, China
    College of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Yinlong Zhang

    (Huadian Electric Power Research Institute, Hangzhou 310030, China)

  • Chao Wei

    (Huadian Electric Power Research Institute, Hangzhou 310030, China)

  • Reuben Seyram Komla Agbozo

    (State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xiaoliang Zhao

    (School of Software Technology, Zhejiang University, Ningbo 315000, China)

Abstract

In an effort to address the load adjustment time in the thermal and electrical load distribution of thermal power plant units, we propose an optimal load distribution method based on load prediction among multiple units in thermal power plants. The proposed method utilizes optimization by attention to fine-tune a deep convolutional long-short-term memory network (CNN-LSTM-A) model for accurately predicting the heat supply load of two 30 MW extraction back pressure units. First, the inherent relationship between the heat supply load and thermal power plant unit parameters is qualitatively analyzed, and the influencing factors of the power load are screened based on a data-driven analysis. Then, a mathematical model for load distribution optimization is established by analyzing and fitting the unit’s energy consumption characteristic curves on the boiler and turbine sides. Subsequently, by using a randomly chosen operating point as an example, a genetic algorithm is used to optimize the distribution of thermal and electrical loads among the units. The results showed that the combined deep learning model has a high prediction accuracy, with a mean absolute percentage error (MAPE) of less than 1.3%. By predicting heat supply load variations, the preparedness for load adjustments is done in advance. At the same time, this helps reduce the real-time load adjustment response time while enhancing the unit load’s overall competitiveness. After that, the genetic algorithm optimizes the load distribution, and the overall steam consumption rate from power generation on the turbine side is reduced by 0.488 t/MWh. Consequently, the coal consumption rate of steam generation on the boiler side decreases by 0.197 kg (coal)/t (steam). These described changes can greatly increase the power plant’s revenue by CNY 6.2673 million per year. The thermal power plant used in this case study is in Zhejiang Province, China.

Suggested Citation

  • Anping Wan & Qing Chang & Yinlong Zhang & Chao Wei & Reuben Seyram Komla Agbozo & Xiaoliang Zhao, 2022. "Optimal Load Distribution of CHP Based on Combined Deep Learning and Genetic Algorithm," Energies, MDPI, vol. 15(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7736-:d:947351
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    References listed on IDEAS

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    1. Mingfei Hu & Xinyi Hu & Zhenzhou Deng & Bing Tu, 2022. "Fault Diagnosis of Tennessee Eastman Process with XGB-AVSSA-KELM Algorithm," Energies, MDPI, vol. 15(9), pages 1-25, April.
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    Cited by:

    1. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).

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